Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/RAG_Documents/Autism_Books.txt filter=lfs diff=lfs merge=lfs -text
+tests/Computational[[:space:]]Requirements[[:space:]]for[[:space:]]Embed.docx filter=lfs diff=lfs merge=lfs -text
+tests/Computational[[:space:]]Requirements[[:space:]]for[[:space:]]Embed.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,208 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+/logs
+
+# Distribution / packaging
+.Python
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+develop-eggs/
+dist/
+downloads/
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+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
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+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+
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+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+#poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+#pdm.lock
+#pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+#pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
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+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
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+.cursorignore
+.cursorindexingignore
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/

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.vscode/settings.json

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+{
+    "kiroAgent.configureMCP": "Disabled"
+}

README.md

@@ -1,12 +1,107 @@
 ---
-title: Autism QA
-emoji: 🐠
-colorFrom: blue
-colorTo: red
+title: Autism_QA
+app_file: multi_page_gradio_demo.py
 sdk: gradio
-sdk_version: 5.45.0
-app_file: app.py
-pinned: false
+sdk_version: 5.40.0
 ---
+# Wisal: Autism AI Assistant
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Wisal is an AI-powered assistant specifically designed to help individuals with autism with their communication needs. The application provides both text and audio responses to make communication more accessible.
+
+## Features
+
+- Text and voice input/output
+- Document Q&A capabilities
+- Live audio chat
+- Customizable voice responses
+- User-specific document handling
+
+## Prerequisites
+
+Before you begin, ensure you have the following installed:
+- Python 3.8 or higher
+- pip (Python package installer)
+- Conda (optional, but recommended for environment management)
+
+## Setup Instructions
+
+### 1. Clone the Repository
+
+```bash
+git clone <repository-url>
+cd autim-qa-mine
+```
+
+### 2. Environment Configuration
+
+Copy the example environment file and fill in your API keys:
+
+```bash
+cp .env.example .env
+```
+
+Then edit the `.env` file and add your API keys:
+- Google Gemini API Key
+- SiliconFlow API Key
+- Other optional API keys as needed
+
+### 3. Running the Application
+
+We provide a convenient script to run the application with different environment options:
+
+```bash
+./run.sh
+```
+
+The script will present you with three options:
+1. Use an existing conda environment
+2. Create a new conda environment
+3. Install requirements with the current Python environment
+
+### Manual Installation (Alternative)
+
+If you prefer to set up the environment manually:
+
+1. **Create a virtual environment (recommended):**
+   ```bash
+   python -m venv wisal-env
+   source wisal-env/bin/activate  # On Windows: wisal-env\Scripts\activate
+   ```
+
+2. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Run the application:**
+   ```bash
+   python main.py
+   ```
+
+## Usage
+
+Once the application is running, it will be accessible at `http://localhost:8080` in your web browser.
+
+### Basic Usage
+1. Type your question in the text box or record audio using the microphone
+2. Select your preferred voice for the response
+3. Click "Send Message" or press Enter
+4. The AI will respond with both text and audio
+
+### Advanced Features
+- **Document Q&A**: Upload documents (PDF, DOCX, TXT) to ask questions about their content
+- **Live Chat**: Use the WebRTC feature for real-time audio conversation
+- **User-Specific Documents**: Store and query personal documents
+
+## Configuration
+
+The application can be configured through the `config.yaml` file for model settings and other parameters.
+
+## Troubleshooting
+
+If you encounter issues:
+1. Ensure all API keys in `.env` are correct and active
+2. Check that all dependencies are installed: `pip install -r requirements.txt`
+3. Verify the application is using the correct Python environment
+
+For further assistance, please check the logs in the `logs/` directory.

WARP.md

@@ -0,0 +1,282 @@
+# WARP.md
+
+This file provides guidance to WARP (warp.dev) when working with code in this repository.
+
+## Project Overview
+
+**Wisal QA** is an advanced AI assistant specialized in Autism Spectrum Disorders (ASD), developed by Compumacy AI. The system uses sophisticated confidence scoring and relevance filtering to provide accurate, autism-focused responses while recognizing comorbid conditions and indirect relationships.
+
+### Key Features
+
+- Multi-tiered relevance assessment (0-100% confidence scoring)
+- Comorbidity recognition (depression, anxiety, ADHD, sleep issues in autism context)
+- Smart query rewriting with autism-specific context
+- Multi-source information combining (web search, RAG, LLM generation)
+- Real-time voice chat with transcription and TTS
+- Document upload and Q&A functionality
+
+## Development Commands
+
+### Environment Setup
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Set up environment variables (copy from .env.example if available)
+cp .env.example .env
+# Edit .env with your API keys:
+# - SILICONFLOW_API_KEY
+# - GEMINI_API_KEY  
+# - WEAVIATE_API_KEY
+# - QDRANT_API_KEY
+# - TAVILY_API_KEY
+```
+
+### Running the Application
+
+```bash
+# Main application (Gradio interface)
+python main.py
+
+# Alternative runner with pre-flight checks
+python run_main.py
+
+# Test functional components
+python test.py
+```
+
+### Development and Testing
+
+```bash
+# Run single test file
+python -m pytest test.py -v
+
+# Test specific functionality
+python -c "from utils import process_query; print(process_query('What is autism?'))"
+
+# Test query processing pipeline
+python -c "from query_utils import process_query_for_rewrite; print(process_query_for_rewrite('My child has depression'))"
+
+# Functional RAG pipeline testing
+python -c "from src.pipeline import create_rag_pipeline; pipeline = create_rag_pipeline(); print(pipeline('What is autism?', ['Autism is a developmental disorder...']))"
+```
+
+### Configuration Management
+
+```bash
+# View current configuration
+cat config.yaml
+cat src/config.yaml
+
+# Test configuration loading
+python -c "from src.config import load_config; print(load_config())"
+```
+
+### Logging and Debugging
+
+```bash
+# View recent logs
+ls -la logs/
+tail -f logs/log_*.txt
+
+# Check logger functionality
+python -c "from logger.custom_logger import CustomLoggerTracker; logger = CustomLoggerTracker().get_logger('test'); logger.info('Test message')"
+```
+
+## Architecture Overview
+
+### High-Level System Design
+
+The codebase follows a **multi-paradigm architecture** combining:
+
+1. **Functional Programming** (src/ directory) - Pure functions, immutable data structures
+2. **Traditional OOP** (root directory) - Gradio interface, handlers, utilities
+
+### Core Components
+
+#### 1. Confidence Scoring Pipeline (`query_utils.py`)
+
+- **Enhanced relevance checking**: 6-tier confidence system (0-100%)
+- **Smart rewriting**: Automatically frames questions in autism context
+- **Comorbidity awareness**: Recognizes depression, anxiety, ADHD as autism-relevant
+
+**Key Functions:**
+
+- `enhanced_autism_relevance_check()` - Main confidence scoring
+- `process_query_for_rewrite()` - Complete query processing pipeline
+- `rewrite_query_for_autism()` - Context-aware query rewriting
+
+#### 2. Multi-Source Processing Pipeline
+
+The system combines three information sources:
+
+- **Web Search** (`web_search.py`) - Real-time autism information via Tavily API
+- **RAG Systems** (`rag.py`, `rag_domain_know_doc.py`) - Domain knowledge retrieval
+- **LLM Generation** (`utils.py`) - Direct autism expertise via SiliconFlow/Qwen
+
+#### 3. Functional RAG Architecture (`src/` directory)
+
+Modern functional programming approach with:
+
+- **Immutable data structures** (`@dataclass(frozen=True)`)
+- **Pure functions** with consistent interfaces
+- **Composable pipeline** (`src/pipeline.py`)
+- **Model factories** (`src/models.py`) for API/local model abstraction
+
+#### 4. Quality Control Layer
+
+Multi-stage validation:
+
+- **Pre-processing**: Query relevance filtering
+- **Post-processing**: Answer autism-relevance checking  
+- **Hallucination detection**: 5-point accuracy scoring
+- **Translation support**: Auto-detect and translate responses
+
+### Configuration Architecture
+
+**Dual Configuration System:**
+
+- `config.yaml` (root) - Application-level settings, API keys
+- `src/config.yaml` - Functional pipeline configuration (models, chunking, performance)
+
+**Model Support:**
+
+- **API Models**: SiliconFlow, Gemini, Weaviate, Qdrant
+- **Local Models**: Hugging Face transformers, sentence-transformers
+- **Configurable switching** between API and local inference
+
+### Key Architectural Patterns
+
+#### 1. Confidence-Driven Processing
+
+```python
+# Query processing follows confidence scoring
+if confidence_score >= 70:
+    process_directly()
+elif confidence_score >= 25:
+    rewrite_for_autism_context()
+else:
+    politely_reject()
+```
+
+#### 2. Functional Composition
+
+```python
+# Pipeline composition in src/
+pipeline = compose(
+    chunk_documents,
+    embed_chunks, 
+    retrieve_similar_chunks,
+    rerank_documents,
+    generate_answer
+)
+```
+
+#### 3. Multi-Modal Integration
+
+- **Text Input/Output**: Primary interface
+- **Voice Input**: Gemini transcription via WebRTC
+- **Voice Output**: Gemini TTS with multiple voice options
+- **Document Processing**: PDF, DOCX, TXT support
+
+## Important Implementation Details
+
+### Confidence Scoring Thresholds
+
+```python
+DIRECT_AUTISM_THRESHOLD = 85      # Accept as-is
+HIGH_RELEVANCE_THRESHOLD = 70     # Accept as-is  
+SIGNIFICANT_THRESHOLD = 55        # Rewrite for autism
+MODERATE_THRESHOLD = 40           # Rewrite for autism
+SOMEWHAT_THRESHOLD = 25           # Conditional rewrite
+REJECTION_THRESHOLD = 24          # Reject
+```
+
+### Comorbidity Recognition Logic
+
+The system specifically boosts scores for:
+
+- **Depression in children/teens**: +15 points (65-75% final score)
+- **Anxiety disorders**: +15 points
+- **ADHD symptoms**: +15 points
+- **Sleep disorders**: +15 points
+- **Sensory processing**: +20 points
+
+### Model Configuration Patterns
+
+```python
+# API vs Local model switching
+if model_config.type == ModelType.API:
+    return create_api_model(config)
+else:
+    return create_local_model(config)
+```
+
+### Error Handling Strategy
+
+- **Graceful degradation**: Fallback to simpler models/methods
+- **Comprehensive logging**: All failures logged with context
+- **User-friendly messages**: Technical errors translated to helpful responses
+
+## Development Guidelines
+
+### Working with Confidence Scoring
+
+- **Test edge cases**: Borderline queries (scores 25-75)
+- **Validate comorbidity detection**: Depression/anxiety in autism context
+- **Monitor false positives/negatives**: Use logging to track decision quality
+
+### Adding New Features
+
+1. **Functional approach**: Add to `src/` directory for pipeline components
+2. **Integration**: Use existing confidence scoring for relevance checking
+3. **Logging**: Integrate with `CustomLoggerTracker` for consistency
+4. **Configuration**: Add settings to appropriate config.yaml
+
+### Model Integration
+
+- **API models**: Add to model factory in `src/models.py`
+- **Local models**: Ensure HuggingFace compatibility
+- **Configuration**: Update model configs in `src/config.yaml`
+
+### Testing Autism Relevance
+
+```python
+# Test confidence scoring
+from query_utils import enhanced_autism_relevance_check
+result = enhanced_autism_relevance_check("My teenager seems depressed")
+# Expected: score=65, action="rewrite_for_autism"
+```
+
+### Audio/Voice Features
+
+- **WebRTC integration**: Real-time voice chat via `fastrtc`
+- **Gemini STT/TTS**: Voice input/output processing
+- **VAD (Voice Activity Detection)**: Automatic speech detection
+
+## Common Development Patterns
+
+### Adding a New Information Source
+
+1. Create async function in dedicated module
+2. Integrate with reranking system in `utils.py`
+3. Add to multi-source processing pipeline
+4. Update confidence thresholds if needed
+
+### Extending Comorbidity Recognition  
+
+1. Update confidence scoring prompts in `prompt_template.py`
+2. Add condition-specific scoring logic in `query_utils.py`
+3. Test with representative queries
+4. Update documentation with new thresholds
+
+### Document Processing Workflow
+
+1. Upload via Gradio interface
+2. Route to appropriate handler (`old_docs.py`, `rag_domain_know_doc.py`, `user_specific_documents.py`)
+3. Chunk and embed using functional pipeline
+4. Store in vector database (Weaviate/Qdrant)
+5. Integrate with RAG retrieval
+
+This codebase represents a sophisticated autism-focused AI system with strong architectural foundations for both traditional and functional programming paradigms. The confidence scoring system and comorbidity awareness are key differentiators that should be preserved and extended carefully.

__init__.py

@@ -0,0 +1,2 @@
+from .configs import load_yaml_config
+from clients import init_weaviate_client

assets/Books Chunks/Autism Spectrum Disorders in Infants and Toddlers_ Diagnosis, Assessment, and Treatment/Autism Spectrum Disorders in Infants and Toddlers_ Diagnosis, Assessment, and Treatment Part_1.txt

@@ -0,0 +1,156 @@
+Autism Spectrum Disorders
+in Infants and Toddlers
+An Introduction
+FRED R. VOLKMAR
+KATARZYNA CHAWARSKA
+AMI KLIN
+In his original report on the syndrome of early infantile autism, Leo Kanner (1943/1968) indicated that autism was a congenital disorder. Although a minority of children seem to develop autism after some months of normal development, most of the subsequent work on autism has generally supported his contention (see Volkmar, Chawarska, & Klin, 2005, for a review). This observation would also be highly consistent with the large body of work supporting a genetic basis for the condition (Rutter, 2005). Somewhat paradoxically, however, our knowledge of autism as it is expressed in the first year of life is quite limited. Fortunately, within the last decade this situation has begun to change. A little more than a decade ago various factors acted to delay case detection and early diagnosis (Siegel, Pliner, Eschler, & Elliott, 1988), but now various programs specifically focused on early diagnosis of infants at risk for autism have been developed. Growing public awareness of the condition and an increasingly large body of work on the importance of early intervention and stability of early diagnosis (National Research Council, 2001) have increased interest in the early stages of autism. A growing body of research work focused on this age group has begun to appear. In previous years most of this work was based on either parent report (Chawarska, Paul, et al., 2007; Cohen, Volkmar, & Paul, 1986) or review of videotapes or movies (e.g., Osterling & Dawson, 1994; Werner, Dawson, Osterling, & Dinno, 2000), with all the attendant problems associated with the lack of contemporaneous methods. The first prospective longitudinal studies of young children (Lord, 1995; Lord et al., 2006) and the recognition of the importance of early intervention has stimulated the National Institute of Mental Health to set the ambitious goal of reducing the number of children with autism through early diagnosis and intervention. In this chapter we are concerned with issues of the clinical expression of autism in infants. Although our major focus is on infancy and early childhood, some of the work on preschool children is highly relevant and is touched upon as well. We attempt to highlight areas critical for future research on this important topic.
+
+AUTISM AS A DIAGNOSTIC CONCEPT
+Kanner’s Original Report
+Kanner’s (1943/1968) original report contrasted the lack of social interest (autism) with the normative marked predisposition to engage with others in reciprocal interactions; he carefully framed his observation developmentally by citing the work of Gesell on the early emergence of social interest in the first weeks of life. We now are aware that this interest is present from birth in the typically developing infant. Since Kanner’s first description, the diagnostic concept has undergone modification based on research and clinical work. At the same time, the diagnostic conceptualization retains important historical and conceptual continuities with Kanner’s first description. Kanner emphasized the centrality of the social difficulties, as well as the presence of a set of unusual behaviors he subsumed under the term “insistence of sameness” or “resistance to change.” These unusual behaviors included unusual movements and mannerisms as well as problems in dealing with change and novelty. Of the first 11 patients described in his report, only one was below age 3 years when Kanner first examined him and three children were between the ages of 3 and 4 years. Although Kanner emphasized the uniqueness of the condition and its apparent difference from schizophrenia, other clinicians tended to assume some form of continuity of the two conditions. This issue was clarified over the next several decades as longitudinal and other data made it clear that autism formed a distinct diagnostic category. As a result, however, Kanner’s early focus on “early infantile autism” was lost and most research focused on school-age or adolescent children.
+
+DSM-I and DSM-II:
+Confusion with Childhood Schizophrenia
+In the first two editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) only the term childhood schizophrenia was officially available to describe autism. This situation was very unfortunate. Subsequently, the work of Kolvin (1971) and Rutter (1972) made it clear that autism was distinctive and could not simply be considered an early form of schizophrenia (Volkmar & Klin, 2005). Furthermore, available research suggested that autism was a brain-based disorder and not a result of deviant parent–child interaction. In parallel with attempts to provide better definitions of adult psychiatric disorders for research (Spitzer, Endicott, & Robins, 1978), similar attempts were made of childhood-onset disorders like autism. Among the investigators of this time, Rutter (1978) provided an important and influential synthesis of Kanner’s original report with subsequent research. Rutter suggested the importance of four essential features: (1) early onset, (2) distinctively impaired social development, (3) distinctively impaired communication, and (4) unusual behaviors of the type suggested in Kanner’s concept of “insistence on sameness” (resistance to change, idiosyncratic responses to the environment, motor mannerisms and stereotypies, etc.). Rutter was clear that the social and communication difficulties were not just a function of associated intellectual disability. These various issues were considered as autism was first included in the landmark, third edition of DSM (DSM-III; American Psychiatric Association, 1980).
+
+DSM-III and DSM-III-R
+DSM-III (American Psychiatric Association, 1980) represented a marked change from its two predecessors. The taxonomy proposed was based on research findings and emphasized the importance of an atheoretical and empirically based set of criteria. Autism was included in a newly designated class of childhood-onset disorders, Pervasive Developmental Disorders (PDD). A “subthreshold” condition was included as well, atypical PDD; this term had considerable (if unintended) overlap with earlier terms such as atypical personality development (Volkmar & Klin, 2005). The definition included in DSM-III was heavily dependent on Rutter’s earlier conceptualization and provided for a clear differentiation of autism from schizophrenia. Interestingly, the original DSM-III approach lacked a developmental orientation and, if anything, the criteria proposed were much more appropriate to very young children with autism, that is, consistent with the term infantile autism. Although the use of a multiaxial approach was a clear benefit for child psychiatry, some aspects of the organization of this system were confusing—for example, autism and related disorders were placed on a different axis than other developmental disorders. A much more developmental orientation was introduced in DSM-III-R (American Psychiatric Association, 1987), which was greatly influenced by the work of Lorna Wing (Wing & Gould, 1979). Although the now familiar three major areas of dysfunction were still included, the new criteria were much more detailed and included a range of examples (with the goal of producing an approach applicable to the broad range of age and developmental levels). The use of a polythetic approach was also adopted, and the requirement for early onset was dropped (although onset before or after age 3 could still be specified). The official name of the condition was changed from infantile autism to Autistic Disorder in reflection of these changes. Although many aspects of the DSM-III-R approach were improvements, it quickly became apparent that the system tended to “overdiagnose” autism, particularly in the cases of more intellectually challenged children (Rutter & Schopler, 1992). This observation led to the potential for major difficulties in the comparison of studies using different diagnostic criteria and also posed problems for pending revision in the International Classification of Diseases—tenth edition (ICD-10; World Health Organization, 1990). The ICD and DSM approaches are fundamentally related and share many aspects of diagnostic coding, although there are also important differences.
+
+ICD-10 and DSM-IV
+Extensive revision of both the ICD and DSM systems was undertaken early in 1994. As part of the DSM-IV revision process (American Psychiatric Association, 1994), attempts were made to identify areas of both consensus and controversy such as clinical utility, reliability, and descriptive validity of categories and criteria. Coordination with the pending ICD revision was also a consideration. Literature reviews and data reanalyses were also undertaken for specific issues, such as those relative to the concept of Childhood Disintegrative Disorder—a concept included in previous versions of ICD but not DSM. Data reanalyses suggested that the DSM-III-R approach was overbroad, and a decision was made to undertake a large multinational field trial (Volkmar et al., 1994). This field trial was conducted in coordination with the ICD-10 revision effort and included more than 100 raters working at more than 20 sites around the world. The final sample included information on nearly 1,000 cases seen by one (or sometimes more than one) rater. In the nearly 1,000 cases, more than 300 children were less than 5 years of age (although most were between ages 3 and 5 and no child younger than 2 was seen). A standard coding system was used to provide basic information on a case and rater and on a number of diagnostic criteria. The overall results of the field trial (see Table 1.1) confirmed that DSM-III-R had a higher sensitivity but lower specificity, whereas the ICD-10 draft definition, designed to be a research diagnostic system, had, as expected, higher specificity. A series of analyses were undertaken, including reliability of criteria and diagnosis, factor analyses, signal detection analysis, and so forth (Volkmar et al., 1994, Klin, Lang, Cicchetti, & Volkmar, 2000). As expected, social criteria were, as individual diagnostic items, generally the most potent single diagnostic predictors, and a decision was made to weight them more heavily in the final DSM-IV definition. Possible modifications in the ICD-10 system were examined, the goal being to have convergent definitions in the DSM and ICD. The final diagnostic approach provided reasonable coverage over the range of syndrome expression in autism as reflected in the field trial sample and was applicable from early childhood (i.e., at about age 3) through adulthood. It must be emphasized that the DSM-IV and ICD-10 approach did consider developmental aspects of syndrome change, but, not surprisingly at that time, the focus was not on infants and very young children; that is, it appeared that the approach derived worked satisfactorily starting at about age 3. Interestingly, examination of some of the DSM-IV field trial data (children under age 5) reveals a few items with stronger developmental correlates. In general, such items were discarded because they would not be applicable to the entire range of syndrome expression. For example, attachment to unusual objects has low sensitivity (.50) but high specificity (.90), so that when it is observed, it has high predictive power for autism but only in this younger age group. Interest in the earliest development of children with problems included in the autism spectrum was also fueled by inclusion of additional disorders within the revised PDD section of DSM-IV (e.g., Asperger’s Disorder, Rett’s Disorder, and Childhood Disintegrative Disorder). A need to differentiate these disorders highlighted the importance of understanding developmental history and early clinical presentations. At the time that DSM-IV appeared (1994), there was little concern with the manifestation of autism in infants and very young children. For children by about age 3, the DSM system appeared to generally work well with reasonable stability of diagnosis (Lord & Risi, 2000). However, with the growing interest in genetic mechanisms, screening of at-risk populations such as siblings, and the marked increase in research in the earliest manifestations of autism there has been progressively more concern about autism as it is manifested in infancy. We consider these issues before returning to the problem of early diagnosis.
+
+|          | n   | DSM-III a       |        | DSM-III-R       |        | ICD-10 b        |        |
+|----------|-----|-----------------|--------|-----------------|--------|-----------------|--------|
+|          |     | Se              | Sp     | Se              | Sp     | Se              | Sp     |
+| Overall  | 940 | .82             | .80    | .86             | .83    | .79             | .89    |
+| By IQ level|     |                 |        |                 |        |                 |        |
+| < 25     | 64  | .90             | .76    | .84             | .39    | .74             | .88    |
+| 25–39    | 148 | .88             | .76    | .90             | .60    | .88             | .92    |
+| 40–54    | 191 | .79             | .76    | .93             | .74    | .84             | .83    |
+| 55–69    | 167 | .86             | .78    | .84             | .77    | .78             | .89    |
+| 70–85    | 152 | .79             | .81    | .88             | .81    | .74             | .96    |
+| > 85     | 218 | .78             | .83    | .78             | .78    | .78             | .91    |
+Note. Se, sensitivity; Sp, specificity.
+a“Lifetime” diagnosis (current infantile autism or “residual” infantile autism).
+bOriginal ICD-10 criteria and scoring table adapted from Volkmar et al. (1994). Copyright 1994 by the American Psychiatric Association. Adapted by permission.
+
+CLINICAL PHENOMENOLOGY
+Onset of the Condition
+As noted, Kanner (1943/1968) emphasized the apparently congenital nature of autism in his original report. Direct evidence regarding the actual onset of the symptoms is still lacking, and a vast majority of the current reports rely on parental recollection regarding the age of onset and type of first abnormalities. Although these reports have their obvious limitations and the onset of parental concerns is likely to follow the actual time when the symptoms of autism spectrum disorder (ASD) (equivalent to the term PDD) begin to manifest, they also offer some insight into the nature of the first concerns that are likely to motivate parents to seek professional advice, which in turn may lead to an earlier initiation of treatment. Raising parental awareness of the first signs of various developmental disorders, including ASD, has become one of the priorities of a number of parent organizations, such as Autism Speaks (www.autismspeaks.org) and the Centers for Disease Control and Prevention (www.cdc.gov/ncbddd/autism/actearly/), as one of the factors that are likely to contribute to early identification and treatment of infants with developmental disabilities. A number of studies have suggested that the vast majority of parents of children with ASD first notice abnormalities during the course of the first 2 years of life (Baghdadli, Picot, Pascal, Pry, & Aussilloux, 2003; Chawarska, Paul, et al., 2007; De Giacomo & Fombonne, 1998; Rogers & DiLalla, 1990; Tolbert, Brown, Fowler, & Parsons, 2001; Volkmar, Stier, & Cohen, 1985). The first concerns arise, on average, in the second year, usually at about 14 months (Chawarska, Paul, et al., 2007), 17 months (Baghdadli et al., 2003), or 19 months (De Giacomo & Fombonne, 1998). These ages are likely to be sensitive to several factors, such as the time elapsing between the onset of parental concerns and the time when the information was collected. With a shorter lag, reports of earlier ages of onset are to be expected; otherwise a “forward-telescoping” effect seems to apply (Cooper, Kim, Taylor, & Lord, 2001), that is, a shift of the estimate regarding the age when the child began manifesting first symptoms to later ages. The time when parents begin to notice the first abnormities varies, such that 30–50% of parents report concerns in the first year of the child’s life and 80–90% by the second birthday (Baghdadli et al., 2003; Chawarska, Paul, et al., 2007; De Giacomo & Fombonne, 1998; Volkmar et al., 1985). There are relatively few studies reporting on the association between clinical outcome and the onset of parental concerns. Most of the studies were conducted retrospectively and produced very mixed results. A recent study examined prospectively the link between the onset of parental concerns, measured when the toddlers were between 18 and 36 months, and clinical diagnosis at the age of 4 (Chawarska, Paul, et al., 2007). Children who were identified by their parents as having problems between birth and 10 months were four times more likely to be later diagnosed with autism than with Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS). However, those identified by parents as having difficulties between 11 and 18 months were equally likely to receive a diagnosis of autism or PDD-NOS at 4 years. Finally, all children in the group with concerns arising at or after 18 months received a diagnosis of autism at the age of 4. This finding suggests a strong and nonlinear relationship between the age of parental recognition (and presumably the onset of symptoms) and clinical diagnosis assigned 2–3 years later and raises a question of possible variants that manifest differently in the onset of symptoms.
+
+Among the most common and often first noted concerns are delays in speech and language development, followed by an abnormal social responsivity level, medical problems, and nonspecific difficulties related to sleeping, eating, and attention (Chawarska, Paul, et al., 2007; De Giacomo & Fombonne 1998). Notably, in young children, the appearance of stereotyped behaviors, motor mannerisms, and unusual interests rarely trigger parental concerns, most likely because of their relatively mild manifestations in infancy or a later onset. Although concerns regarding the development of speech and the level of social engagement are frequent for toddlers with autism and PDD-NOS, the nonspecific concerns related to feeding, eating, and sleep appear to be more frequent for toddlers with PDD-NOS (Chawarska, Paul, et al., 2007). Although the presence of specific delays constitutes a strong basis for parental concerns, such concerns may also emerge in response to unusual variations in the rate of progress, such as an apparent slowing of development (e.g., if babbling is not followed by the emergence of the first words) or a loss of previously acquired skills (regression) (Siperstein & Volkmar, 2004). Regression is usually reported in 20–35% of cases (Chawarska, Paul, et al., 2007; Goldberg et al., 2003; Luyster et al., 2005; Rapin & Katzman, 1998; Rogers, 2004; Werner & Dawson, 2005) and can involve the loss of words, vocalizations, nonverbal communication skills (e.g., eye contact, gestures), social dyadic interaction skills, imitation, or pretend play (Davidovitch, Glick, Holtzman, Tirosh, & Safir, 2000; Goldberg et al., 2003; Luyster et al., 2005). The perception of regression appears to be specific, though clearly not universal, to ASD (Luyster et al., 2005; Siperstein & Volkmar, 2004). Parental reports of regression do not necessarily indicate normal development prior to the perceived loss of skills, nor do early abnormalities preclude regression (Lord, Shulman, & DiLavore, 2004; Siperstein & Volkmar, 2004; Werner & Dawson, 2005; Wilson, Djukie, Shinnar, Dharmani, & Rapin, 2003). In fact, unequivocal loss of skills following normal developmental milestones is relatively uncommon (Siperstein & Volkmar, 2004; see Figure 1.1). An analysis of developmental history in a large sample of children with autism suggested that in most instances of reported loss, the development seemed to reach a plateau and then stagnate rather than undergo a true loss of skills. In other instances, parents reported a loss in a child who was already experiencing developmental delays (Siperstein & Volkmar, 2004). However, it is clear that in some cases a marked regression does occur—such regression has been documented in very young children with ASD through analysis of video recordings in the first year of life (Werner & Dawson, 2005). Werner and Dawson (2005) used home videotapes of the first and second birthday parties of children with ASD and of typically developing controls. Raters blind to diagnosis and history of regression confirmed regression, as defined by a decline in frequency of joint attention acts and word/babble use in a subset of the ASD sample.
+FIGURE 1.1. Loss of developmental skills.
+
+It is clear that skill loss after a prolonged period of normal development (e.g., to 3 or 4 years) is relatively uncommon. A specific diagnostic term, Childhood Disintegrative Disorder, exists for this category of cases, and the outcome appears to be worse than that in autism, with little or no recovery of previously exhibited abilities (Volkmar, Koenig, & State, 2005). Given the complexities of understanding the role of regression in autism, it remains unclear as to what relationship exists between this less common later-onset condition and reported early regression in autism. Among the factors that precipitate the onset of parental concerns are concurrent cognitive delays, delays in motor development, and the presence of medical problems (De Giacomo & Fombonne, 1998). The presence of perinatal complications and sensory deficits has also been associated with earlier recognition (Baghdadli et al., 2003). A more recent study suggests that in the first year, late onset of social smile, delays in responsivity to speech and language understanding, and late onset of independent walking are possible factors precipitating parental concerns (Chawarska, Paul, et al., 2007). Factors that have not been found to influence the age of recognition include birth order, social class, and gender (De Giacomo & Fombonne, 1998). More recently, the growing appreciation of the genetic factors in autism and increased risk for ASD in younger siblings of the affected children may sensitize parents to early signs of vulnerability and contribute to earlier recognition of developmental problems (Klin et al., 2004; Zwaigenbaum et al., 2007).
+
+Clinical Presentation in the First Year of Life
+Kanner’s original report emphasized the central role of social difficulties in autism. It is a tribute to his powers of observation that most subsequent research has supported this observation, albeit with considerable refinement (Carter, Davis, Klin, & Volkmar, 2005; see also Chawarska & Volkmar, 2005, for a review). Although early reports on symptoms of autism in the first year of life relied heavily on parental report (e.g., Dahlgren & Gillberg, 1989; Klin, Volkmar, & Sparrow, 1992) and single case studies (Dawson, Osterling, Meltzoff, & Kuhl, 2000), these reports were later supplemented by analytic studies of home video recordings depicting, for instance, a first birthday party or other family events (e.g., Baranek, 1999; Maestro et al., 2001; Osterling, Dawson, & Munson, 2002; Werner et al., 2000). Studies based on these approaches have contributed greatly to raising awareness, regardless of possible early symptoms of ASD. However, they suffer a number of important methodological limitations related, for instance, to parental ability to detect and report on the more subtle and contextualized symptoms of ASD (Chawarska, Klin, Paul, & Volkmar, 2007; Stone, Hoffman, Lewis, & Ousley, 1994) as well as to the sensitivity and specificity of the identified deficits to ASD owing to issues with control groups or the representativeness of the source material (i.e., videotapes) (see also Zwaigenbaum et al., 2007, for a review).
+
+More recently, however, the findings of increased genetic liability for ASD in younger children enabled researchers to study ASD in statu nascendi by following prospectively large cohorts of younger siblings at risk for developing the disorders (Zwiegenbaum et al., 2007). The sibling recurrence rate of autism has been estimated between 3 and 8% (Bailey et al., 1995; Bailey, Phillips, & Rutter, 1996; Ritvo et al., 1989). These numbers may underestimate the true recurrence rate for several reasons, including (1) increased prevalence rates related to the employment of more inclusive diagnostic criteria for autism and PDD-NOS since the advent of DSM-IV, and (2) the stoppage phenomenon exemplified by a high number of families avoiding further pregnancies once an offspring is diagnosed with autism (Jones & Szatmari, 1988; Slager, Faroud, Haghighi, Spence, & Hodge, 2001). Increased rates for nonautistic PDD in siblings (Asperger syndrome, PDD-NOS) have also been reported (Bailey et al., 1995; Le Couteur et al., 1996). Features of a broader autism phenotype (BAP) have been reported in 15–45% of family members (Bailey, Palferman, Heavey, & Le Couteur, 1998; Folstein et al., 1999), with higher rates of both narrow and broad autistic phenotype in male rather than female relatives of individuals with autism (Bolton et al., 1994; Pickles et al., 1995; Piven, Palmer, Jacobi, Childress, & Arndt, 1997). Preliminary findings from ongoing studies on high-risk siblings suggest that 20–25% of younger siblings of children with autism may exhibit developmental impairments in the first or second year of life (Zwaigenbaum et al., 2005), though studies examining the developmental trajectories of younger siblings are clearly needed and are slowly emerging (Landa & Garrett-Mayer, 2006; Yirmiya et al., 2006).
+
+Analysis of videotapes suggests that as compared with typical controls, infants who were later diagnosed with ASD were less likely to look at and seek other people, and they were less likely to smile and vocalize at others in the first 6 months of life (Maestro et al., 2002). In the second half of the first year, infants later diagnosed with ASD might show difficulties in responding when their names were called and look at others less frequently, as compared with typically developing children or infants with developmental delays (Baranek, 1999; Osterling et al., 2002; Werner et al., 2000). However, as a recent prospective study of high-risk infants suggests, limited response to their names at 12 months, although quite specific to infants with ASD as well as high-risk siblings with developmental delays, is by no means universally present in all infants who are later diagnosed with the disorder (Nadig et al., 2007). Thus, failure to respond to his or her name may be an indicator that a 12-month-old child would benefit from further evaluation, but passing the “name-calling” test does not mean that the child is not at risk of developing ASD. Studies of the presence of unusual sensory behaviors and motor stereotypies in samples of children with ASD, as compared with children with developmental delays, yield mixed results. Although some suggest the presence of excessive mouthing and possibly aversion to social touch (Baranek, 1999; Loh et al., 2007; Osterling et al., 2002), others fail to detect similar effects. Furthermore, motor stereotypies have been reported in some samples (Loh et al., 2007; Osterling et al., 2002) but not in others (Baranek, 1999; Werner & Dawson, 2005).
+
+Presently the vast majority of prospective baby sibling studies report on the expression of the broader autism phenotype that can be detected in infant siblings who are not affected with a full-blown ASD, rather than on children who were actually diagnosed with ASD later on (e.g., Toth, Dawson, Meltzoff, Greenson, & Fein, 2007; Cassel et al., 2007; Merin, Young, Ozonoff, & Rogers, 2007; Gamliel, Yirmiya, & Sigman, 2007). This current trend is related to the fact that owing to a relatively low recurrence rate among siblings, very large longitudinal samples need to accumulate for certain research questions to be addressed. Nonetheless, the first experimental studies reporting on the presentation of infants with ASD in the first year of life are beginning to emerge. Prospective studies of infant siblings, followed from 6 to 24 or 36 months and identified as having some form of ASD, suggest that robust behavioral features of ASD that could be captured through standard assessment instruments such the Autism Observation Scale for Infants (AOSI; Bryson, Zwaigenbaum, McDermott, Rombough, & Brian, 2007) and the Mullen Scales of Early Learning (MSEL; Mullen, 1995) may not emerge until some time after 6 months and before 12 months, with further intensification of their expression occurring between 12 and 24 months (Bryson, Zwaigenbaum, Brian, et al., 2007; Landa & Garrett-Mayer, 2006; Zwaigenbaum et al., 2005). Zwaigenbaum and colleagues (2005) identified several features at 12 months that are likely to differentiate siblings with ASD from those without social disability. Among the features were poor eye contact, limited social interest and smiling, limited use of gestures, poor response to name, poor imitation, and delays in receptive and expressive language. These infants also exhibited temperamental abnormalities, including initial passivity in early development followed by the emergence of a tendency for extreme distress reactions by 12 months. Difficulties in disengagement of visual attention were also noted. Studies such as these constitute the first step toward establishing clear diagnostic criteria for ASD in the first year of life, although extensive studies are needed to establish both sensitivity to and specificity of the identified abnormalities. A complementary approach to identifying behavioral markers of ASD in infancy involves the employment of experimental designs targeting basic perceptual and cognitive processes involved in development of social interactions and communication. Among these are eye-tracking studies of perception of social and nonsocial stimuli (e.g., Chawarska & Shic, 2007; Klin & Jones, in press; Merin et al., 2007) and speech perception (Nadig et al., 2007). These studies are discussed in greater detail by Klin, Saulnier, Chawarska, and Volkmar (Chapter 6, this volume).
+
+Symptoms of ASD in the Second and Third Years of Life
+Several factors have contributed to a much larger body of data on autism as it manifests after the first birthday and before age 3. Recent advances in clinical research suggest that in 2- and 3-year-olds, symptoms of autism center on areas of social interaction and communication and are often accompanied by delays in multiple areas of functioning, including motor and nonverbal cognitive development (see Chawarska & Volkmar, 2005, for a review; see also Bishop, Luyster, Richler, & Lord, Chapter 2; Chawarska & Bearss, Chapter 3; and Paul, Chapter 4, this volume). In the social domain, the most frequently reported symptoms are diminished eye contact, limited interest in social games and turn-taking exchanges, low frequency of looking referentially at parents, and preference for being alone (Cox et al., 1999; Lord, 1995; Stone, Lee, et al., 1999). Vocal and motor imitation and symbolic play skills appear delayed as compared with the children’s overall developmental levels (Baron-Cohen, Cox, Baird, Sweettenham, & Nightingale, 1996; Cox et al., 1999). Young children with autism direct their visual attention more frequently toward objects than toward people (Dawson et al., 2004; Swettenham et al., 1998). A limited range of facial expressions and infrequent instances of sharing affect (e.g., by smiling and looking at others) have been reported as well (Cox et al., 1999; Lord, 1995; Stone, Lee, et al., 1999). In the area of communication, the most striking differences relate to early emerging social communicative exchanges through nonverbal (e.g., use of gestures or gaze to communicate interest or joint attention) and vocal or verbal means. The child’s responsivity to speech in general, and to his or her name in particular, continues to be limited (Baron-Cohen et al., 1996; Cox et al., 1999; DiLavore, Lord, & Rutter, 1995; Klin, 1991; Lord & Pickles, 1996; Paul, Chawarska, Klin, & Volkmar, 2007). Vocalizations may take on an abnormal quality (Sheinkopf, Mundy, Oller, & Steffens, 2000; Wetherby, Yonclas, & Bryan, 1989). Stereotypic and repetitive behaviors reach a clinical threshold in the second year in some children (Chawarska, Klin, et al., 2007), and in a vast majority of children by the age of 4 (e.g., Lord, 1995). Adaptive skills are usually delayed beyond what would be expected based on the developmental level (Klin et al., 1992; Stone, Ousley, Hepburn, Hogan, & Brown, 1999). The relatively mild expression of the unusual repetitive behaviors (stereotyped movements and mannerisms) and the general category of “resistance to change” behaviors in this age group is of some interest (e.g., Chawarska, Klin, et al., 2007; Loh et al., 2007; Lord, 1995). The absence of clear-cut behaviors in this general category is one of the more general conceptual problems in the application of categorical (DSM-IV or ICD-10) diagnostic criteria. In Lord’s longitudinal study the absence of such behaviors before age 3 was a frequent reason that a diagnosis of autism could not be made (Lord, 1995; Lord et al., 2006). Although clear precursors of such behaviors may potentially be used as alternatives for this age group, relatively few attempts have been made to identify such precursors (Loh et al., 2007) and to assess their specificity to ASD. However, difficulties in adapting to new situations, interest in visually repetitive phenomena (e.g., ceiling fans), and overattention to the nonsocial environment (focusing on alphabet letters on blocks or small details of play materials) are potential candidates. Furthermore, an increase in the second year, rather than the expected decrease, of some of the repetitive movements observed in the first year (Thelen, 1979) may be a sign of abnormal development in this area (Loh et al., 2007).
+
+IMPLICATIONS FOR DIAGNOSIS AND SCREENING
+Issues of diagnosis and screening are discussed in detail by Bishop et al. (Chapter 2, this volume) and are only briefly touched upon here. Clearly, by about age 3 (and often even before) the current DSM-IV/ICD-10 categorical approach can be used with little difficulty. Available work does highlight some limitations of their criteria for very young children (Stone, Lee, et al., 1999). An alternative categorical classification (National Center for Clinical Infant Programs [NCCIP], 1994) has been proposed, but its utilization in the clinical community has been limited, probably because its underlying conceptualization has been developed outside the body of nosological research in autism. Thus, there is little information on its concurrent validity with DSM-IV and related literature. Because the history of this system precedes the current wave of nosological efforts related to children under the age of 3 years, it would be critical for the NCCIP (now Zero to Three) system to be properly researched and its clinical and concurrent validity (relative to other systems), reliability, and other psychometric properties to be adequately assessed. More generally, well-documented diagnostic instruments may work well after age 3–4 years or past a certain developmental level (often around 18 months), but their use is not clearly established for the first years of life. Dimensional assessment instruments have a number of potential advantages—for example, in their approach to developmental change and/or developmental level—and may be of particular use, given the greater potential for change in this age group. Similarly, screening approaches (see Bishop et al., Chapter 2, this volume) are particularly important in terms of identification of children in need of services but present their own issues in terms of design and evaluation. Unfortunately, what is critically needed, but not yet available, are methods that rely on biological markers or some other robust, readily measured indicator of risk. Given the lack of such markers, clinician-assigned diagnosis, as provided by experienced clinicians, remains the “gold standard” for diagnosis in infancy (Chawarska, Klin, et al., 2007; Cox et al., 1999; Gillberg et al., 1990; Lord, 1995; Stone, Lee, et al., 1999).
+
+SUMMARY AND CONCLUSIONS:
+THE SIGNIFICANCE OF EARLY CASE DETECTION
+The growing body of work on autism in infants is important for several reasons. Available data suggest that with earlier case detection the outcome of autism is gradually improving; for example, more and more individuals are able to live independently and fewer are likely to remain mute and to exhibit comorbid intellectual disability (Howlin, 2005). The recent National Research Council (2001) review of evidence on early treatment notes that, despite various limitations, a considerable body of work on the importance of early intervention now exists. In addition to its being important for treatment and long-term outcome, early detection is also important in clarifying the earliest developmental processes, which may be disrupted in autism. Prospective research is critically needed to help us to more fully understand the basic mechanisms of psychopathology and to clarify how early difficulties become entrained in subsequent development. Somewhat paradoxically, those who work with both higher-functioning older individuals with autism and very young infants are impressed not only by the potential for significant developmental change, but by the severity and continuity of difficulties across time and development—for example, in modulation of the human voice in prosody and in the use of eye contact to mediate social interaction (Paul, Augustyn, Klin, & Volkmar, 2005). The ability to observe these early processes without the accompanying overlay of subsequent development will be particularly important. Study of the range of early developmental skills in this population may also result in some clinical surprises; for examples, there is now a suggestion that for a subgroup of infants, difficulties in affect regulation and temperament may be the more striking initial signs of autism rather than disturbances in social interaction (Bryson, Zwaigenbaum, Brian, et al., 2007). Consistent with Kanner’s (1943/1968) original description, it appears that in many cases infants are born with autism. It is also clear that in a variably reported, apparently small number of cases, the child develops reasonably normally for a time before autism appears. Although much work remains to be done, it is possible even now to begin to understand how some of the early manifestations of autism become entrained in subsequent development. Data from this age group may shed important light on perplexing clinical questions—for instance, the well-established differences in gender ratio and severity may be apparent before age 3 years (Carter et al., 2007). Careful follow-up studies also emphasize the potential difficulties of early diagnosis (Sutera et al., 2007), further underscoring the importance of biological markers and the study of specific biological and neuropsychological processes for better early diagnosis. To this end, the study of very specific social processes under highly controlled conditions may be particularly important (e.g., Chawarska, Klin, & Volkmar, 2003; Chawarska & Volkmar, 2005; Chawarska & Shic, 2007; Klin, 1992; Klin & Jones, in press; Klin, Jones, Schultz, Volkmar, & Cohen, 2002; Merin et al., 2007; Presmanes et al., 2007). As such processes are identified, siblings can also be studied to address potential contributions of these processes to the broader autism phenotype (Cassel et al., 2007; Presmanes, Walden, Stone, & Yoder, 2007; Toth et al., 2007; Gamliel et al., 2007).
+
+ACKNOWLEDGMENTS
+Preparation of this chapter was supported in part by grants from the National Alliance for Autism Research/Autism Speaks and the National Institute of Mental Health (Grant No. U54 MH676494) to Fred R. Volkmar, Katarzyna Chawarska, and Ami Klin.
+
+Diagnostic Assessment
+SOMER L. BISHOP
+RHIANNON LUYSTER
+JENNIFER RICHLER
+CATHERINE LORD
+Autism is a neurodevelopmental disorder characterized by deficits in social reciprocity and communication and by the presence of restricted and repetitive behaviors and/or interests. According to the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994) and the International Classification of Diseases (ICD-10; World Health Organization, 1992), in order to receive a diagnosis of autism, a child must have shown abnormalities in social interaction, language as used in social communication, or symbolic/imaginative play before the age of 3 years. If a child does not meet all of the above criteria for autism, he or she may be given a diagnosis of Asperger syndrome (AS) or Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS). We refer to these three diagnoses together as autism spectrum disorders (ASD). Because there is not yet a biological marker for ASD, a diagnosis of ASD is made on the basis of a behavioral profile, which is characterized by both the absence of typical behaviors as well as the presence of atypical behaviors. Recently, researchers and clinicians have sought to identify ASD earlier and earlier, owing to findings that early intervention is associated with improved outcomes (Harris & Handleman, 2000). This is somewhat problematic, however, because whereas the behavioral features of ASD are well established for children in the preschool years and beyond, less is known about symptom presentation in the first 2 years of life (Zwaigenbaum et al., 2005; Mitchell et al., 2006). Indeed, DSM-IV criteria were established based on the profile exhibited in early and middle childhood and do not necessarily apply to children under the age of 3. Therefore, professionals should exercise caution when making diagnoses in very young children. Furthermore, any assessment for possible ASD needs to be comprehensive and include a consideration of other disorders of early childhood. Because ASD is a developmental disorder and different symptoms are diagnostic at different points in development, understanding what is developmentally appropriate for children under 3 is an important first step in early identification of the disorder. This chapter addresses issues in the assessment and diagnosis of ASD in infants and toddlers. The first section provides a brief summary of the development of social, communication, and play behaviors in typically developing young children. Next, we provide guidelines for assessment and differential diagnosis of children with ASD, including the importance of considering social, communication, and play behaviors in the context of a child’s overall developmental functioning. Finally, we review the currently available screening instruments for identifying ASD in infants and toddlers, with special attention given to their appropriateness and limitations for use with children under 3 years of age.
+
+EARLY TYPICAL DEVELOPMENT
+The impairments that result from ASD are defined in relation to typical development. Reciprocal interaction and communication difficulties involve deficits in behaviors that emerge in typically developing children without explicit teaching and that are adaptive in their social contexts. In the area of restricted and repetitive behaviors and interests (RRBs), impairment refers to the presence of unusual, sometimes maladaptive behaviors that are, at least according to common wisdom, not usually seen in typically developing children. Thus, in order to determine if a child is showing signs of ASD, it is crucial to have a clear understanding of what constitutes typical behavior in a child of the same developmental level. As more and more children are being referred for a diagnosis of ASD at very young ages, it has become particularly important to have a comprehensive picture of social and communicative behavior in typically developing infants and toddlers. This understanding can help clinicians and researchers avoid overdiagnosing autism as well as wrongly dismissing real, appropriate concerns about behaviors associated with ASD. A large body of evidence suggests that children come into the world already socially oriented and that their social understanding becomes richer and more sophisticated in a relatively short period of time. Newborns prefer looking at faces over nonface patterns (Valenza, Simion, Cassia, & Umilta, 1996) and prefer listening to speech over nonspeech sounds (Vouloumanos & Werker, 2004). Meltzoff and Moore (1989) have shown that newborns can imitate simple human gestures, such as tongue protrusions and head movements, and by just 6 weeks of age they can engage in deferred imitation, emulating others’ facial movements after a 24-hour delay (Meltzoff & Moore, 1994). Children as young as 6 months can distinguish between purposeful and nonpurposeful action (Woodward, 1999), and by 9 months of age they are able to follow and direct the attention of adults to outside entities, a capacity known as joint attention (Tomasello, 1995). At approximately 12 months of age, infants begin to engage in social referencing by using the emotional reactions of others to determine how to behave (Walden & Ogan, 1988). At approximately 18 months, toddlers can infer an adult’s intended action by watching failed attempts (Meltzoff, 1995). By 24 months of age, children adjust the language they use in conversation based on their understanding of what the listener knows (Tomasello, Farrar, & Dines, 1984).
+
+This progression shows an increasing understanding of others’ intentions in the first 2 years of life. Tomasello (1995) has proposed a developmental trajectory for the understanding of others’ intentions, whereby children progress from following and directing the attention of others without understanding their intentions, to understanding others as intentional agents, to learning that others’ intentions may not always match the situation. This work has recently been expanded to suggest that these early developments culminate in the understanding of shared intentions with another individual, which is believed to be a defining feature of human social interaction (Tomasello, Carpenter, Call, Behne, & Moll, 2005). Understanding of others’ intentions is also thought to underlie the ability to learn words. According to this view, early language acquisition represents a form of social cognition. In order for a child to learn the referent of a novel word, he or she must infer the referential intent of the speaker, using subtle cues such as the direction of the speaker’s gaze and other contextual clues (Baldwin, 1993). One of the most remarkable aspects of early development is its rapid pace. In a relatively short period of time, children’s understanding of the social world becomes quite sophisticated. Yet it is important to remember that there is a great deal of variability in early trajectories of social and communication development. Fenson et al. (1994) emphasize the importance of going beyond descriptions of the “modal child” in order to understand the range of variability that can be expected in typically developing children. For example, there is a great deal of variability in both early receptive language and expressive vocabulary development. As children get older, this variability increases, because children whose initial language is more advanced also show a higher rate of word acquisition. When considering variability in early social and communication development, then, it may be useful to examine not only differences in children’s abilities at a given point in development, but also differences in the developmental trajectories of these abilities over time.
+
+Marked individual differences have been found in other areas of communication, such as use of gestures, as well as in the development of social cognition. In a comprehensive study of children’s social and communication development from 9 to 15 months of age, Carpenter, Nagell, Tomasello, Butterworth, and Moore (1998) found considerable variability in attention and gaze following, imitation, gesture production, and joint engagement, among other skills. Although the majority of the children in their sample displayed these skills by the age of 12 months, some children acquired these skills earlier than others (e.g., as young as 9 months) and some children had not acquired certain skills by 15 months. Some researchers have argued that differences in child temperament might explain some of the variability in early social and communication development. Dixon and Smith (2000) found that temperament exhibited in early development was related to subsequent language skills, both receptive and expressive. In their sample, children who showed greater adaptability, more positive mood, and greater persistence at 13 months tended to have more productive language at 20 months, and children who had long durations of orientation, smiled and laughed frequently, or were easily soothed at 7 months tended to have advanced comprehension at 7 and 10 months of age. Other studies have found similar relationships between children’s early temperament and later language (Slomkowski, Nelson, Dunn, & Plomin, 1992). Dixon and Smith (2000) suggest that the relationship between temperament and language may be mediated by amount of joint engagement. That is, parents and others may be less likely to enter into a social exchange with a child who shows negative affect and poor adaptability than they would with a child who shows positive affect. The reduced amount of social interaction may in turn adversely affect the child’s understanding and production of language. This model is partly supported by a finding in the study by Carpenter et al. (1998) that the amount of time mother–infant dyads spent in joint engagement was related to the child’s early verbal and nonverbal communication skills.
+
+The fact that there is a wide range of social and communication skills among typically developing young children presents a challenge to those trying to identify “markers” of ASD in children of this age. How does one decide if a toddler has a true impairment in reciprocal social interaction and/or communication that puts him at risk for a diagnosis of ASD, or whether he simply falls on the lower end of the continuum of typical development in these areas? Is a toddler who does not smile at others very often simply showing less positive affect than the “average” typical child of the same age because of her temperament, or does she have a more fundamental difficulty in interacting with others? It is also important to consider the role that cultural differences play in a child’s social and communication behaviors (see Babad et al., 1983). The child’s social environment, including culturally based parenting practices, is likely to influence some of the aspects of infant social communication behaviors. Additional insight into this issue may come from considering “constellations” of deficits, rather than individual impairments. ASD is commonly thought of as involving deficits in several different areas (Siegel, Pliner, Eschler, & Elliott, 1988). It may be that, in order to be considered “at risk” for ASD, a child should be showing deficits in more than one of these areas. Therefore, when considering a particular social or communicative behavior in a young child, it may be important to consider whether the child is “below average” or “impaired” in a specific behavior, but also whether difficulties in that behavior occur in the context of other impairments. For example, a child who shows delays in using sounds and words but who shows positive affect, good eye contact, and use of early gestures would likely not elicit much concern as a child who, in addition to having delayed expressive language, shows impairments in other areas. Practitioners may also want to consider these issues when conducting evaluations of slightly older children. Despite the minimum onset requirement presented by DSM-IV—delay or abnormality in social interaction, or in language as used in social communication, or in imaginative play prior to age 3—practitioners may want to require that these types of early atypicalities occur in conjunction with one another in order to establish the onset of ASD.
+
+Even with these distinctions in mind, it can be difficult for clinicians and researchers to determine whether a child who shows two or three developmental difficulties falls somewhere on the autism spectrum, is developmentally delayed but not on the spectrum, or is simply behind relative to the “average” typical child, but still within the range of typical development. PDD-NOS is usually diagnosed in young children who show some impairments characteristic of autism, but in such instances the number of impairments is fewer than that required for a diagnosis of autism, the impairments do not occur across all three areas specified (i.e., social, communication, RRBs), or the impairments are not as severe. This could explain why studies have found that early diagnoses of PDD-NOS are not as stable as diagnoses of autism (Chawarska, Klin, Paul, & Volkmar, 2007). A recent study found that the majority of children diagnosed with PDD-NOS at age 2 remained on the autism spectrum at age 9. Nevertheless, more than 10% of the children with PDD-NOS diagnoses at 2 years moved to a nonspectrum classification by 5 or 9 years (Lord et al., 2006). Given these criteria, it is possible to see how some young children thought to have “mild autism” might, at older ages, more clearly appear to have nonspectrum developmental delays or fall at the lower end of the continuum of typical development. RRBs differ from most social and communication deficits required for a diagnosis of ASD, because they involve the presence of “atypical” behaviors rather than the absence of typical ones. Yet it is important to remember that some RRBs are actually seen in young children with typical development. Thelen (1979) reported motor stereotypies, such as kicking, waving, banging, rocking, and bouncing in normal infants in their first year of life, especially between the ages of 24 and 42 weeks. As toddlers and preschoolers, many typical young children display compulsive-like behaviors, such as insistence on sameness in their routines and/or environment, strong likes and dislikes, a rigid idea of how things should look, feel, taste, or smell, and a strict adherence to rituals during times of transition, such as at bedtime. Evans et al. (1997) found evidence for two kinds of compulsive-like behaviors in a substantial portion of young children: “just right” behaviors (e.g., lining objects up or insisting that they be arranged in a precise way) and repetitive behaviors/insistence on sameness (e.g., preferring to have the same schedule every day). It has also been argued that some repetition in object use or exploration (i.e., Piagetian secondary circular reactions) is important for developing cognitive skills, such as problem solving.
+
+It is interesting to note the similarities between these behaviors and those considered to be “restricted and repetitive behaviors and interests” (American Psychiatric Association, 1994) in children with ASD. Many of the behaviors that constitute the category of RRBs are similar to those described in studies of typical children. Factor analyses of RRB scales in ASD have also found evidence for the two “subtypes” of RRBs described above (Cuccaro et al., 2003; Bishop, Richler, & Lord, 2006). Given these similarities, it is important to ask what is different about these behaviors in typically developing children as opposed to children on the autism spectrum, and why their presence at a young age is not necessarily an indicator of later impairment. Part of the answer may lie in the developmental trajectories of these behaviors in typically developing children as compared to those of children with ASD. In the study by Evans et al. (1997), children between the ages of 24 and 36 months were found to exhibit the highest frequency and intensity of compulsive-like behaviors; after 36 months, scores tended to decrease steadily and then more steeply, so that mean scores between the ages of 48 and 72 months were significantly lower. This pattern suggests that most typically developing children tend to “grow out of” these behaviors, although this conclusion should be made with caution, given that the data in this study were cross-sectional. In contrast, longitudinal studies of RRBs in children with ASD have found that many of these behaviors tend to increase in prevalence and severity with time, at least up until the age of 5 and possible until older ages (Moore & Goodson, 2003; Charman et al., 2005). One of the main differences in these behaviors in typically developing children versus those with ASD could be that, for most typical children, the behaviors tend to be common only within a relatively narrow window of development, in contrast to children with ASD, who exhibit these behaviors for longer periods of time (Thelen, 1979).
+
+This again highlights the importance of considering the differences in constellations of behaviors in young children with typical development as opposed to those with ASD. In the study by Evans et al. (1997), mean scores of typical children on the Childhood Routines Inventory (CRI) were very low relative to the maximum achievable score. Most children exhibited one or two of these behaviors, or if they did exhibit a few, the behaviors were relatively mild. In contrast, studies of RRBs in children with ASD indicate that the majority of children, even those at young ages, tend to exhibit more than two repetitive behaviors, and that these behaviors often interfere with the functioning of the child or the family (Richler, Bishop, Kleinke, & Lord, 2007). These findings suggest that it is important to consider whether a child who shows one particular compulsive-like behavior (e.g., lining up toy cars) also shows other similar behaviors (e.g., insisting that objects be placed in specific locations). Similarly, it is important to consider whether the presence of such behaviors occurs in concert with impairments in social interaction and communication. Part of the reason that repetitive behaviors tend not to be as severe in typically developing children may be that these children do not have the added component of impairment in social interaction and communication to contend with. As a result, they are likely to spend more of their time interacting and communicating with others than engaging in repetitive activities. Even when they do engage in repetitive activities, they are likely to involve others in these activities, which makes the activities more social and flexible. In contrast, children with ASD often prefer to participate in repetitive activities rather than interact with others, which further deprives them of social stimulation (Rogers & Ozonoff, 2005).
+
+Considering the early pictures of typical development and atypical development in social interaction and communication side by side, it is interesting to consider why the trajectories of typical development begin to diverge from those of the development of children with ASD. Some have argued that most children are born “hard-wired” to be oriented to the social world, responding to social input in their environment, and in turn, receiving more input (see Johnson et al., 2005). It has also been suggested that the early plasticity of the brain may provide an opportunity for experience to shape synaptic connections, eliminating those that are not needed and strengthening those that are crucial for higher-order functions, such as social cognition (Courchesne, Carper, & Akshoomoff, 2003). In contrast, some children may be born without the same predisposition to prioritize social input over nonsocial input (Dawson, Meltzoff, Osterling, Rinaldi, & Brown, 1998; Dawson et al., 2004) or may experience changes in trajectories of social and communication development, such as reaching a developmental plateau (Siperstein & Volkmar, 2004) or experiencing an actual worsening or regression in social and communication skills (Ozonoff, Williams, & Landa, 2005). Thus, for a number of reasons, children with ASD may not receive the same social input from the environment as typically developing children during this critical period of brain development (Mundy & Neal, 2001). Consequently, the parts of the brain normally involved in social cognition may not be selectively shaped for this role (Johnson et al., 2005). As typically developing children become more socially sophisticated in the first few years of life, the impairments of children with ASD may build on each other and become more apparent.
+
+EARLY ASSESSMENT OF ASD
+Because of the cumulative effects of early appearing deficits, such as those described above, detection of the symptoms of ASD tends to become easier as children get older. However, as we have come to understand more about early development of ASD, it has become increasingly possible to differentiate children with ASD from typically developing young children. Furthermore, whereas professionals have traditionally been hesitant to make diagnoses of ASD in children under the age of 3, recent literature suggests that when made by experienced clinicians, diagnoses of toddlers are relatively stable over time. Because different methods (i.e., clinical observation and parent report) provide different types of information, diagnoses are most accurate and stable when based on information obtained from multiple sources (e.g., Lord et al., 2006; Chawarska, Klin, et al., 2007). Even for experienced clinicians, diagnosis can be difficult when trying to distinguish between ASD and other early childhood disorders. Psychological diagnoses, such as intellectual disability, expressive and receptive language disorders, anxiety disorders, and Attention-Deficit/ Hyperactivity Disorder (ADHD), as well as genetic disorders, such as fragile-X syndrome, share many features with ASD. Making diagnostic distinctions in very young children is therefore a difficult process.
+
+Diagnosis of ASD is further complicated when a researcher or clinician is trying to determine whether a child meets the criteria for autism versus another spectrum condition (e.g., PDD-NOS, AS). Childhood Disintegrative Disorder (CDD) and Rett syndrome are quite rare and are less likely to be confused with autism, especially if a thorough medical history is obtained. On the other hand, clinicians often find themselves trying to distinguish between autism and AS or PDD-NOS, in part because of poor agreement about the diagnostic criteria of these disorders (Ozonoff, South, & Miller, 2000). DSM-IV provides guidelines for making diagnostic distinctions between these disorders, but these guidelines were written on the basis of studies of substantially older children (Volkmar et al., 1994; Chawarska & Volkmar, 2005). Both CDD and Rett syndrome are characterized by a period of apparently normal development followed by a substantial regression. The onset of CDD must occur after 2 years of age, and the child must exhibit loss of previously acquired skills in at least two areas, such as language, social skills, adaptive behavior, play, toileting, or motor skills (American Psychiatric Association, 1994). After the regression, children with CDD must also exhibit impairments in at least two of the three domains in autism (i.e., social interaction, communication, restricted and repetitive behaviors/interests). The regression in Rett syndrome, which is a genetic disorder that occurs primarily in girls, occurs between the ages of 5 and 48 months and is characterized by decelerated head growth; loss of previously acquired purposeful hand movements, such as holding utensils or picking up objects; the development of stereotyped midline hand movements, such as hand wringing; loss of social engagement; appearance of poorly coordinated gait or trunk movements; and severely impaired language development (American Psychiatric Association, 1994).
+
+Although approximately 20% of children with autism experience a regression in language or social behaviors (Lord, Shulman, & DiLavore, 2004; Volkmar, Chawarska, & Klin, 2005), the regressions in CDD and Rett syndrome are qualitatively distinct from the most common forms of regression in autism. First, whereas the regressions in CDD and Rett syndrome follow a period of typical development, some abnormality in children with autism is most often recognized, in hindsight, in the first year of life (Osterling & Dawson, 1994), prior to the onset of regression. Second, the regression in autism is characterized by a loss of language and/or social behaviors, without a loss of adaptive or motor skills (Volkmar & Rutter, 1995; Luyster et al., 2005), which are both typically seen in Rett syndrome and CDD. Moreover, the regression in children with autism is almost always before the age of 24 months (Lord et al., 2004; Luyster et al., 2005; Ozonoff et al., 2005; Chawarska, Paul, et al., 2007). In addition to early regression, there are other behavioral markers that have been established as indicators of ASD in the first few years of life. Retrospective analyses of videotapes of children in their first year of life have indicated that those who later receive a diagnosis of ASD exhibit poor visual orientation and attention, limited response to name, lack of socially directed looking, excessive mouthing of objects, and aversion to social touch, relative to comparison groups of typically developing children and children with non-ASD developmental delays (Baranek, 1999; Osterling, Dawson, & Munson, 2002).
+
+More recently, prospective studies of infant siblings of children with ASD, a population of children at high risk for developing ASD, have suggested a number of early features that are associated with a later diagnosis of ASD. Using the Autism Observation Scale for Infants (AOSI) (see below), Zwaigenbaum et al. (2005) found that at 12 months of age, children who were later diagnosed with ASD showed evidence of language delay, as well as several behavioral abnormalities, such as difficulties with eye contact, visual tracking and attention, social smiling, imitation, social interest and affect. These infants also tended to demonstrate decreased positive affect and were more likely to exhibit extreme distress reactions and to fixate on objects. Using a parent-report measure of early communication skills, the MacArthur–Bates Communicative Development Inventory—Infant Form (Fenson et al., 1993), Mitchell et al. (2006) found that infant siblings who later met criteria for ASD reportedly understood fewer phrases and demonstrated significantly fewer gestures at 12 months of age than typically developing controls or siblings who did not receive ASD diagnoses. These delays were still apparent at 18 months of age, as were reported delays in understanding and use of single words.
+
+Studies comparing very young children with ASD to those with other types of developmental delays have made it increasingly possible for clinicians and researchers to differentiate between ASD and other non-spectrum disorders. However, obtaining agreement between diagnoses of autism, AS, and PDD-NOS has been much more difficult, particularly in young children. Several different definitions for these disorders exist, which has complicated communication between professionals in the field (Ozonoff et al., 2000; Klin, Pauls, Schultz, & Volkmar, 2005). According to DSM-IV, AS is characterized by both qualitative impairments in social interaction and the presence of RRBs that are identical to those seen in autism. However, unlike in autism, there can be no delay in language, cognitive development, or adaptive behavior (except social skills) in a diagnosis of AS (American Psychiatric Association, 1994). A diagnosis of PDD-NOS is intended for children who exhibit significant impairments in reciprocal social interaction, as well as difficulties in either communication or the presence of RRBs (or subthreshold difficulties in both areas), who do not meet criteria for another ASD. Evidence suggests that diagnoses of PDD-NOS in early preschool are less stable than autism diagnoses (e.g., Stone et al., 1999; Lord et al., 2006). Clinicians are more reliable when making distinctions in 2-year-olds between ASD and nonspectrum diagnoses than between specific diagnoses on the spectrum, and it is not uncommon for children to have a change in diagnosis within the spectrum (e.g., from a diagnosis of PDD-NOS to autism) (Stone et al., 1999; Lord et al., 2006). What may be most important for very young children, therefore, is making a distinction between a spectrum and a nonspectrum diagnosis, because differential diagnoses within the spectrum tend to be less stable (Lord et al., 2006). Thus, the intervention that children receive should be based more on their individual profiles of strengths and weaknesses, rather than on their specific diagnostic classifications.
+
+SCREENING FOR ASD
+General Issues
+Our increasing knowledge of early development in children later diagnosed with ASD has facilitated the creation of a number of screeners targeted at identifying young children with ASD. A major challenge associated with the development of these instruments is being able to discriminate children with significant developmental delays from children with less pervasive and often temporary developmental delays. In large part, the ability to do this depends on our understanding of what verbal and nonverbal skills cluster together in the first few years of typical development, and the degree to which these skills are impaired in children for whom ASD is a concern. Clarifying these early profiles of development and using them to screen for ASD in young children has both theoretical and practical implications. First, identifying children with ASD in the first few years of life allows for the collection of data about early profiles and trajectories of development. Such information is valuable for theoretical accounts of ASD as well as for informing efforts to improve the accuracy of the screening instruments themselves. In addition, earlier detection of ASD permits prompt delivery of intervention services, and research has indicated that intervention is more effective if provided earlier (Harris & Handleman, 2000).
+
+Different approaches have been taken in designing screeners, some using caregivers as informants and others using professionals. There are also two levels of screeners, one designed for population-based screening (i.e., level 1 screeners), and the other designed for more targeted screening of children suspected of having a developmental disorder (i.e., level 2 screeners). In general, screening is distinct from diagnostic assessment in that the former is a relatively broad-based approach intended to identify children with unrecognized or ambiguous symptoms of developmental disabilities, whereas the latter is most appropriate for children for whom there is already some clear evidence of developmental abnormality. Level 1 screeners typically employ caregiver reports as a means of gathering information. The primary advantage of this approach is that parents and caregivers are most familiar with the skills of the child across a variety of situations, and they may be more accurate than professionals in reporting low-frequency behaviors (such as using another person’s hand as a tool). However, caregivers may have less experience with children and a less refined understanding of the questions on the screener, which could potentially result in either over- or underestimating their child’s skills. There is also a risk of biased reporting (if caregivers already have beliefs about the diagnostic status of their child). Finally, creating a scale that caregivers will interpret as intended can be quite difficult. Level 2 screeners that use the reports of professionals (such as health care workers or psychologists) have a different set of advantages and disadvantages. Professionals may be more highly trained in observing and identifying certain diagnostically meaningful behaviors, and completion of the screeners can be standardized across reporters. However, they spend much less time with the child. As a result, they generally do not have the opportunity to evaluate the child across contexts and are much less likely than caregivers to note low-frequency behaviors.
+
+In evaluating the effectiveness of level 1 and level 2 screeners, it is useful to consider the constructs of sensitivity and specificity. Sensitivity refers to a measure’s ability to accurately “rule in” all individuals with the targeted trait, and specificity refers to its ability to accurately “rule out” all individuals without the targeted trait. In the context of screening for ASD, sensitivity may be more important for level 1 screeners, because high sensitivity can maximize the detection of children who are showing a behavioral profile suggestive of ASD. Regardless of whether ASD is their final diagnosis or not, these children are likely to be “at risk” for one form of disability or another and will benefit from identification and referral. Thus, even though decreasing specificity and increasing sensitivity results in more false positives, which can be expensive and potentially problematic, it is better to identify these children with developmental complications early on, rather than later or not at all. In contrast, for level 2 screeners and diagnostic measures, specificity is a higher priority, because it is at this point that the measures must be able to discriminate ASD from phenotypically similar conditions, such as language delay or intellectual disability.
+
+Screening Instruments
+Current measures for screening and diagnosis are considered here in turn, with reference to current research on their advantages and limitations for children under age 3. Population (level 1) and focused (level 2) screening are addressed (see Figure 2.1). The CHecklist for Autism in Toddlers (CHAT; a level 1 screener) was initially introduced in the United Kingdom as a population screening measure for ASD (Baron-Cohen, Allen, & Gillberg, 1992). The CHAT emphasized joint attention and imagination and was administered to children by health nurses, who routinely visit 18-month-olds in their homes in the United Kingdom. During the visit, the parents were also asked a series of questions about their child’s development. Results indicated that most children classified by the CHAT as having autism were, in fact, later diagnosed with the disorder. However, it later became clear that two-thirds of the children who eventually received an ASD diagnosis were missed by the CHAT (Baird et al., 2000). Moreover, because children with suspected developmental disabilities were eliminated even before the screening, the CHAT’s effectiveness in distinguishing between ASD and other developmental disabilities was unclear. The Modified CHecklist for Autism in Toddlers (M-CHAT), a modified version of the CHAT (Robins, Fein, Barton, & Green, 2001), was created to address some of these concerns. The M-CHAT was administered to parents of 24-month-old children who were recruited from pediatric practices and special education programs in the United States. In contrast to the CHAT, the M-CHAT is not administered to the child and instead relies on parent report. Like its predecessor, the M-CHAT successfully identified children with autism at age 2. The M-CHAT was tested on two groups of children, a population sample and a sample from special education programs. More than 90% of the children identified as having autism were already in special education programs (Robins et al., 2001), so the effectiveness of the M-CHAT for use in the general population is not yet clear. Initial reports of sensitivity and specificity were very high (.87 and .99, respectively), but the authors caution that absolute psychometrics for this measure cannot be determined until all follow-up evaluations are completed (Robins et al., 2001; Robins & Dumont-Mathieu, 2006). A larger study of the M-CHAT in a more representative sample is now under way, and the results of this study will be important in evaluating the effectiveness of the M-CHAT.
+FIGURE 2.1. Levels of screening and diagnosis for children with ASD at age 3 or younger.
+
+The Early Screening for Autistic Traits (ESAT; Swinkels et al., 2006) is a level 1 screener questionnaire with a greater emphasis on play and less on joint attention than the previous instruments. Children who earned high scores on the instrument were likely to have developmental problems. However, for children younger than 24 months of age, the ESAT did not successfully distinguish children with ASD from those with non-ASD conditions. In addition, like the CHAT and M-CHAT, it also failed to identify many children who were later diagnosed with ASD (see, e.g., Buitelaar et al., 2000). Despite the measure’s problems with poor sensitivity, the use of the ESAT heightened public awareness and provided easy access to referrals. As a result of these related benefits, early identification increased. The Communication and Symbolic Behavior Scales—Developmental Profile (CSBS-DP; Wetherby & Prizant, 2002) is a brief caregiver questionnaire intended to identify children with communication disorders (not specifically ASD) between the ages of 6 and 24 months. If a child screens positively on the questionnaire based on his or her caregiver’s responses, then a direct assessment (the Behavior Sample) and an additional caregiver questionnaire are administered. Although the initial questionnaire is a level 1 screener, a level 2 screener—the Scale of Red Flags (SORF; Wetherby & Woods, 2002) for autism—was developed for use in scoring videotapes of the Behavior Sample. With the SORF, researchers were able to successfully identify most children with language delay as having or not having autism. However, because most of the children observed had screened positively on the CSBS caregiver questionnaire, there was no way to identify missed cases and determine the measure’s sensitivity.
+
+The Pervasive Developmental Disorders Screening Test (PDDST; Siegel, 1996) also offers a level 1 and a level 2 screener and is intended for children over the age of 18 months and under the age of 6 years. It is a parent report questionnaire and is designed to screen specifically for ASD. It targets areas of first concern frequently reported by parents of children with ASD, such as nonverbal communication, temperament, play, language, and social engagement. Stage 1 of the PDDST (intended for use in primary care settings) was reported to have a sensitivity of .85 and a specificity of .71 in a clinic-based sample. In a sample of children with ASD and children with other developmental disorders, sensitivity and specificity of the PDDST-Stage 2 (intended for use in developmental disorders clinics) varied according to the cutoff used, ranging from .69 to .88 and .25 to .63, respectively (Siegel, 1996; Siegel & Hayer, 1999). Research on the PDDST is ongoing to provide further details about its psychometric properties and usefulness in different populations. The Screening Test for Autism in Two-Year-Olds (STAT; Stone, Coonrod, & Ousley, 2000) involves a direct assessment and, as a level 2 screener, is intended for children already suspected of having ASD. However, unlike the diagnostic tests described below, it is relatively brief. In addition, it is more straightforward to administer and score; consequently, it does not require extensive training on the part of the examiner. In a validation sample of 12 children with autism and 21 children with non-spectrum developmental disorders, the STAT correctly identified 10 (83%) of the children with autism and 18 (86%) of the children with other developmental disorders (Stone et al., 2000). The Social Communication Questionnaire (SCQ; Rutter, Bailey, Lord, & Berument, 2003) is a level 2 caregiver questionnaire designed to identify participants with ASD for research purposes. Although the measure was normed on older children and adults, research has indicated that if the cutoff is modified so that fewer endorsed items are required, the SCQ works well for children as young as 3 years old (Corsello et al., 2007). However, because the children had already been referred for services, it is unclear how appropriate the SCQ is for use in the general population.
+
+There are two other well-known scales primarily intended for level 2 screening but which may be mistaken for diagnostic instruments: the Childhood Autism Rating Scale (CARS) and the Gilliam Autism Rating Scale (GARS). The CARS (Schopler, Reichler, & Renner, 1988) is most useful with children beyond the 2-year-old level and up to 4 or 5 years in developmental skills, and it has been shown to have high sensitivity in older children and adults (Sevin, Matson, Coe, Fee, & Sevin, 1991; Eaves & Milner, 1993). Studies have yielded mixed results with regard to the utility of the CARS for use with very young children. Lord (1995) reported that the CARS overidentified autism in 2-year-olds with cognitive impairments, whereas Stone and colleagues (1999) reported good agreement with clinical diagnosis at age 2 (82% agreement). Agreement of the CARS and clinical impression is better by age 3 (Lord, 1995; Stone et al., 1999), and specificity can be improved by raising the CARS cutoff by 2 points (Lord, 1995). The GARS (Gilliam, 1995) is a behavioral checklist that was developed to screen for autism. However, the measure was not designed for or normed on children under 3 years of age, and thus its usefulness for a young population is unknown. One study (South et al., 2002) employed the GARS in a sample of 119 preschool and school-age children with autism. Overall, the GARS underdiagnosed autism, failing to accurately classify more than half of the sample. Until revisions are made, the appropriateness of the GARS for children under 3 is limited.
+
+EARLY DIAGNOSIS OF ASD
+Once a child has been identified as being at risk for ASD, he or she should be referred to a psychologist, psychiatrist, or developmental pediatrician who specializes in early diagnosis of developmental disabilities. To aid clinicians in making accurate diagnoses, it is essential that a diagnostic assessment be multidimensional and multidisciplinary (see Figure 2.2). This includes gathering information from different sources and assessing a child’s behavior across a variety of contexts. Research has indicated that diagnoses of 2-year-olds were significantly more stable when confirmed across two or three sources (i.e., standardized parent interview, direct child observation, and clinician’s best estimate diagnosis) as opposed to just one (Lord et al., 2006). Several instruments have been designed to aid professionals in gathering information needed to make a diagnosis of ASD. (For a more comprehensive discussion of practice parameters and diagnostic instruments, see Filipek, Accardo, & Ashwal, 2000; Klinger & Renner, 2000; Lord & Corsello, 2005; and Bishop & Lord, 2006.) Standardized parent interviews and questionnaires can be useful in eliciting information from parents about their child’s behavior. In contrast to the traditional open-ended interview, semistructured interviews allow for a more comprehensive assessment of communication, social, and play behaviors associated with ASD and other developmental disorders. The most widely used and well-established semistructured interview that is designed to diagnose ASD is the Autism Diagnostic Interview—Revised (ADI-R; Lord, Rutter, & Le Couteur, 1994).
+FIGURE 2.2. Assessing young children with suspected ASD.
+Medical Examination
+• Rule out sensory impairment (check hearing and vision).
+• Conduct genetic testing if indicated based on dysmorphology or family history.
+• Conduct neurological exam.
+Parent Interview
+• Obtain thorough developmental history (attainment and/ or loss of motor, speech, self-help milestones).
+• Administer semistructured interview to gather information about social and communication development, play, restricted and repetitive behaviors, and adaptive skills.
+Child Observation
+• Create context in which to observe child’s social-communication behaviors, play, and repetitive behaviors (with both parent and examiner).
+• Consult parents and teachers about whether behaviors observed during assessment were consistent with child’s behavior in other settings.
+Developmental and Language Testing
+• Assess verbal (expressive and receptive) and nonverbal abilities.
+• Gather information about receptive and expressive language abilities.
+• Evaluate gross and fine motor skills.
+
+The ADI-R provides quantifiable scores related to severity of symptoms in the areas of communication, reciprocal social interaction, and restricted and repetitive behaviors, as well as separate algorithms for verbal and nonverbal children. In order to meet criteria for a diagnosis of autism, the child must meet cutoffs in Communication, Reciprocal Social Interaction, Restricted and Repetitive Behaviors and Interests, and Age of Onset. In children over the age of 3, these cutoffs have been found to clearly differentiate between children with autism and those with other disorders (Lord et al., 1994). The validity of this instrument for children under the age of 3 has not been established. Therefore, a “Toddler” version of the ADI-R is undergoing development and being used in some investigations. It includes 32 additional questions and codes specifically relevant to onset of difficulties in the early years (C. Lord, personal communication, August, 2006). Because this modified instrument is not yet available for general use, professionals may decide to use the published version of the ADI-R but should use caution when interpreting the scores for children under the age of 3. In particular, some studies have reported low sensitivity of the ADI-R for populations of young children because many children do not meet cutoffs in the Restricted, Repetitive and Stereotyped Patterns of Behavior Domain (Ventola et al., 2006; Chawarska, Klin, et al., 2007). Although the majority of 2-year-olds with ASD exhibit RRBs (Richler et al., 2007), some of the RRB items that are currently included in the ADI-R algorithm (e.g., compulsions and rituals) may be less prevalent in very young children with ASD.
+
+The Diagnostic Interview for Social and Communication Disorders (DISCO; Wing, Leekam, Libby, Gould, & Larcombe, 2002) is another semistructured interview designed to aid in the diagnosis of ASD. Whereas the ADI-R is a diagnostic measure of ASD, the DISCO includes questions about a wider range of difficulties and can be used to compile information necessary to diagnose other developmental and psychiatric disorders. The Development, Diagnostic and Dimensional Interview (3di; Skuse et al., 2004) is a computer-based standardized interview intended to assess autism severity, as well as symptoms of comorbid conditions, such as ADHD. Although reliability and validity estimates of the 3di were high, the original validation sample of children with ASD consisted mainly of school-age children with relatively mild symptoms. Thus, the utility of the 3di for use in young children or those with more severe symptoms of ASD is less well established. Information obtained through parent report is an important part of any child assessment, but direct clinical observation of the child is also required in order to make an accurate diagnosis. Observations in which the clinician simply observes the child in an unstructured context do not always elicit behaviors associated with a diagnosis of ASD. Therefore, administering measures such as the Autism Diagnostic Observation Schedule—Generic (ADOS-G; Lord et al., 2000) and the Autism Observation Scale for Infants (AOSI; Bryson, Zwaigenbaum, McDermott, Rombough, & Brian, 2007) provides opportunities for the clinician to observe social, communication, and play behaviors in standardized, semi-structured contexts.
+
+The ADOS-G is organized into four modules that correspond to various levels of language skills, and each module is composed of a standard series of tasks designed to elicit information in the areas of communication, reciprocal social behavior, and restricted and repetitive behaviors. Module 1 is intended for children who are nonverbal or who have single-word speech, which generally makes it most appropriate for use in very young children suspected of having ASD. Module 2 is designed for children with phrase speech, so it may also be employed in assessing highly verbal 2- and 3-year-old children with suspected ASD. Modules 3 and 4 are less relevant for the present discussion, as they are intended for children with complex sentences (i.e., sentences with two or more clauses). As with the ADI-R, a toddler version of the ADOS-G is also currently under development. In addition, new algorithms have been developed that use the existing items of the ADOS-G to improve the sensitivity and specificity of the algorithms, especially in populations that can be difficult to classify (e.g., very young children) (see Gotham, Risi, Pickles, & Lord, 2007). Separate algorithms have been developed for children in Module 1 who use words meaningfully and spontaneously during the session and those who do not, and separate modules have been developed for children under and over age 5 years who receive Module 2. These modifications have resulted in increased specificity for autism and better sensitivity and specificity for nonautism ASD in most modules. The AOSI is intended to elicit the same types of information as the ADOS-G, but it is specifically designed for infants under 18 months of age. Thus, in addition to play-based activities similar to those in the ADOS-G, the AOSI also includes some tasks, such as eye tracking and attention shifts, that are intended to detect very early markers of ASD. As discussed previously, initial studies using the AOSI have suggested that siblings who are later diagnosed with ASD show differences in social and communication behaviors as early as 12 months (Zwaigenbaum et al., 2005). These play-based assessment tools help the clinician to structure the assessment context such that the child is given multiple opportunities to communicate and engage in social interactions with the examiner and parent. However, both the ADOS-G and the AOSI require extensive training to ensure standardized administration procedures and coding reliability.
+
+A number of prospective studies have suggested that children can be accurately diagnosed with ASD as young as 24 months when using standard instruments such as the ADI-R and the ADOS-G. However, although these measures produce reliable information about very young children, their diagnostic thresholds should be carefully applied to children who have nonverbal mental ages below certain cutoffs (15 months on the ADOS-G or 18 months on the ADI-R). Research has suggested that using the ADI-R and ADOS-G in this population results in a high rate of misdiagnosis for children who do not have ASD but are at very low developmental levels. Therefore, as described above, researchers are currently in the process of modifying these instruments to improve their appropriateness for use with very young children. The use of these instruments with certain special populations is also not well established, as they have not been validated for use in children with severe sensory impairments (e.g., congenital blindness, profound hearing impairment) or motor difficulties (e.g., children who are not walking).
+
+Because the selection of appropriate diagnostic instruments depends in part on the child’s developmental level and associated medical characteristics, it is essential to obtain a thorough medical and developmental history. This information is needed in order to make accurate interpretations about the child’s behavior. For example, in a child who has delayed motor milestones, such as sitting upright or walking, it is necessary to assess for general developmental delay and motor problems before interpreting behaviors, such as lack of babbling or poor eye contact, that may be more indicative of ASD. An adaptive behavior measure, such as the Vineland Adaptive Behavior Scales (VABS; Sparrow, Balla, & Cicchetti, 1984), can be helpful in providing estimates of a child’s general developmental level. Some researchers even suggest that children with ASD follow a particular profile on measures of adaptive behavior, which could be used for diagnostic purposes (Carter et al., 1998; Paul et al., 2004), but this has not yet been examined in very young children with ASD. Researchers are beginning to examine whether profiles on developmental tests, such as the Mullen Scales of Early Learning (MSEL; Mullen, 1995), can be used to differentiate between young children with and without ASD (Landa & Garrett-Mayer, 2006).
+
+Directly assessing a child’s developmental level is another important component of a diagnostic evaluation for ASD. Social, communication, and play behaviors cannot be accurately assessed without first knowing what is developmentally appropriate for an individual child. A toddler with moderate to severe intellectual disability who is functioning at a 1-year-old level would not be expected to communicate or interact socially at the same level as a 2-year-old with average developmental or cognitive skills. Similarly, a toddler with a language disorder would not be expected to verbally communicate as proficiently as a toddler without language delays. Therefore, incorporating developmental and language testing into the assessment battery is required in order for a clinician to interpret a child’s behavior in the context of his or her general developmental level (see Chawarska & Bearss, Chapter 3, this volume for further discussion). Because individuals on the autism spectrum often exhibit significant discrepancies between their verbal and nonverbal IQs (Joseph, Tager-Flusberg, & Lord, 2002), tests that do not rely too heavily on the use of language, such as the MSEL (Mullen, 1995), and that assess nonverbal skills separately from verbal skills, such as the Differential Ability Scales (DAS; Elliott, 1990), are ideal for testing children with suspected ASD or communication disorders. Assessing receptive and expressive language abilities separately is also important and can be accomplished through the use of measures such as the Sequenced Inventory of Communication Development (Hedrick, Prather, & Tobin, 1999), the Preschool Language Scale, fourth edition (PLS-4; Zimmerman, Steiner, & Pond, 2002) and the Reynell Developmental Language Scales (Reynell & Gruber, 1978) (see Paul, Chapter 4, this volume, for further discussion).
+
+CONCLUSION
+There is an accumulating body of evidence to suggest that by the age of 2 years, it is possible to distinguish children with ASD from typically developing infants and those with nonspectrum developmental delays. Furthermore, longitudinal studies suggest that diagnoses of ASD are relatively stable over time. However, differentiating ASD from other kinds of developmental delays requires information from multiple sources, as well as systematic observation of the child by an experienced clinician. Research has suggested that obtaining an accurate diagnosis of ASD early in life has important theoretical and practical implications. The earlier we are able to identify the symptoms of ASD, the closer we will come to understanding the etiology of the disorder on a genetic and neurobiological level. In addition, gathering information about the earliest indicators of ASD may provide insight into the core symptoms and primary deficits of the disorder. Early identification is also a crucial first step in obtaining appropriate intervention services. This is particularly important, given recent findings that earlier intervention is associated with improved outcomes.
+
+ACKNOWLEDGMENTS
+The authorship of this chapter is alphabetical. Each author contributed equally to the writing of the chapter. This work was supported by Grant Nos. R01MH066496 from the National Institute of Mental Health and HD 35482-01 from the National Institute of Child Health and Human Development to Catherine Lord. Somer L. Bishop’s work was also supported in part by National Institute on Alcohol Abuse and Alcoholism Training Grant No. T32 AA 07477 to Robert Zucker, and Rhiannon Luyster’s work by National Research Service Award No. F31MH73210-02 from the National Institute of Mental Health.

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+Children with autism spectrum disorder (ASD) often receive controversial treatments—interventions that are popular despite an absence of scientific or theoretical support. As many as one-third of all newly diagnosed children with ASD participate in such treatments (Levy, Mandell, Merhar, Ittenbach, & Pinto-Martin, 2003). Many others start soon after they begin conventional therapies such as behavioral or educational services, and some undergo multiple ones (Smith & Antolovich, 2000), which may continue into adolescence (Witwer & Lecavelier, 2005). The use of controversial treatments for children with ASD is a long-standing issue (Rimland, 1964), and the number of different treatments and their rate of use have grown over time (Levy & Hyman, 2003). The most common controversial treatments for children with ASD are sensory–motor therapies such as auditory integration training, bonding therapies such as Options (also called Son-Rise; Kaufman, 1976), and several forms of complementary and alternative medicine (CAM) interventions such as vitamin therapies and special diets. The proliferation of controversial treatments for children with ASD is probably due to many factors. Among them is that the precise etiology or etiologies of ASD remain unknown, fueling speculation and debate about possible causes and remedies (Levy & Hyman, 2005). Another is that ASD is a complex behavioral syndrome with many areas of need, each of which is potentially a focus of intervention (Lovaas & Smith, 2003). Moreover, caregivers may be eager to try a variety of treatments in search of a favorable outcome for their children. Their hopes may be high because, in some cases, the onset of ASD occurs after a period of apparently typical development (Luyster et al., 2005), and children may be free of obvious physical abnormalities and retain isolated areas of age-appropriate skills. Reports of children who improve markedly may add to caregivers’ hopes (e.g., Seroussi, 2000). In addition, caregivers often feel a sense of urgency, which may be fueled by the significant behavioral difficulties associated with ASD and the stress on caregiver–child relationships arising from a disorder characterized above all by impaired reciprocal social interaction (Bouma & Schweitzer, 1990; Hoppes & Harris, 1990).
+
+Caregivers may hear more about controversial treatments than about treatments with rigorous, scientific evidence for safety and efficacy. Controversial treatments attract far more media publicity than evidence-based treatments, which include behavioral and educational interventions (Lord et al., 2002) and psychopharmacological therapies (McCracken et al., 2002; Research Units on Pediatric Psychopharmacology Autism Network, 2005). Moreover, controversial treatments are frequently touted as cures, whereas evidence-based treatments yield only limited improvement, as they increase adaptive functioning for most children with ASD but do not eliminate the disorder. Some controversial treatments are relatively straightforward to implement; in contrast, evidence-based treatments are hard to obtain in many communities because they require supervision from highly trained professionals and may be expensive. Because all of these factors are likely to persist into the foreseeable future, practitioners and families can expect controversial treatments for children with ASD to remain popular. Therefore, to make informed decisions, it is essential to be able to distinguish controversial from established treatments and to be aware of the most common controversial treatments. To resolve controversies and advance the field, the scientific community must identify constructive ways to respond to advocates of controversial treatments, and practitioners and families must find ways to work together when controversial treatments are being considered for a child with ASD.
+
+DISTINGUISHING CONTROVERSIAL FROM ESTABLISHED TREATMENTS
+
+Standards of Evidence
+The only evidence for many controversial treatments consists of subjective information such as case reports, anecdotes, testimonials from parents or practitioners, and surveys. Reports that a child improved or that families gave high marks for a treatment in a survey are encouraging and may indicate that a treatment deserves further study. Unfortunately, this is not proof that the treatment is effective. Many other explanations are plausible. For example, additional interventions that the children were concurrently receiving, such as behavioral and educational services, may account for favorable outcomes. Furthermore, as children grow up, they may develop new abilities regardless of treatment. It is even possible that reported improvements can reflect parents’ or practitioners’ desires to see gains, rather than real progress. Scientific studies incorporate methodologies that make it possible to test whether a treatment is truly associated with improved outcomes. For instance, participants may be randomly assigned to two groups. One group receives the treatment, and the other is untreated; then the outcomes of the two groups are statistically compared. This design, called a randomized clinical trial (RCT), can offer the strongest test of whether a treatment is effective. The randomization maximizes the probability that children in the treatment group are similar to those in the no-treatment group prior to intervention. If the groups are similar prior to treatment but differ afterward, the posttreatment difference is likely to be attributable to the intervention. Optimally, an RCT includes a large number of children in each group (at least 20, often considerably more) so that the statistical analyses have adequate power to detect differences in outcome between groups. It may also include multiple treatment sites and practitioners to assess the consistency of results at different sites, with different personnel. Another appropriate research strategy is the use of single-case designs. These designs involve comparing a baseline phase, in which an individual receives no treatment, with one or more intervention phases in which treatment is provided to the individual. Data are collected continuously on the outcome measure. If scores on the outcome measure consistently improve during intervention relative to baseline, one may conclude that the treatment was effective for that individual. However, because the design involves only one individual, multiple studies by independent investigators are required to confirm the findings. A series of single-case studies may need to be followed by an RCT in order to test the treatment with a sufficiently large number of individuals (Smith et al., 2007).
+
+In both RCTs and single-case studies, standardized measures such as the Autism Diagnostic Interview—Revised (ADI-R; Rutter, LeCouteur, & Lord, 2003) and the Autism Diagnostic Observation Schedule–Generic (ADOS-G; Lord, Rutter, DiLavore, & Risi, 2001) should be used to confirm the diagnosis. In addition, investigators should show that the outcome measures are valid indicators of improvement, and the measures should reflect readily observable gains in functioning such as increased communication or reduced aggression, rather than vague constructs such as “greater focus” or “improved sense of self.” Moreover, to ensure unbiased data collection, individuals who are unaware of the purpose of the study or the children’s treatment histories should administer and score the measures. Assessments should also be conducted to ascertain whether treatment was delivered as intended, in keeping with a standard protocol or set of procedures. When possible, intervention should be administered in a double-blind, placebo-control design. In this approach, children and practitioners are unaware of whether the children are receiving treatment or a placebo. For example, in a study of a medication or vitamin therapy, the pills that contain the active ingredient can be made identical to placebo pills. Investigators can postpone telling the children and practitioners which pill the children received until the completion of the study. Although this strategy is not viable for most behavioral or educational studies because the interventions cannot be disguised, it is feasible for most CAM treatments.
+
+Table 9.1 presents a standard system for rating the evidence from scientific studies and shows that anecdotal reports are considered the weakest form of evidence, and favorable results from multiple studies that incorporate strong designs constitute the strongest evidence. When only anecdotal evidence is available, a treatment is considered to be essentially unproven; if studies were conducted, they could find that the treatment was helpful, harmful, or neither. Families and practitioners should consider such treatment experimental and should be very cautious about implementing it (or decide not to try it). However, when multiple, well-designed studies indicate that a treatment is effective, one can be confident that the treatment really is effective. Table 9.2 summarizes the criteria for a strong scientific study.
+
+| Grade | Criteria |
+|---|---|
+| I | Evidence from studies of strong design, with minor flaws at most and free from serious doubts about bias. Results are both clinically important and consistent. Results are free from concerns about generalizability. Studies with negative results have sufficiently large samples to have adequate statistical power. |
+| II | Evidence from studies of strong design, but there is some uncertainty owing to inconsistencies in findings, or concern about generalizability, bias, research design flaws, or sample size (for negative findings, again). OR, consistent evidence, but from studies of weaker design. |
+| III | Evidence from a limited number of studies of weaker design. Studies with strong design have not been done or are inconclusive. |
+| IV | Support solely from informed professional commentators based on clinical experience without substantiation from the published literature. |
+*Note. Adapted from Joint Commission Resources. (2000). Copyright 2000 by Joint Commission Resources. Adapted by permission.*
+
+**TABLE 9.2. Characteristics of Scientifically Sound Studies on Treatment**
+1. Participants are assigned randomly to groups (or use of single-subject experimental designs, with multiple replications by independent investigators).
+2. The study includes a large enough number of participants to support meaningful statistical analyses.
+3. Diagnosis is based on standardized measures.
+4. Validated outcome measures relating to improvements in functioning are collected.
+5. Measures are collected in an unbiased manner.
+6. Assessments are conducted to determine whether treatment adheres to a standard, predetermined set of procedures.
+7. When possible, the study is performed double-blind (participants and practitioners are unaware of whether the treatment or a placebo is being provided).
+
+Plausibility
+Although scientific evidence is the primary criterion for evaluating a treatment, the theoretical basis of the treatment is another important consideration. To be plausible, a treatment must address a problem known to be associated with ASD, and its mechanism for producing change must be consistent with principles of behavior or biology. For example, Floortime is an intervention that involves playfully obstructing children’s activities (Greenspan & Wieder, 1999). Although it has not been evaluated in studies with strong scientific designs (Greenspan & Wieder, 1997), it is viewed as a possibly effective intervention (National Research Council [NRC], 2001). Its purpose is to improve reciprocal social interactions, which are a major area of difficulty for children with ASD, via sustained back-and-forth communication during unstructured games. Playful obstruction is similar to a scientifically validated instructional method called incidental teaching (Hart & Risley, 1980), which is often a useful component of intervention programs for children with ASD. In contrast, “gentle teaching” is a therapy that is said to provide “unconditional and authentic valuing” of individuals with ASD in order to facilitate bonding or attachment to caregivers (McGee & Gonzales, 1990). However, most individuals with ASD already display attachment to caregivers (Sigman & Mundy, 1989), and it is unclear as to what unconditional and authentic valuing is or how it would be beneficial. Because it does not address a known problem in ASD and does not include interventions known to change behavior, gentle teaching is not usually regarded as a plausible treatment. These criteria are also applicable to biomedical interventions. For example, mood swings are a problem for some children with ASD. Psychotropic medication may be a reasonable intervention even if the medication has not been studied in children with ASD. Because of government regulations, all medications undergo extensive testing for safety and efficacy before becoming available in clinical practice. Psychotropic medications alter the function of neurotransmitters in the brain, and some have been shown to be effective in reducing mood swings associated with disorders other than ASD. For these reasons, such medications are potentially effective for children with ASD, though close monitoring by the prescribing physician is necessary. In contrast, although ASD is known to be a neurological disorder that affects brain development, many CAM interventions focus on entirely different parts of the body such as the gastrointestinal system. It is unclear whether individuals with ASD are at any greater risk than other individuals for such problems. It is also unknown whether interventions such as hormone injections or dietary changes are safe or effective in addressing these problems if they do exist, and whether improvement in gastrointestinal functioning is relevant to the underlying neurological difficulties in ASD. Thus, the theoretical basis for many CAM interventions is often questionable.
+
+Potential “Red Flags”
+Unfortunately, families and professionals often view particular treatments as having support from scientific studies and theories even when the consensus of the scientific community advises otherwise (Smith & Antolovich, 2000). Treatments may be pseudoscientific (described as proven and well-grounded in established theory yet lacking any such basis), and it may be difficult to distinguish between scientific and pseudoscientific approaches. However, one study identified a set of 10 “red flags” that may increase nonspecialists’ ability to spot pseudoscientific treatments (Finn, Bothe, & Bramlett, 2005; see Table 9.3):
+
+**TABLE 9.3. Red Flags for Identifying a Treatment as Pseudoscientific**
+1. Does the evidence in support of the treatment rely on personal experience and anecdotal accounts?
+2. Is the treatment approach disconnected from well-established scientific models or paradigms?
+3. Is the treatment unable to be tested or disproved?
+4. Does the treatment remain unchanged even in the face of contradictory evidence?
+5. Is the rationale for the treatment based only on confirming evidence, with disconfirmatory evidence ignored or minimized?
+6. Are the treatment claims incommensurate with the supporting evidence for those claims?
+7. Are the treatment claims unsupported by evidence that has undergone critical scrutiny?
+8. Is the treatment described by terms that appear to be scientific but upon further examination are determined not to be?
+9. Is the treatment based on grandiose claims or poorly described outcomes?
+10. Is the treatment claimed to make sense only within a vaguely described holistic framework?
+*Note. Adapted from Finn, Bothe, and Bramlett (2005). Copyright 2005 by the American Speech–Language–Hearing Association. Adapted by permission.*
+
+1. Does the evidence in support of the treatment rely on personal experience and anecdotal accounts? As discussed, anecdotes may suggest that scientific testing of a treatment would be worthwhile but in themselves are weak evidence that the treatment is effective.
+2. Is the treatment approach disconnected from well-established scientific models or paradigms? As noted, a treatment should address problems known to be associated with ASD and should be consistent with principles of behavior and biology.
+3. Is the treatment unable to be tested or disproved? To qualify as scientific, assertions about treatment effects must be stated in such a way that direct observation and experiments can either confirm or falsify them. Otherwise, the credibility of the treatment depends solely on the authority of its developer. However, assertions about controversial treatments are often untestable and therefore pseudoscientific. For example, the developer of one controversial treatment contended that any intervention for children with ASD would be impossible to study because treatment “cannot observe the rigors of a ‘scientific’ experiment since it must, in its course, pursue the vagarities of life which are nothing if not unpredictable” (Bettelheim, 1967, p. 6). Proponents of another controversial treatment maintained that negative research findings could never be used as evidence against the intervention because the presence of an objective observer (as required for research) disrupted the therapeutic relationship so severely that treatment gains were lost (Biklen & Cardinal, 1997).
+
+4. Does the treatment remain unchanged even in the face of contradictory evidence? Established treatments such as behavioral interventions continually evolve as a result of new research findings. However, many controversial treatments originated many years ago and are still implemented in essentially their original form, without revisions based on scientific advances (see, e.g., Kaufman, 1976).
+5. Is the rationale for the treatment based only on confirming evidence, with disconfirmatory evidence ignored or minimized? Scientific evaluation of a treatment requires consideration of all evidence from relevant well-designed studies, including both positive and negative results. However, advocates of controversial treatments sometimes focus only on supporting evidence. For example, proponents of vitamin therapies sometimes cite a large number of uncontrolled studies that appear to support these therapies but do not cite relevant RCTs, all of which so far indicate that the therapies are not effective (see, e.g., Rimland, 2000).
+6. Are the treatment claims incommensurate with the supporting evidence for those claims? Advocates of a controversial treatment may recommend the treatment for children with ASD solely on the basis of anecdotal information. They also may divert attention away from this weak evidence by criticizing other treatments, arguing that skepticism about their treatment reflects opposition from a narrow-minded establishment (Rimland, 1992) or insisting that scientific tests of the treatment are superfluous (Biklen & Cardinal, 1997).
+7. Are treatment claims unsupported by evidence that has undergone critical scrutiny? Before publication in a scholarly journal, reports of scientific studies undergo careful peer review. The report is read by several experts, whose identities are usually withheld from the authors of the report so that they can give honest feedback. The experts critique the adequacy of the research methodology, soundness of the conclusions, and contribution to scientific knowledge. Based on the experts’ critique, an editor makes a recommendation for or against publishing the report. Although not a perfect process, peer review increases the likelihood that published reports are reliable and useful sources of information. Many controversial treatments, however, do not receive this kind of scrutiny and are instead publicized through press releases to the popular media, websites, advertisements, workshops, and the like.
+
+8. Is the treatment described by terms that appear to be scientific but upon further examination are found not to be scientific at all? Controversial treatments often use scientific-sounding jargon to describe ideas that lack a scientific foundation. For example, the developer of sensory integration therapy (SIT) asserted, “Sensations [from activities such as riding a scooter board] and the resulting movements leave memories stored in his brain, and so the child gradually makes his body percept more accurate” (Ayres, 1979, p. 143). However, no direct evidence of changes in the brain or behavior is provided. Thus, despite the technical terms, the reported benefits are merely the subjective impressions of one practitioner, rather than the results of scientific study.
+9. Is the treatment based on grandiose claims or poorly described outcomes? Many controversial treatments are said to produce a “cure,” “miracle,” “breakthrough,” “transformation,” or “revolution.” Such unabashed self-promotion should be a warning that marketing rather than science is the main impetus for the treatment. Outcomes for other treatments are described in fuzzy terms. For example, in addition to improving “body percept,” SIT is said to help children pull their lives together, develop sensory maps, and improve postural and equilibrium responses (Ayres, 1979, pp. 143–147). Because these outcomes are so nebulous, it is impossible to test whether the intervention achieves them.
+10. Is the treatment claimed to make sense only within a vaguely described holistic framework? Controversial treatments are often portrayed as “natural,” “organic,” “purifying,” or “cleansing.” They may also be depicted as designed to help the “whole person” through processes such as “unconditional and authentic valuing” (as in gentle teaching). The use of such feel-good words cannot substitute for a clear, concrete explanation of how the treatment works.
+
+COMMON CONTROVERSIAL TREATMENTS FOR ASD
+
+Sensory–Motor Therapies
+Children with ASD often react incongruously to sensory input. They may be so unresponsive when their names are called that caregivers wonder whether they are deaf, yet they may cover their ears and appear pained in response to other sounds such as noises made by household appliances (Kanner, 1943). Many practitioners infer that these reactions are a sign of a sensory dysfunction that causes children with ASD to be either under-aroused or overaroused by everyday sounds, sights, and other environmental events. Many also suggest that children with ASD have a motor apraxia—difficulty in producing an adaptive response to sensory input despite having the desire and physical ability to do so. It remains unknown whether these hypotheses are correct, as research has yielded conflicting findings regarding the presence or absence of sensory dysfunction and apraxia in children with ASD (Rogers & Ozonoff, 2005). It is therefore unclear whether or how to intervene for these proposed areas of difficulty. Nevertheless, many children with ASD receive sensory–motor treatments.
+
+Sensory Integration Therapy
+Sensory integration therapy (SIT) is designed to address sensory dysfunction through activities that provide vestibular, proprioceptive, or tactile senations (Ayres, 1972, 1979). Vestibular activities focus on the movement of the body through space and include swinging, rolling, jumping on a trampoline, and riding on scooter boards. Proprioceptive activities emphasize stimulating the muscles and joints and may consist of “smooshing” the child between gymnasium pads or pillows to provide “deep pressure” or providing “joint compression” by repeatedly tightening the individual’s joints at the wrist or elbow. Tactile activities pertain to the child’s responses to being touched; examples include brushing the child’s body and providing textured toys for the child to use during play. The application of a “sensory diet” is a related clinical practice in which practitioners develop individualized plans to meet the presumed sensory needs of the child with ASD. Such a plan may include a schedule for having children play gross motor games, wear weighted vests or wrist bands, put on a body sock, brush their gums and massage their faces, and modify their environment (e.g., adjusting the lighting) in order to improve or alter arousal states and affect (Alhage-Kientz, 1996). SIT practitioners are usually occupational therapists (OTs). These practitioners typically conduct 30- to 60-minute sessions one to three times per week and often direct parents and paraprofessionals such as classroom aides to carry out the intervention at other times throughout the day (Bundy & Murray, 2002). Most OTs view SIT as a standard part of treatment for children with ASD (Watling, Dietz, Kanny, & McLaughlin, 1999), and SIT takes place in a variety of settings, including many public schools, residential placements, and independent agencies (Smith, Mruzek, & Mozingo, 2005). Four published reports contained objective data on SIT for children with autism: one case study (Ray, King, & Grandin, 1988), two uncontrolled studies with small samples and no comparison groups (Case-Smith & Bryan, 1999; Linderman & Stewart, 1998), and one study with a larger sample that failed to demonstrate gains in speech following participation in sensory activities (Reilly, Nelson, & Bundy, 1984). Dawson and Watling (2000) commented, “There exist so few studies that conclusions cannot be drawn” (p. 419).
+
+Auditory Integration Training
+Auditory integration training (AIT; Berard, 1993) is based on the view that the hypersensitive hearing displayed by some children with ASD causes them to avoid social interactions and tune out what others say. AIT practitioners are human service professionals who complete a training workshop and obtain certification. The Tomatis and Berard methods are the most influential forms of AIT. Both begin with an audiogram (observations by an AIT practitioner) to determine the frequencies at which a child’s hearing appears to be too sensitive. Children then listen to music played through a device that filters out the threshold frequencies identified by the audiogram. In the Tomatis method, children may also speak into a microphone as their own filtered speech is played back. This method typically involves 60–90 hours of intervention in sessions lasting 1–3 hours. The Berard method involves a total of 10 hours of intervention over a 2-week period. Several small RCTs of AIT have obtained mixed results, with some studies showing benefits and others failing to do so (Sinha, Silove, Wheeler, & Williams, 2005). Additional studies are needed to evaluate AIT more conclusively.
+
+Facilitated Communication
+Facilitated communication (FC; Biklen, 1993) derives from the hypothesis that individuals with ASD have a motor apraxia that prevents them from expressing themselves despite a sophisticated understanding of spoken and written language. To overcome this conjectured problem, trained facilitators (professionals or nonprofessionals who complete a workshop on the treatment) hold a person’s hands, wrists, or arms to spell messages on a keyboard or a board with printed letters. FC practitioners assert that this intervention suddenly and dramatically increases appropriate language displayed by individuals with ASD. Investigators have evaluated this assertion in numerous studies by testing whether the facilitator or the individual with ASD produced the communications made during FC. For example, in some evaluations, the facilitators and children were simultaneously but separately asked questions. Sometimes the questions were the same for both the facilitators and the children; other times, they differed. When the questions were the same, the child’s answers were often correct; but when the questions were different, most answers were in response to the facilitator’s questions, not the child’s. This evidence, replicated across several hundred children with ASD, shows that the facilitators rather than the individuals with ASD control the communication and that FC does not improve language skills (Mostert, 2001). Therefore, FC is an inappropriate intervention for individuals with ASD.
+
+Rapid Prompting Method
+In the rapid prompting method (RPM), practitioners attempt to compensate for the hypothesized sensory overload and apraxia in children with ASD by continually speaking and requesting responses so that the children stay attentive (Mukhopadhyay, 2003). To encourage successful responding, they initially focus on having children observe correct responses. As the children progress, practitioners begin to ask children to point to correct responses. Subsequently, they teach children to spell answers on a keyboard or write them down, often attaching a rubber band to the children’s hands to help them hold the pen or pencil. No scientific studies have evaluated RPM.
+
+Vision Therapy
+Many children with ASD have poor eye contact. Some also flap their hands or fingers in front of their eyes repeatedly, look at objects out of the corners of their eyes, and display unusually intense interest in visual stimuli such as spinning objects. Vision therapy is intended to address these problems through the use of tinted eyeglasses, prisms, or eye exercises (Kaplan, Edelson, & Seip, 1998). Tinted eyeglasses, such as Irlen lenses, are thought to reduce “perceptual stress” by filtering out certain colors, decreasing glare, or dimming the light. Prisms are used to displace children’s field of vision to the left, right, up, or down. Eye exercises emphasize relaxing the eyes or activities such as following a series of blinking lights, gazing at a string of objects, or working on hand–eye coordination. There are no studies on vision therapy for children with ASD. Studies of other populations such as children with learning disabilities indicate that it is likely to be ineffective (Rawstron, Burley, & Elder, 2005).
+
+Bonding Therapies
+Although impaired reciprocal social interaction is a central feature of ASD, most children with ASD form attachments to their caregivers. Like typically developing children, children with ASD may become distressed upon separation, are eager to see caregivers when reunited, and stay nearer to caregivers than to unfamiliar adults (Sigman & Mundy, 1989). Nevertheless, a number of interventions are intended to facilitate attachment or bonding between individuals with ASD and their caregivers. In holding therapy (Tinbergen & Tinbergen, 1983; Welch, 1987), the mother forcibly holds the child close to her so as to cause “the autistic defense . . . to crumble” (Welch, 1987, p. 48). Options (also called Son-Rise) offers individualized, loving attention to a child in a residential setting for most of the child’s waking hours (Kaufman, 1976). As described earlier, “gentle teaching” focuses on providing unconditional support and encouragement to individuals with ASD (McGee & Gonzales, 1990). None of these therapies have been evaluated in scientific studies on children with ASD, although one study suggests that gentle teaching may be nonbeneficial for children with other developmental disabilities (Mudford, 1995). Given that attachment difficulties are not characteristic of most children with ASD, the theoretical rationale for bonding therapies is suspect.
+
+CAM Interventions
+
+Diets
+Many children with ASD have idiosyncratic eating habits: Some are very picky about what they eat, and others crave large amounts of certain foods. A few professionals suggest that these behaviors reflect a serious underlying problem, namely, a difficulty in tolerating certain substances found in various foods. They argue that eliminating these substances from children’s diets may alleviate physical discomfort, which may lead to an improvement in their behavior (Reiten, 1987). The most common special diet for children with ASD is the gluten-free–casein-free (GfCf) diet. Gluten is an elastic protein in wheat that gives cohesiveness to dough. Casein is a protein in milk, cheese, and other dairy products. Numerous parents and professionals aver that the GfCf diet cures a few people with ASD and helps many others. The diet reportedly improves communication, social interaction, and sleep patterns while reducing autistic behaviors and digestive problems such as diarrhea. These benefits are said to occur rapidly, often within a few days of starting the diet (Seroussi, 2000). Supporters of the GfCf diet propose that people with autism have a metabolic disorder that causes them to break down gluten and casein into opioids, which are peptides produced by the body and found in drugs such as morphine (Shattock, Kennedy, Rowell, & Berney, 1990). They also suggest that people with autism have leaky guts, which allow some of the opioids to escape from the digestive system and circulate to other parts of the body, including the brain (Horvath, Papadimitriou, Rabsztyn, Drachenberg, & Tildon, 1999). According to the theory, these problems create an addiction to foods that contain gluten and casein, as evidenced by the strong cravings that people with autism often have for such foods. The cravings are thought to be symptomatic of pervasive toxic effects in the brain, thus resulting in autism. The intended purpose of the GfCf diet is to reverse the damage by detoxifying the brain.
+
+Although some investigators have presented evidence that people with autism overproduce opioids and have leaky guts (Reichelt, Knivsberg, Nodland, & Lind, 1994), other investigators have failed to replicate these findings (Williams & Marshall, 1992). Only two small RCTs have evaluated the GfCf diet. Knivsberg, Reichelt, Hoien, and Nodland (2002) found that although the diet did not significantly improve cognitive, language, or motor skills, it may have reduced autistic behaviors such as repetitive statements. Elder et al. (2006) reported that the diet did not produce significant changes for children with autism in their study. Additional study of the theoretical basis and efficacy of the GfCf diet is an important area for research (Millward, Ferriter, Calver, & Connell-Jones, 2004). Because the removal of gluten and casein may compromise a child’s nutritional intake, dietary counseling is recommended for families who place their children on the diet (Levy & Hyman, 2003).
+
+Vitamin Therapies
+A few investigators assert that some children with autism require much higher doses of certain nutrients than can be obtained from any traditional diet (Rimland, 1987). According to these investigators, children with autism have a genetic or acquired medical disorder (as yet unspecified) that increases their need for specific nutrients. Research based on this hypothesis has centered on the use of a combination of vitamin B6 (pyridoxine) and magnesium. B6 is a chemical whose primary function is to aid in protein digestion; magnesium is a mineral that helps build bones, maintain nerve and muscle cells, and enhance the function of various enzymes in the body. Three small-scale RCTs indicated that B6 with magnesium is ineffective in changing behavior (Findling et al., 1997; Kuriyama et al., 2002; Tolbert, Haigler, Waits, & Dennis, 1993), but further study may be warranted (Nye & Brice, 2005). Other common vitamin therapies include (1) dimethylglycine (DMG), which assists in the metabolism of amino acids and other substances, (2) vitamin A (often in tablets of fish oil or omega-3 fatty acids), (3) vitamin B12 (folic acid or folate), and (4) vitamin C. The theoretical basis for these vitamin therapies is unclear, and none have been evaluated in well-designed studies of children with ASD.
+
+Treatment of Infections
+Some researchers contend that children with ASD may have impaired immune systems (see Lawler, Croen, Grether, & van de Water, 2004, for a review), though evidence for such an impairment remains inconclusive. One small study indicated that an antibiotic, vancolycin, may increase the amount of communication initiated by children with ASD (Sandler et al., 2000), but until this finding is replicated, it is premature to recommend antibiotic treatment. Antifungal or antiyeast medications such as mycostatin (Nystatin) or fluconazole (Diflucan) are sometimes also prescribed. However, well-designed studies have not been conducted to examine the effectiveness of these medications in changing the behavior of children with ASD (Levy & Hyman, 2005). Moreover, the diagnostic tests used to identify fungal or yeast infections have not been empirically validated and must be viewed with skepticism. Intravenous injections of immunoglobulin treatments (IV-Ig) have been proposed as a way to improve immune functioning but have also not been evaluated in well-designed studies (Levy & Hyman, 2005).
+
+Immunizations and Nonvaccination
+Much concern has arisen among the general public that vaccines cause autism (Kennedy, 2005), and this concern has significant public health implications, as it has apparently contributed to a reduction in vaccination rates in many countries (Fleck, 2003). Initially, it was suggested that some vaccines, particularly diphtheria–tetanus–pertusis (DTaP) and measles–mumps–rubella (MMR), may trigger out-of-control infections or immune responses, leading to brain damage and the onset of autism (Coulter, 1990). This view was largely set aside and replaced with a new hypothesis, that the MMR vaccine may cause bowel inflammation, hindering the absorption of essential vitamins and nutrients (Wakefield et al., 1998). The Wakefield et al. hypothesis generated enormous publicity and led to numerous studies evaluating the putative links among the MMR vaccine, bowel inflammation, and ASD. A review of 31 well-designed studies found no evidence for the proposed links (Demicheli, Jefferson, Rivetti, & Price, 2005). For instance, a Japanese city stopped administering the MMR vaccine in 1993, but the prevalence of ASD did not decrease after the vaccine’s removal (Honda, Shimzu, & Rutter, 2005). Further weakening the MMR hypothesis, 10 of the 13 authors of the Wakefield et al. (1998) report retracted their initial conclusion that findings in the report showed a possible connection between MMR and ASD (Murch et al., 2004). Thus, many studies have failed to find an association between the MMR vaccine and ASD (Demicheli et al., 2005). As evidence began to accumulate against a link between the MMR vaccine and ASD, another hypothesized connection between vaccines and ASD was advanced: Bernard, Enayati, Redwood, Roger, and Binstock (2000) and subsequent writers proposed that vaccines containing thimerosal, which is a mercury compound used as a preservative, may cause autism. In 1999, the U.S. Food and Drug Administration (FDA) mandated the removal of this substance from all childhood vaccines, including DTaP, haemophilus influenza type b (Hib), and hepatitis B. (The MMR vaccine never contained thimerosal; some influenza vaccines continue to include trace amounts.) This action is sometimes cited as an indication that the FDA had evidence of a link between thimerosal and ASD or other conditions (Kennedy, 2005). However, studies indicate that thimerosal is not associated with ASD (Institute of Medicine, 2004). Doses of thimerosal in vaccines are excreted quickly and appear to pose little risk (Pichichero, Cernichiari, Lopreiato, & Treanor, 2002). More generally, these studies confirm that vaccines are safe and that withholding them poses much greater risk than administering them to children with or without ASD.
+
+Secretin
+Secretin is a hormone that is secreted by the lining of the duodenum (part of the small intestine) and assists with food digestion. In 1998, news stories publicized a report that intravenous injections of secretin led to symptom improvement in three children with ASD (Horvath et al., 1998). Some news stories also described a child whose ASD was said to be cured by secretin. Subsequently, secretin attracted a great deal of interest from families and practitioners, and many researchers began to study it. Investigators discovered that secretin receptors resided in both the gut and the brain and that secretin can cross the blood–brain barrier, indicating that it could potentially influence brain function (Levy & Hyman, 2005). However, an authoritative review identified 14 RCTs of secretin, all of which found secretin to be ineffective, and concluded, “There is no evidence that single or multiple dose intravenous secretin is effective and as such it should not currently be recommended or administered as a treatment for autism” (Williams, Wray, & Wheeler, 2005).
+
+Chelation
+Chelation therapy involves administering a substance that binds to metal ions so that the metal can be excreted from the body. The substance, called the chelating agent, can be administered intravenously, intramuscularly, orally, or rectally. With the increased interest in the (unproven) hypothesis that ASD is caused by exposure to mercury, chelation has become a common intervention for children with ASD. Chelating agents that are used for children with ASD include disodium versante (Na2-EDTA), calcium disodium versante (CaNa2-EDTA), dimercaptosuccinic acid (DMSA), sodium dimercaptopropanesulfonate (DMPS), and thiamine tetrahydrofurfyl disulfide (TTFD). However, none of these agents cross the blood–brain barrier in significant amounts; thus, their theoretical basis is dubious, as there is no mechanism by which any chelating agent could reverse the brain damage associated with ASD (Levy & Hyman, 2005). Only Na2-EDTA and DMSA have been approved by the FDA to treat acute poisoning from heavy metals, and Na2-EDTA is not effective in removing mercury from the body. These and other chelating agents have significant risks of side effects. For example, in August 2005, a 5-year-old boy died as a result of chelation therapy with intravenous Na2-EDTA (Kane, 2006). Thus, although no RCTs have evaluated any form of chelation therapy for children with ASD, and although other chelating agents may not be as dangerous as Na2-EDTA, this therapeutic approach appears implausible and unacceptably risky. It should not be used as a treatment for ASD.
+
+Discussion
+Table 9.4 summarizes common controversial therapies and their intended outcomes. The preceding sections reveal that several of these therapies have undergone extensive evaluation in well-controlled studies (providing Grade I evidence, according to the criteria in Table 9.1) and have clearly been refuted: facilitated communication, secretin, and nonvaccination. Therefore, a strong recommendation can be made against implementing these treatments. Chelation, although not evaluated in well-controlled studies, has a faulty theoretical basis and an intolerable level of risk; as such, it is also an intervention to avoid. The remaining controversial therapies have received little or no scientific testing, leaving only Grade III or Grade IV evidence, as outlined in Table 9.1. These treatments have unknown effects, and families and practitioners should be cautious about them (either deciding not to implement them or monitoring them carefully). Some treatments, such as bonding therapies, are based on obsolete theories about ASD, and interventions such as some sensory–motor therapies, diets, and vitamins are based on unproven theories that may merit further research.
+
+| Intervention Category/Intervention | Example of method                                                                                                                            | Intended outcome                                                                      |
+|------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|
+| **Sensory–motor therapies**        |                                                                                                                                              |                                                                                       |
+| Sensory integration therapy        | Repeated exposure to vestibular, proprioceptive, and tactile activities                                                                        | Organize sensory input and reduce anxiety associated with hypersensitivity to sensations |
+| Auditory integration therapy       | Headphones to listen to filtered sound frequencies                                                                                           | Reduce sensitivity to sounds, thereby increasing social interaction and attentiveness   |
+| Facilitated communication          | Physical support given by placing a practitioner’s hand on the child’s arm or hand; with support, child expresses ideas via picture board, typewriter, or computer | Overcome motor apraxia to enable communication                                      |
+| Rapid prompting method             | Continuous verbal requesting in order to maintain attending behavior; children initially observe correct responses to requests, then are required to emit progressively more active responses | Compensate for sensory overload and apraxia to improve communication                |
+| Vision therapy                     | Use of tinted eyeglasses, prism lenses, or eye exercises                                                                                       | Improve eye contact and diminish repetitive behaviors that involve visual stimuli such as spinning objects |
+| **Bonding therapies**              |                                                                                                                                              |                                                                                       |
+| Options or Son-Rise, holding therapy, gentle teaching | Giving unconditional loving attention to the child                                                                               | Increase attachment to familiar adults                                                  |
+| **CAM interventions**              |                                                                                                                                              |                                                                                       |
+| Diets                              | Removal of gluten and casein from the diet                                                                                                     | Heal leaky gut and detoxify the brain of opiods                                        |
+| Vitamin therapies                  | Vitamin or nutritional supplements: vitamin B6 + magnesium; DMG; vitamin A; vitamin B12 (folate); vitamin C                                      | Alter neurotransmitter levels to produce global improvements in behavior              |
+| Treatment of infections            | Antibiotic or antifungal treatments; IV-Ig                                                                                                     | Eliminate infectious disease, improve immune functioning                              |
+| **CAM interventions (cont.)**      |                                                                                                                                              |                                                                                       |
+| Nonvaccination                     | Withholding vaccines such as MMR                                                                                                               | Avoid bowel inflammation or metal toxicity to prevent ASD                        |
+| Secretin                           | Intravenous injection                                                                                                                          | Alter activity of secretin receptors in gut and brain                            |
+| Chelation                          | Oral or intravenous administration of a chelating agent such as DMSA or EDTA                                                                 | Remove heavy metals such as mercury from the body to restore brain functioning |
+
+IMPLICATIONS FOR THE SCIENTIFIC COMMUNITY
+Ideally, the scientific community could settle controversies about treatments by providing evidence on their effectiveness or lack thereof. The reality, however, is somewhat more complicated. As detailed in the preceding section, controversial treatments are many and varied, and new ones continually emerge. Therefore, it is not feasible to evaluate all controversial treatments adequately. Even when a treatment has been studied extensively and found to be ineffective, some families and practitioners remain steadfast in their belief that the treatment is beneficial. For example, FC, secretin, and nonvaccination still have many ardent supporters in spite of devastating evidence against them. One study revealed that, despite being informed of the negative results from a secretin study in which they participated, 69% of families remained interested in receiving secretin as a treatment for their children with ASD (Sandler et al., 1999). This enduring support shows that the hope for effective interventions and the appeal of pseudoscientific claims may be so strong that they override any amount of scientific data that researchers may produce. Nevertheless, the scientific community can play a constructive role in responding to controversial treatments. Research that pertains to the theoretical basis of the treatments may be especially useful. For example, until the 1980s, many bonding therapies were proposed for children with ASD (discussed by Smith, 1993). However, with the increase in research during the 1980s regarding social deficits displayed by children with ASD, interest in bonding therapies may have waned as it became apparent that bonding was not a primary concern for most of these children. In contrast, unusual sensory–motor behaviors, which are also a central feature of ASD, have generated much less research, and no generally accepted scientific theory accounts for these behaviors (Rogers & Ozonoff, 2005). Perhaps as a result, sensory–motor and dietary interventions continue to proliferate. Other controversial treatments, particularly CAM interventions, are based in part on the belief that there is an epidemic of ASD and that recent changes in children’s environments, such as the introduction of new vaccines or exposure to toxic substances, must be responsible. Although estimates of the prevalence of ASD have certainly increased since the 1980s (Fombonne, 2003), it remains unclear whether this increase reflects an actual rise in the prevalence in ASD or merely improved detection and broadened diagnostic criteria for the disorder. Extensive research is now under way to resolve this issue, and such investigations may influence the extent to which the belief in an autism epidemic continues to drive the development of new CAM interventions.
+
+Scientific evaluations of treatments, as well as position statements by professional organizations based on these evaluations, can also have an effect on controversial treatments, albeit a limited one. For example, a search of the database Lexis/Nexis was conducted for reports on several controversial treatments in the popular media (newspapers and magazines, television, and radio); these reports were rated as having a positive, neutral, or negative stance toward a particular treatment. Figure 9.1 shows reports on FC. This intervention was virtually unknown prior to 1990 but suddenly became a topic of many favorable media reports in the early 1990s. Reports often described miraculous improvements in the communications made by children with ASD. From the start, scientists expressed skepticism about the validity of FC and responded quickly by conducting single-case studies of FC involving many children with autism. By 1994, studies had unequivocally shown FC to be ineffective (Green & Shane, 1994), and professional organizations presented position statements advising against its use (American Psychological Association, 1994). As shown in Figure 9.1, positive media references to FC sharply decreased at that time, suggesting that the studies and position statements may have created doubts about the intervention. However, the reports remained mostly favorable and rose in frequency again in 2004, perhaps because a film on FC was nominated for an Academy Award that year. Thus, evidence from scientific evaluations did not put an end to public interest in FC, but did appear to have an impact on media coverage (perhaps only temporarily). Figure 9.2 displays media reports on secretin, which attracted a flurry of publicity in 1998 when an article described favorable outcomes in three children with ASD. Within weeks, the National Institutes of Health (1998) issued a call for the scientific evaluation of secretin, and scientists responded with three RCTs published in 1999, all finding secretin to be ineffective. At that time, as revealed in Figure 9.2, skeptical media reports on secretin began to surface, but positive reports also continued to flourish. As with FC, negative research findings appear to have dampened enthusiasm about this treatment, though they did not eliminate it.
+FIGURE 9.1. References to facilitated communication in the popular media (1991–2005).
+FIGURE 9.2. References to secretin in the popular media (1997–2005).
+
+The influence of research findings on public opinion may be enhanced by illustrating them with case examples. For example, media reports on FC supplemented discussions of research findings with demonstrations on television that the facilitators rather than the children with ASD were controlling the communication (Palfreman, 1993). As another example, the media report of a death resulting from chelation in 2005 was followed by a number of other media reports cautioning against this intervention, as shown in Figure 9.3. Many of these reports cited scientific evidence for the risks and limitations of chelation, in addition to commenting on the tragic death. In sum, although not a perfect solution, research on characteristics of ASD, scientific evaluation of controversial treatments, and position statements by professional organizations can influence public interest in a treatment, particularly if presented in an accessible format (e.g., with case reports).
+FIGURE 9.3. References to chelation therapy in the popular media (1995–2005).
+
+IMPLICATIONS FOR CLINICAL PRACTICE
+Given the prevalence and durability of controversial treatments for ASD, practitioners who assess and treat children with ASD can neither ignore nor dismiss such treatments. Instead, they must anticipate that the treatments will be appealing to many families. To be in a position to help families make informed decisions, practitioners can encourage families to discuss controversial treatments by asking direct, nonjudgmental questions about treatments that families have tried or considered. Practitioners can also show an awareness of and compassion for the many understandable motives that families may have for trying unproven or even disproven approaches (Committee on Children with Disabilities, 2001). Open discussion on controversial treatments creates an opportunity for practitioners to present information on how to distinguish between scientific and pseudoscientific treatments, and to review what is known and unknown from relevant research. Research supports clear recommendations against some treatments, notably FC, secretin, and nonvaccination. It also provides reasons to be skeptical about other treatments such as AIT. However, because of the large number of controversial treatments available, practitioners may not always be familiar with a particular treatment or have up-to-date knowledge of the research on that treatment. Under this circumstance, practitioners can express a willingness to learn about the treatment, review information that families bring, and describe criteria they would use to gauge whether the treatment appears promising. Finally, practitioners can advocate for and, if resources are available, assist with an objective evaluation of a controversial treatment so that families can assess the treatment efficacy themselves. Guidelines for conducting this evaluation include the following (Hyman & Levy, 2000): First, make only one treatment change at a time and hold other treatments constant. Second, identify specific target behaviors to be addressed by the treatment, and use objective measures to obtain a baseline of this behavior prior to treatment. Finally, monitor ongoing changes in the target behavior with objective measures obtained by raters who are blind to the treatment (e.g., a teacher who is unaware of changes in vitamin consumption rate).
+
+In some settings, such as schools, it is often possible to go a step further and conduct single-case experiments in which a child with ASD serves as his or her own control (Smith et al., 2005). The multielement design (also called alternating treatment design) may be especially useful because it yields quick results. The design involves implementing a treatment on alternate days or in alternate sessions. During the other days or sessions, a baseline is in effect (i.e., no intervention is provided) or another treatment is provided. Kay and Vyse (2005) used this approach to evaluate the effects of prism glasses on appropriate walking by an 8-year-old boy with ASD. Data are shown in Figure 9.4 and reveal that prism glasses interfered with appropriate walking, rather than helping. As a result, the intervention was discontinued. A limitation of the alternating treatment design is that it is appropriate only when treatment effects are observable within a single day or session. Thus, if an intervention is said to require multiple days or weeks to change the target behavior, other designs must be considered. A useful example is the reversal design, in which a baseline phase is followed by a treatment phase, followed by a return to the baseline phase, and so on. Each phase lasts several sessions, days, or weeks. Figure 9.5 illustrates the use of a reversal design to evaluate the effects of an SIT intervention (brushing) for a 4-year-old boy with ASD who engaged in tantrums (screaming, crying, throwing objects, falling to the floor). During the baseline phases, Robert played with favorite toys or briefly watched videos when he had breaks in learning activities. During treatment phases, Robert’s mother performed the brushing at break times. Instructors, who were unaware of whether Robert was in the baseline or treatment condition, collected frequency data on tantrums during teaching sessions. Figure 9.5 shows that SIT failed to reduce this behavior (and possibly increased it). After the findings were discussed with the family, a decision was made to discontinue SIT.
+FIGURE 9.4. Alternating treatment design to evaluate the effect of prism lenses on appropriate walking by an 8-year-old boy with ASD. From Kay and Vyse (2005). Copyright 2005. Reprinted by permission of Lawrence Erlbaum Associates, Inc., a division of Taylor & Francis Group.
+FIGURE 9.5. Reversal design to evaluate the effect of brushing on a 4-year-old boy with ASD. From Smith, Mruzek, and Mozingo (2005). Copyright 2005. Reprinted by permission of Lawrence Erlbaum Associates, Inc., a division of Taylor & Francis Group.
+
+CONCLUDING COMMENTS
+Beyond acknowledging the many reasons for the ubiquity of controversial treatments in ASD and considering how to confront them, an important next step is to increase support for developing treatments that scientists view as promising. Until the late 1990s, little funding was available for research on ASD. The funds that did become available were devoted mainly to studies of the characteristics and causes of ASD, rather than to treatment. More recently, however, private foundations have begun to sponsor pilot studies on innovative treatments, and federal agencies have formed multisite networks to carry out large-scale clinical trials evaluating treatments that have shown promise in preliminary investigations (Vitiello & Wagner, 2004). These initiatives are encouraging. Although treatment studies often take years to complete, and although they are not infallible, they ultimately provide the firmest foundation for enabling families and practitioners to choose from an array of appropriate treatment options and for improving outcomes achieved by children with ASD.

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+Supporting Families
+
+The evaluation of a young child suspected of having autism spectrum disorder (ASD) effectively starts with the initial contact with the family. Although the primary goal of the clinicians is to gain a thorough understanding of the child’s strengths and vulnerabilities, exploration of many other factors involved in the assessment process is essential for advancing positive outcomes for children and families living with ASD. This chapter summarizes a wide range of issues that need to be considered by clinicians and service providers that can potentially maximize adherence to this overarching mission. Such issues should both guide and anchor clinical efforts before, during, and after the evaluation.
+
+SUPPORTING FAMILIES THROUGH THE DIAGNOSTIC PROCESS
+
+The Impact of Parental Early Concerns and Experiences
+
+Many families that have young children with ASD bring a history of experience with professionals, family, and friends that often impacts their regard for the diagnostic assessment. More specifically, most of the parents have a sense that something is just not right from very early on (Howlin & Moore, 1997; Chawarska & Volkmar, 2005), sometimes as early as a child’s birth (Wetherby, Prizant, & Schuler, 2000). In ideal circumstances, these concerns are shared with family, friends, and professionals who are sensitive and supportive and can assist the family in securing a comprehensive evaluation and warranted services. Such support does not necessarily eliminate the anxiety that families experience in anticipation of an evaluation that may confirm their worst fear for their child. In hindsight, however, families often express gratitude for such early assistance, as it serves as a springboard in the process of implementing early intervention services. Although the severity of their child’s difficulties may bring sadness into their lives, parents who experience appropriate early support, especially from professionals, are able to act with greater confidence on their child’s behalf. Careful attention to parental concerns may prevent a buildup of feelings of anger or guilt that parents may experience because of a prolonged lag between the onset of their concerns, the validation of their feelings by professionals, and the beginning of treatment. Unfortunately, however, early parental concerns may be met with a dismissive attitude by professionals and family members. Comments such as “You’re just an anxious parent” or “Boys talk later than girls” are not unusual. Although such comments may be heartfelt and well intentioned, they can potentially distract parents and derail efforts to pursue greater understanding of their child’s challenges and may delay implementation of treatment. This is especially true when the comments come from professionals (DeGiacomo & Fombonne, 1998; Howlin & Asgharian 1999; Howlin & Moore, 1997). These comments may serve the parents’ need to believe that all is well, yet the haunting sense that something is “not quite right” never fully abates. Usually, by the time a child reaches the age of 2 or 3 years, professionals and other caretakers begin to share the parents’ concerns. Although this is likely to happen earlier, as in the case of children who exhibit marked speech and/or cognitive delays, by the time the child is about 3 years old the concerns are likely to be corroborated even in the absence or significant delay in the development of language (Rapin, 2005). Even though there may be a sense of relief in having one’s concerns finally validated and taken seriously, that same validation serves to exacerbate the ongoing anxiety about “what’s wrong.” Thus, many families come to an evaluation with mixed emotions (Randall & Parker, 1999), which are sometimes accompanied by a sense of mistrust of professionals. It is very important to understand how and why this mix of emotions and the accompanying mistrust of professionals can impact the ultimate goal of advancing outcomes for children and families living with ASD. The emotional mix is a powerful and confusing combination of anxiety, sadness, fear, anger, guilt, and ambivalence that comes from both desiring and dreading to hear the “truth about my child.” When parental concerns have been dismissed by professionals in the past and now those concerns are suddenly and finally confirmed, parents may feel a sense of ambivalence regarding professionals’ competence. The risk is that the family may discount or dismiss the findings and recommendations of the evaluation and, consequently, may not pursue appropriate and necessary services for their child. In such cases, delays in implementation of appropriate treatment may impact the potential outcome, including the child’s and the family’s overall quality of life (Harris & Handleman, 2000). Thus, it is essential to establish trust and build a healthy working alliance with the family from the initial contact and to maintain this throughout the evaluation process and subsequent follow-up.
+
+The Initial Intake Process
+
+The first encounter with the clinical team can be crucial for the formation of an alliance between the parents and those involved in the assessment of the child. It often occurs in the context of collecting basic referral information necessary to determine the scope of the difficulties, as well as a basic developmental and medical history. It is important to keep in mind that this process should also be aimed at giving the parents an opportunity to express their concerns and ask questions about the upcoming evaluation. This has to be done in a clinically sensitive manner, as parents often feel compelled to give very detailed accounts about their child’s medical and developmental history. In a way, the information may be more detailed than needed at this stage of the process. Yet the intake professional should listen attentively and with patience, because this will assuredly contribute to the establishment of trust and a working alliance with the parents. In addition to attention to what parents have to say, awareness of what is not said, or more specifically, what is not asked, is quite useful. Most parents want to know what to expect during the evaluation, yet few will ask questions such as, “What instruments will be used, and what is their purpose?” “What happens, when, and how long will it take?” “Can I stay with my child and observe the assessment?” “Will I be able to discuss the results immediately with the assessment team?” “Can I bring along a family member or the child’s therapist?” These are very important questions, the answers to which help to reduce uncertainty, and thus anxiety. Equally important, it will help parents prepare for the evaluation so that the conditions for maximal engagement of child and family are attained. Avoidable long waiting periods, indication of preferred waiting areas, even clear instructions for parking arrangements, all facilitate success or, alternatively their absence can exacerbate anxiety in all involved and can even exacerbate or trigger maladaptive behaviors in the child. Parents can also help the process by bringing a favorite toy or treat to entertain their child during transitions and waiting periods. Extended family members or family friends are often very helpful in supporting a parent and the child through the assessment process, which often extends for several hours at a time. Understanding the process of assessment and familiarity with its components helps parents face this potentially complex and stressful process. Another clinical consideration for the intake process is the emotional state of the family. Understandably, most parents are greatly alarmed by the prospect of their child having a diagnosis of ASD, although many will work hard to mask their feelings. One way to explore their emotional state is to open with a statement aimed at normalizing the anxiety that naturally accompanies the evaluation, such as, “Most people are quite nervous about coming to the clinic. How are you doing with all of this?” This type of conversation often reveals key aspects of family coping strategies and informs the clinician about the most effective ways of communicating with the family about the assessment results and recommendations for treatment. For instance, the conversation during the intake process may reveal that one or both parents are struggling with such high levels of anxiety or depression that their ability to concentrate may be compromised. Such parents may benefit from recommendations from the assessment team that are clear, concise, and focused on two to three priorities. This subject is elaborated on in a subsequent section of this chapter. Although gathering intake information alone may be technically sufficient, clinical sensitivity and exploration of the emotional states of the parent or parents lends an opportunity to normalize feelings, establish trust, build relationships, and discover how to tailor the evaluation process, especially the discussion of the results, to best meet the needs of the family. The essential point is that the aim of the assessment is not exclusively to learn about the child. A good assessment pursues and includes information about the family and uses that information to guide interactions. The hope is that the family will be better prepared and able to implement the recommendations and advocate effectively for appropriate services in a timely manner.
+
+The Assessment Process
+
+Ideally, a family arrives for a comprehensive assessment for the child already having a good understanding of the diagnostic procedures and the roles of the members of the assessment team. Nonetheless, it is often necessary and certainly helpful to recapitulate the main points, introduce the members of the team, and encourage the parents to seek clarification whenever they feel necessary. Parents are often uncertain as to how they should behave during the assessment; therefore, it is important to facilitate a level of comfort by outlining the goals and course of each of the assessment procedures. Many parents are concerned that the standardized assessment conducted in an unfamiliar environment will skew the results and not reveal the child’s true level of skills. Such concerns often stem from the fact that the child may only rarely or inconsistently display certain skills. It is often necessary to explain that it is necessary to sample the child’s skills in unfamiliar contexts to gain a sense of the generalization of existing skills. Similarly, parents often need to be reassured that all measures will be taken (within reason) to obtain the child’s optimal performance in such situations (see Chawarska & Bearss, Chapter 3, this volume). Consideration of how consistently a skill is displayed across people and across settings and specific situations is also helpful. One should assure parents that clinicians are not only measuring the child’s skills but also learning about conditions that promote and support the child’s learning. Moreover, an explanation as to the need to ascertain both strengths and deficits, and not simply reach a categorical diagnosis, is key in engaging parents about the ways in which the educational program should be designed and implemented. Responding to this type of parental concern affords the team members an opportunity to strengthen their credibility and alliance with the parents and helps parents to appreciate the complexity of the assessment process and their child’s needs. The formation of such an alliance is likely to have significant implications for coping and the development of effective advocacy, as described in greater detail later. Inviting a parent to join the young child in the assessment room and to observe the assessment process is essential for several reasons. First, it provides much needed comfort and a sense of familiarity for the child. Second, parental participation in the assessment process can greatly enhance subsequent discussions, when the meaning of specific behaviors that the parents and the clinicians observed can be elucidated. However, many parents can be quite uncertain as to how active they should be during the evaluation. A clear explanation of the goals of the procedures and the anticipated parental role (e.g., as an observer and supporter or as a play partner for the child) usually helps to alleviate this uncertainty and reinforce the parental sense of participation in the assessment process. Sometimes parents who are eager to bring out their child’s best performance may take an active role in the assessment in ways that can be counterproductive, such as, for instance, by violating standard test administration conditions. Sensitive yet firm reminders by the professional may be warranted, along with an explanation of the degree of parental involvement that is typically helpful versus interfering. Eliciting a parent’s view of the child’s functioning during the assessment, as compared with other, more natural settings, is also imperative (Klin, Saulnier, Tsatsanis, & Volkmar, 2005; see also Chawarska & Bearss, Chapter 3, this volume). As mentioned above, a discussion of differences in the child’s presentation across contexts has implications for educational programming, as well as for parents’ regard for the credibility of the evaluation and their motivation to follow through on recommendations.
+
+Communicating Diagnostic Findings
+
+Once the evaluation is completed, an immediate discussion with the parent or parents is preferable, as long delays may be difficult to tolerate. The discussion, often referred to as a parent conference or a feedback session, is essentially an opportunity to serve the family members and prepare them to serve their child (Klin et al., 2005). An effective feedback delivers details at the level of parental interest and probes for parents’ questions. Following a detailed discussion with the clinical team regarding the child’s current level of functioning in various key areas and the diagnostic considerations, parents invariably feel compelled to project the current situation into the future by asking, “Will my child be ‘high functioning,’ ” “Will her challenges be mild or severe?” “Will she be mainstreamed by the time of kindergarten?” and, at times, “What about college?” The questions relating to both short- and long-term outcomes are naturally very important for both parents and professionals. These are also questions very difficult to answer on a case-by-case basis, as our ability to predict long-term outcomes for very young children with ASD is still limited (Charman et al., 2003; Chawarska, 2007; Lord et al., 2006; see also Chawarska & Bearss, Chapter 3, this volume). Although some parents may find it reassuring that their child’s future has not been “sealed,” others find it difficult to accept, as they may be seeking guarantees that their child will eventually “outgrow” or “recover” from his or her social and cognitive disabilities. Providing parents with the most up-to-date information regarding the stability of the diagnosis and predictors of outcome (Howlin, Goode, Hutton, & Rutter, 2004; see also Bishop, Luyster, Richler, & Lord, Chapter 2, and Chawarska & Bearss, Chapter 3, this volume) may help them cope with the diagnosis and make decisions about treatment options. Early characteristics that bode well for more positive outcomes are the acquisition of speech, nonverbal cognitive strength, and a good rate of progress over time (Chawarska, 2007; Howlin et al., 2004). Highlighting the relative strengths of the child is critical, as this gives parents a sense of hope and provides a more complete picture of their child that extends beyond the identified delays and deficits. Hope inspires and energizes parents to take action (Marcus, Kunce, & Schopler, 2005), which, coupled with competent guidance, helps parents to pursue early, intensive, and appropriately focused interventions.
+
+Another frequently asked question is, “What can we do to help our child now?” Effectively, this is a call for explicit guidance and instruction. Helping families to find answers to this question requires extensive training and familiarity with effective treatment approaches, as well as available resources. This question may need to be addressed on two levels. The first level involves recommendations on how to attain appropriate treatment and educational programming for the child, utilizing the community resources. The second level involves a question about what the parent or parents can do themselves to facilitate the child’s development on a daily basis at home and in other settings, or ways in which they can extend their roles from parent to therapist. Both levels need to be addressed. It is typically the case that the more appropriate and structured the educational program is, the less stressful the parents are likely to be. Parents should also be encouraged to safeguard times in which they are unconditionally accepting and loving parents, not therapists, for their child. If every moment of the day is conceived as a moment for therapy, burnout can ensue. The child is unlikely to be able to please a parent in some situations of learning, which can lead to constant frustration. Thus, typical parent–child interactions, with periods of silly play and “winding down,” can be very important to preserve the natural pleasures of parenting while also preserving the child’s and the family’s energy for the long periods of work and directed learning. Although it is important to be aware of the general features of ASD, it is even more important to appreciate how ASD is manifesting in a particular child and to help parents understand and articulate their child’s specific needs (Dunlap, 1999). This impacts the parents’ efficacy as advocates when discussion on educational programming centers on identifying the child’s needs (Volkmar, Cook, Pomeroy, Realmuto, & Tanguay, 1999). To simply report that the child has ASD is insufficient. Parents need to know how their child is functioning, as compared with same-age peers, in each area of development; therefore, it behooves professionals to identify and communicate this clearly to parents (Marcus, Flagler, & Robinson, 2001). Areas of intervention to consider for young children with ASD include safety; cognition; motor skills (both fine and gross); speech, language, and communication; social interaction skills; play and imagination; adaptive skills (e.g., toileting, dressing, bathing, feeding, sleeping, and coping); recreation; and the presence of interfering behaviors. Undoubtedly, this list is extensive and reflects the wide range of needs that young children with ASD may have. It is also meant to highlight the fact that current law and the existing educational guidelines indicate that an appropriate program for children with ASD must address all areas of educational need that spring from the disability (20 U.S.C. § 1412 et seq.; National Research Council, 2001; Olley, 2005). Notably, educational need encompasses academic, developmental, and functional skills and abilities. It is essential that all of these areas be considered during the evaluation and that relevant recommendations be made for each area of need (Lord & Risi, 2000; Mandlawitz, 2005; Tager-Flusberg, Paul, & Lord, 2005; Klin et al., 2005). This effort should be explicit, as the current educational climate continues to focus primarily on academic instruction. Fortunately, this trend is shifting to include social and adaptive skill development, but the shift is slow and uneven across the country. Therefore, feedback discussions and written recommendations that clearly address all areas of need can give parents the awareness that these are key areas to target for services and the leverage to pursue them.
+
+SUPPORTING PARENTS IN OBTAINING APPROPRIATE EDUCATIONAL SERVICES
+
+Establishing Goals and Priorities
+
+The needs of children with ASD can be so extensive and pervasive that it is essential to establish a hierarchy of priorities to be emphasized in a program. By law (20 U.S.C. § 1400 et seq.), each eligible child is entitled to an individually designed and implemented program. Professionals working with a child whose needs span many developmental areas should help the family develop a hierarchy of needs that the program should focus on systematically. Safety should always be at the pinnacle of this hierarchy. Common safety issues among young children with ASD include mouthing and/or ingesting nonfood items, darting, and climbing. Self-injurious behaviors such as head banging, or aggressive behaviors such as biting or hitting, are less frequent, but when they occur they must be treated immediately. Interfering behaviors may also occur, which essentially include anything that interferes with a child’s ability to learn or use a skill functionally, including motor mannerisms, repetitive or restricted interests, distractibility, activity level, and so forth. Basic learning-to-learn skills also fall into the behavioral domain, and these include the ability to attend to speech, sit, monitor the therapist’s behavior, follow directions, and engage in vocal and motor imitation. These skills prepare a child for the fundamental process of learning and are frequently and successfully addressed utilizing behaviorally based methods and techniques (Harris & Weiss, 1998; Hodgdon, 1995). Any behavior that puts the welfare of self or others at risk should be systematically studied via a functional behavioral analysis (FBA) and addressed via positive behavioral supports (20 U.S.C. § 1415 et seq.; Powers, 2005). This is a dynamic process that involves a certain amount of trial and error. In order for the process to be successful, several elements need to be in place: (1) a competent professional to do the analysis and design the intervention, (2) ongoing monitoring of the intervention for effectiveness, (3) flexibility and change of the intervention as needed, and (4) collaboration among all team members, including parents, for consistency in the application of the intervention (Schopler & Mesibov, 2000). This effort should not be restricted to the school setting. If the difficulties exist outside the school—that is, at home or in the community—the child’s needs should be addressed directly in those settings. The hierarchy of needs can vary from family to family and from child to child; however, a useful model puts safety first, interfering behaviors second, followed by a mix of efforts in the areas of communication, social interaction, adaptive skills, motor skills, and cognitive development as warranted.
+
+The need for intervention in the areas of communication is self-evident, as impairments in this area are one of the defining features of ASD. However, it is important to help parents understand that teaching communication to young children is not equivalent to helping them to amass a large vocabulary. Intervention in this area needs to be focused on fostering an understanding of language: expressing needs, sharing interests, and commenting on experiences, as well as using nonverbal means such as gestures, facial expressions, and eye contact for communication (Wetherby et al., 2000; Prizant, Wetherby, & Rydell, 2000; Paul & Sutherland, 2005). For many children, the development of communication can be fostered with the use of pictures, signs, and assistive technology (Bondy & Frost, 1995). Parents need to advocate for intervention that targets the functional use of speech and language for the purpose of spontaneous and flexible communication, rather than simply the acquisition of verbal labels. Written reports should reflect this distinction in the “Recommendations” section. Social impairments are another defining feature of ASD, which also warrant thoughtful intervention. Children who have some means for functional communication and imitation skills may benefit from adult instruction and facilitated support for peer interactions. Children who have not yet developed imitation skills in particular may be better served with individual adult instruction designed to develop these skills in preparation for peer interaction. The goal is to move the child toward independent and functional use of skills. The effort is informed by the child’s present level of need and the pace, level of support, and context in which the child can benefit from intervention. The typical repertoire of adaptive skills in young children includes feeding, toileting, dressing, sleeping, and personal hygiene skills. These areas can be quite challenging for children with ASD. The important point is that these are all legitimate and reasonable areas to target for intervention, by both early intervention providers and preschool settings. Self-reliance and independent living skills are essential long-term goals (Klin et al., 2007). The Vineland Adaptive Behavior Scales–II (VABS-II; Sparrow, Cicchetti, & Balla, 2005) is an example of a useful tool that can be used to formally identify specific adaptive needs. Common areas of adaptive need for young children with ASD include sleep (Didde & Sigafoos, 2001; Honomichi, Goodlin-Jones, Burnham, Gaylor, & Anders, 2002; Wiggs & Stores, 2004), feeding (Ahearn, Castine, Nault, & Green, 2001; Field, Garland, & Williams, 2003), and toileting (Volkmar & Wiesner, 2004; Wheeler, 2004). A feedback discussion, which includes identification of adaptive needs and guidance for setting treatment priorities, assists parents in making decisions and taking action to secure appropriate therapeutic and educational programming. Communication, social interaction, and adaptive skills often overlap. Although the specific teaching in each of these areas may be quite separate and different, the hope is that over time the skills will converge toward a higher level of functioning, especially in the context of peer interaction. For example, it is adaptive for a 4-year-old to be able to kick and throw a ball. Recreation is conducive to social interaction, which, in turn, is conducive to better communication.
+
+Providing Information about Resources
+
+One of the questions frequently asked by parents is, “Where can I find more information about ASD that I can trust?” This question is extremely relevant, considering the proliferation of both expert and nonexpert opinions about ASD, including its causes and treatment, via the Internet and various non-peer-reviewed publications. It is often helpful to provide parents with a reading list that can both give them more information about the disorder and provide them with suggestions on how to help their child in day-to-day situations. Books frequently cited as helpful by parents include Healthcare for Children on the Autism Spectrum (Volkmar & Wiesner, 2004), Children with Autism and Their Families (Powers, 2000), Right from the Start (Harris & Weiss, 1998), More Than Words (Sussman, 1999), Visual Strategies for Improving Communication (Hodgdon, 1995), and Do–Watch–Listen–Say (Quill, 2000). There is evidence suggesting that parents who are actively engaged in the delivery of intervention enjoy a greater sense of confidence and efficacy in the parenting role, and such involvement may contribute to greater progress for the child over time (Eyberg, Edwards, Boggs, & Foote, 1998; Schopler, 2001; Webster, Stratton, Reid, & Hammond, 2001). A word of caution is worth repeating: For a parent who is involved in helping the child, there is a risk that the role of therapist can supersede the role of parent. This speaks to the difficulty in maintaining appropriate roles and balance in a family living with ASD, which is discussed in greater detail subsequently.
+
+Supporting Families in Accessing Services
+
+To a large degree, helping a young child with ASD is a matter of securing appropriate services for an intervention program. Typically, services are available via a designated state agency. Early intervention services typically span the ages of birth to 3 years. At the age of 3, children are usually transitioned from the early intervention system to the public school system. The Special Education Law, formally known as the Individuals with Disabilities Education Improvement Act of 2004 (Public Law 108-446), and often referred to as IDEA-2004, mandates that these public systems provide appropriate intervention services and educational programming to those children who are deemed eligible. Specific eligibility criteria can vary from state to state, particularly in the early intervention system. Familiarity with the current governing regulations is critical in order to give good counsel to parents in their pursuit of services. Some states have published guidelines for educating children with ASD that help to frame and structure the content of a program (New York State Department of Health Early Intervention Program, 1999), which can be useful for professionals in making appropriate recommendations for programming and useful for parents in negotiating for services. If such state guidelines are not available locally, another good reference is Educating Children with Autism (National Research Council, 2001), which is written for both parents and professionals. It covers many topics, including guidelines for effective and appropriate programming for young children. Why is it important to be aware of educational guidelines and special education law? Unfortunately, in some situations, guidelines and laws are not adhered to automatically or to their full extent. Thus, as Mayerson (2004) highlights, parents inherit the often unwelcome yet necessary responsibility to become effective advocates to secure services that their child needs and is entitled to by law. Effective advocacy requires a knowledge of ASD in general, an understanding of how it is manifesting in a particular child, familiarity with educational guidelines (National Research Council, 2001; New York State Department of Health Early Intervention Program, 1999) and special education law, and utilization of negotiation and mediation skills (Volkmar et al., 1999). It may be daunting for parents to discover that they need to learn about and facilitate implementation of these guidelines while reeling from the news that their child has a developmental disorder. The task may at times be overwhelming, yet the need remains (Howlin & Moore, 1997). Parents who wish to become well versed in the educational guidelines and laws often find that it takes considerable time and effort to develop competence in this area, especially in the early years of adjusting to life with ASD. Professionals equipped with this knowledge are well positioned to give good counsel to families.
+
+Supplementary Supports
+
+Parents often pursue the option of supplementing the program offered by early intervention providers or school systems with additional services. Competent private practitioners, whether they are speech therapists, occupational therapists, physical therapists, ABA therapists, or individual or family therapists who are familiar with ASD in young children, are in high demand and short supply. An effective way to find such specialists is to network with other parents to explore who might be available in the community and how they may be helpful. The local chapter of the ASA may be a good place to network with other parents. There may also be other parent groups in the area, and some schools have special education parent–teacher associations. In addition, professionals who go to parent meetings have an opportunity to meet a broader array of families in the community, learn about their concerns and priorities, and learn more about recommended service providers. Taking time to meet providers and to gain an appreciation of their personal styles and working philosophies may also be helpful in matching them with particular families. A good fit is vital in sustaining a healthy and productive working alliance over time. When a family starts creating an overall program that includes multiple providers, a word of caution is warranted. There is nothing inherently wrong with such a mix of professionals, and, in fact, it can be very effective, but it is imperative that all of the team members are communicating and coordinating their efforts with one another, with a view to reaching consensus about the child’s level of need and how to address it (Schopler & Mesibov, 2000). Without such integration of efforts, there is a strong possibility that instruction and intervention across professionals will be fragmented and possibly at odds. When this occurs, the child is at risk of confusion, which impedes learning. The more service providers involved, the greater the need for clear communication, consensus regarding effort, frequent monitoring of efficacy, and flexibility to respond to changes as warranted. Coordinating the schedules of multiple personnel can be quite challenging. Although the process can and probably will be fraught with frustration, striving for it is justified by the positive impact it will have on a child’s opportunity for learning. Still, this can be more than some families can manage. If resources allow, having someone take on the role of service coordinator or education consultant to oversee the process can be very helpful. Even in cases where the child is receiving services from a single provider (e.g., public school), it is still valuable to hold regular meetings with teachers, therapists, and parents to ensure that everyone is working in ways that truly support learning. As generalization of skills is one of the most entrenched challenges in programming for children with ASD, a lack of integrated efforts across people and settings can significantly and deleteriously impact the eventual outcome of the program. In other words, it can undermine the entire effort.
+
+LEGAL CONSIDERATIONS
+
+Individuals with Disabilities Education Act
+
+The Individuals with Disabilities Education Act (IDEA, 2004) mandates the provision of appropriate services for eligible children, yet the term appropriate is not defined in the statue (Wright & Wright, 2004). Appropriate has come to be understood as that which is effective in helping the child to make tangible and measurable progress. Progress that is apparent only in a particular context—that is, the testing environment or the classroom—does not constitute skill mastery. True progress, true skill mastery, is defined by spontaneous and flexible application of a given skill, with a variety of people and materials, across a variety of settings, and across time. Thus, each acquired skill needs to be further maintained and generalized (Klin et al., 2005). Young children with ASD require intensive and explicit instruction and opportunities to practice their skills repeatedly in order to gain true mastery in natural and varied contexts. This process takes time, and there are differing opinions as to how much intervention time is necessary. The Educating Children with Autism report (National Research Council, 2001) has delved into this question extensively and determined that a reasonable and appropriate program for young children with ASD is full-time and full-year, meaning a minimum of 25–30 hours (15–20 hours for children under age 3) of instruction per week, running 12 months a year, and supplemented with additional hours of service provided for in-home and community support as warranted. Early intervention programs are typically designed to run year-round, and services are delivered in the home and community. Public school programs are typically designed to run approximately 9 months of the year and are based at a school. Many public schools offer only half-day programs until the child enters first grade. Regardless of what educational programs are currently available, recommendations addressing the child’s needs while following educational guidelines and federal law are needed. This gives parents leverage to negotiate and push for appropriate, reasonable, and individualized services, regardless of existing programming offerings. The complicating factor is that what is “appropriate” varies from child to child on the basis of his or her individualized profile of needs and developmental assets, hence the importance of highly individualized assessments (see Bishop et al., Chapter 2; Chawarska & Bears, Chapter 3; and Paul, Chapter 4, this volume).
+
+It is reasonable for parents to request a full-time program even though the school currently has only a half-day program for preschool and kindergarten. It is reasonable to request programming through the summer when the school typically closes during that time (Mandlawitz, 2005). It is also reasonable to request services for the family and the child in the home and community that extend beyond the school day. Legal statue (20 U.S.C. § 1400 et seq.) and educational guidelines (National Research Council, 2001) support all such requests as required, given the child’s needs. Just as parents benefit from a working knowledge of special education law, so too do professionals. The statue itself and www.wrightslaw.com are useful resources. Familiarity with case law (Mandlawitz, 2002) quickly reveals the power of language and the importance of choosing words carefully. This is true in meetings of record (e.g., school meetings), and it is certainly true for written reports. It is essential for professionals to understand the implications of language and word choice in order to support rather than undermine parents’ efforts to secure appropriate services for their child.
+
+Free Appropriate Public Education
+
+IDEA-2004 states that children deemed eligible for special education are entitled to a free appropriate public education (FAPE) (20 U.S.C. § 1412 et seq.); however, as mentioned previously, “appropriate” education has been left undefined. It is conventional to think of appropriate education as that which is effective in supporting and moving a child toward progress in areas of need. The importance of word choice when making educational recommendations cannot be overstated. Public service providers are not mandated to provide the “best” services possible, and their mission is not to maximize a child’s potential (Board of Education of Hendrick Hudson Center School District v. Rowley, 1982). Rather, they are responsible for the provision of adequate services (Mandlawitz, 2005). Therefore, when wording recommendations it is imperative that words such as best, excellent, optimal, and ideal are excluded. These words can be counterproductive and in the worst cases can actually undermine the credibility of the entire report. Surprisingly, the word beneficial is also problematic, as any child could potentially “benefit from” the provision of practically any intervention. Professional responsibility dictates an effort to identify the child’s needs and make reasonable and appropriate recommendations to meet those needs with respect to one’s particular area of expertise. Educational guidelines further help to frame what is reasonable.
+
+Individualized Education Program
+
+All eligible children with disabilities are entitled to an individualized education program (IEP) that is designed to address their specific and individual needs (20 U.S.C. § 1414 et seq.). This points again to the importance of delineating the child’s specific needs and helping parents to articulate and prioritize these needs. It is inappropriate to place all children with ASD in the same classroom, targeting all the same goals, utilizing the same instructional methodology and the same supports. The key is that each child with ASD is an individual with specific needs that call for specific and individual attention. The potential benefits of early intervention and special education are compromised when instruction and intervention are not provided in ways and at levels from which a particular child can learn. Common and worrisome experience shows that many children are offered the existing “autism program,” which typically includes a standard and previously determined instructional approach, classroom designation, number of hours of instruction, intervention modalities, and goals. Such educational prepackaging should raise red flags for both parents and professionals. Instead, the designing of the intervention program should be collaborative and involve parents in the decision-making process. Decisions regarding the content and form of the program should be guided by the child’s individual needs, including the child’s profile of challenges and existing strengths.
+
+Parents as Partners
+
+The IEP process is designed to include parents as equal partners (20 U.S.C. § 1414 et seq.). An initial step in the process is to determine eligibility for special education. Once eligibility is established, the next step is to identify the child’s present levels of functioning in all areas of development. This serves to highlight both the child’s strengths and areas of need. Areas of need are further prioritized, and goals are established based on an understanding of those needs. The goals reflect the effort to help move the child toward progress in all areas of educational need with the understanding that these needs may span academic, social, emotional, motor, and adaptive skills that are generalized across contexts and maintained over time. The goals are typically thought to cover a 1-year period, with the expectation that progress can and will be measured objectively (20 U.S.C. § 1414 et seq.). Exactly how progress will be defined and measured is another factor to be discussed and agreed upon by the IEP team. Once goals and measurement are agreed upon, the next step is to determine the logistics of the program. Which professional will be providing what service? Which methodology will be utilized, in what setting, how often, and for how long? Educational guidelines and special education law do not delineate specifics at this level. Although the guidelines suggest the total number of hours of programming that are generally considered appropriate, the specific number and duration of sessions of speech therapy, occupational therapy, physical therapy, applied behavior analysis (ABA), facilitated play, and so forth, are left to the IEP team to determine. Such determination can be daunting for both parents and professionals.
+
+Professional Boundaries
+
+Although a multidisciplinary approach to the evaluation of young children suspected of having ASD is typically recommended (Klin et al., 2005), practice limitations do not always allow for such an intensive process. In such situations, professional restraint is warranted to increase the credibility and usefulness of recommendations stemming from the evaluation. Specific recommendations limited to the parameters of one’s particular professional discipline are necessary. Recommendations that span beyond the scope of a particular discipline can be problematic and counterproductive and can lead to questioning the credibility of the evaluation and subsequent recommendations. Although recommendations should highlight areas of identified need, professional restraint regarding the impulse to designate a specific number of hours or sessions or specific modality is encouraged. Instead, professionals in a given area of expertise may provide recommendations for a formal evaluation to be conducted by a professional in a different field, such as speech–language, occupational, or physical therapy, to assist in determining the frequency, duration, and specific approach of sessions warranted to adequately address the child’s needs and help the child meet his or her IEP goals. Clearly, it is preferable when a group of professionals work together as a transdisciplinary team so that a single coherent view of the child can emerge from the evaluation process. When that is not possible, coordination of different expert opinions stemming from different areas of expertise is needed, particularly insofar as the operationalization and implementation of recommendations are concerned. Otherwise, parents may be left with the daunting task of having to integrate what might be, in some situations, a plethora of conflicted reports and recommendations.
+
+Least Restrictive Environment
+
+Another important consideration for the IEP team is placement. Placement refers to where the child goes to school: which school, which classroom, and with what level of support. IDEA-2004 (20 U.S.C. § 1412 et seq.) refers to the least restrictive environment (LRE), which is the environment closest to that of the mainstream classroom in which the particular child can benefit from instruction and make progress. The intent is to give children with disabilities opportunities to engage and associate with typically developing peers, rather than be automatically assigned to separate and more restricted environments. To automatically place all children with disabilities in a mainstream classroom is equally inappropriate. Placement decisions call for thoughtful consideration of the interaction of a given child’s needs with the instructional environment and how that interaction can support versus undermine the child’s learning. In many ways, the spirit of the law points to the placement of a child in an educational environment whereby the child can learn and profit from instruction with the fewest restrictions regarding access to typically developing peers. The determination of appropriate placement hinges primarily on two factors: the child’s ability to actually benefit from access to typically developing peers and access to the general curriculum (Handleman & Harris, 2001) and the level of competence and ready availability of professionals instructing the child (Simpson, 2004). In order to benefit from access to typically developing peers, a given child needs to have, at a minimum, a functional communication system, imitation skills, some degree of social interest, and an ability to at least briefly stay on task with or without adult support. Such a child, with appropriate levels of support, is more likely to be able to function, learn, and make progress in the mainstream setting and may be a good candidate for such a placement. A child manifesting behaviors that are aggressive, destructive, self-injurious, or highly distracting or who lacks the aforementioned basic skills is questionable if not inappropriate as a candidate for the mainstream setting until these problems are addressed in a more specialized educational setting. Placement is rarely an either–or decision, but instead is often a combination of mainstream and contained environments with varying degrees of adult support. Notably, few young children with ASD are able to function and learn in any school environment without at least some one-on-one adult instruction and support. Equally important, research has shown that a number of factors facilitate successful integration (Handleman & Harris, 2001) and that for many children, rather than a dichotomy of “fully mainstreamed” or “fully segregated” placement, it is a continuum of services that is needed.
+
+Due Process
+
+In the best of circumstances, families and service providers, be they early interventionists or school personnel, can easily and readily reach consensus regarding the child’s needs and how to meet them appropriately. Rightly, legislators have anticipated that such an outcome will not always be the case, and therefore special education law includes a course of due process for those situations that call for legal intervention to resolve differences (20 U.S.C. § 1415 et seq.). The decision to pursue due process is a serious one, and consultation with an attorney who is well versed in special education law is advised. The decision should not be taken lightly as it will change the relationship between the family and the service providers, sometimes in ways that are irreparable (Mandlawitz, 2005). Although due process is a valuable and sometimes necessary option, such a course of action warrants great care and much thought. Efforts to avoid due process are worthwhile and begin with a thorough understanding of the relevant information and access to resources. Professional responsibility encompasses highlighting the child’s needs, making appropriate and comprehensive recommendations, and educating families about the disability, the guidelines, and the law in order for them to be effective advocates for their child. Resources that can assist parents’ efforts at self-education include state guidelines (if available), Educating Children with Autism (National Research Council, 2001), as a national reference for educational guidelines, and www.wrightslaw.com for information regarding special education law and advocacy.
+
+FAMILY IMPLICATIONS
+
+In addition to issues related to diagnosis and treatment planning, there are a number of essential family-related issues that have a potential impact on the functioning of the entire family (Marcus et al., 2005). In many ways ASD is a family disability. Although a single child may be identified as affected with the disorder, the entire family, including extended family members, is affected by its emotional and financial fallout (Burack, Charman, Yirmiya, & Zelazo, 2001). How family members are affected, and to what degree, can vary significantly and can change over time. Here we focus on family implications that are typically seen in the young-child phase of family development and family functioning. As noted, it is quite common for parents of a child with ASD to have concerns early in the child’s development. They often suppress these concerns, especially if this is their first child. By the time the child reaches the age of 2, professionals may concur with the parents’ concern, especially when language development appears delayed. The parents often feel relief and confidence when their concerns are being validated; however, that same validation often stimulates anxiety. What will it mean if their child really does have a problem? Parents may also experience anger at having been dismissed previously. Anger and anxiety can further result from a sense that precious time for early intervention may have already been lost (Marcus et al., 2005), which is compounded by a complicated grief process surrounding the loss of the idealized child and family. The cumulative stress can be unbearable and depression often ensues (Siegel, 2003; Hastings & Johnson, 2001; Olsson & Hwang, 2002; Seltzer, Krauss, Orsmond, & Vestal, 2001; Tobing & Genwick, 2002). Anecdotal evidence suggests that once a child is identified as having ASD, both parents typically experience an overwhelming sadness coupled with a sense of urgency that compels them to actively pursue intensive services for their child. The day-to-day details of this pursuit are often left to one parent. Both parents seem to struggle with depression and anxiety, and both typically take on a task-oriented approach to coping. One parent typically becomes immersed in his or her professional work, while the other parent takes on the responsibility for pursuing, organizing, and monitoring services for their child, to the extent that many such parents give up their professional careers (Gray, 2002; Seltzer et al., 2001). Both parents could probably benefit from some form of therapeutic support at this point, but the inclination is to be self-sacrificing—to ignore their own needs and focus almost exclusively on the work role that they have embraced. Some parents use medication to help them cope and keep up their energy, but they seem much less likely to engage in other pursuits that may offer therapeutic benefit. To do so is often regarded as selfish and frivolous when their child’s needs are so grave and so immediate and the magnitude of financial responsibilities related to, for instance, supplementing the child’s treatment, becomes apparent. Unfortunately, this dynamic has the potential to set down roots for unbalanced individual and family functioning in the future.
+
+Imbalance
+
+Imbalance is often the primary focal point in maladaptive or disrupted family functioning. It is also the most difficult area for therapeutic work. Regrettably, families often do not seek help until they are at the point of breakdown. Therefore, it behooves professionals to alert families to this inclination and help them to monitor for and identify signs that can essentially give them permission and impetus to help themselves. Interestingly, many parents are quite defensive about a direct recommendation for therapy for themselves. A typical response is, “You’d be going crazy too if you were going through what we are.” Often a more helpful approach is to educate parents on the physical and cognitive signs that warrant clinical concern in general: change in appetite, weight, sleeping pattern, or sexual drive and presence of obsessive or persisting thoughts. Seemingly, parents find it more logical and more acceptable to think about these factors versus emotional factors as triggers for self-help. This may be especially true when the focus is on keeping themselves healthy so they can continue to have the energy and clear thinking necessary to help their child.
+
+Not surprisingly, living with ASD and all of its ramifications puts enormous stress on families. Perhaps it is true of all disabilities, but it is certainly true that ASD is a family disability in that it impacts all family members (Powers, 2000), including extended family. The risks to the family include isolation, role imbalance, depression, anxiety, grief, guilt, blaming, and extreme self-sacrifice. Additional risks include financial hardship due to costs of supplementary services and/or due process expenses, and chronic sleep deprivation in cases involving children with disrupted or difficult sleeping patterns. Some families experience isolation from their communities because of the child’s behavior being idiosyncratic, unsafe, or disruptive to the extent that it becomes too difficult or too embarrassing to go out in public (Gray, 2002; Avdi, Griffin, & Brough, 2000). Isolation can also occur within the family. This tends to happen when parents are unable to agree on the needs of the child or how to meet those needs. In such instances communication between the parents diminishes along with emotional support, with one parent becoming overinvolved with professional work life, spending less and less time at home and being more withdrawn while at home. The other parent can become hyperfocused on the child with ASD, with the thought that everyone else in the family is able to function on his or her own. This seems to be driven by a parent’s anxiety regarding the perceived ever-closing window for early intervention. It also seems to be driven by a deep-seated sense of guilt for somehow causing the ASD and/or a sense of guilt for not trusting his or her initial intuition of concern and not being more proactive in getting help for the child at the first moment of concern. Although these self-imposed pressures may seem irrational to an outside party, the stress is very real and can be quite debilitating for a family caught in this dynamic, all of which is further complicated in single-parent families. The unrelenting effort, coupled with emotional weight, quickly becomes quite tiresome, and relations with spouse, other children, extended family members, and friends are easily strained if not neglected. The strains are particularly worrisome when the child with ASD makes limited progress. For, despite the best efforts of the family and service providers, some children unfortunately do not make much progress (Mundy, 2003). In such situations it is not unusual for the family to pursue less conventional therapies and practices (Marcus et al., 2005; Rapin, 2005; see also Smith & Wick, Chapter 9, this volume). Such is a time to help families think through options carefully and weigh hoped-for benefits against some very real risks that may have the potential to do harm.
+
+Siblings and Extended Family
+
+Siblings of a child with ASD often find their emotional needs somewhat neglected within their primary family (Harris & Glasberg, 1994). They tend to compensate for this experience by striving for excellence in all they do. Siblings are typically more mature than their same-aged peers and often take on responsibilities far beyond what would be expected from other children their age (Fishman, Wolf, Ellison, & Freeman, 2000; Konidaris, 2005). Typically, the most vulnerable sibling is the oldest female, who is most at risk for becoming quite parentified at a very early age. On the surface, siblings appear quite well adjusted and competent. Unfortunately, the heightened sense of responsibility and effort to excel may at times be driven by a very basic need for recognition, acceptance, and validation. Value as a person becomes associated with tasks and accomplishments versus simply being a child and a member of a family. External sources of validation (e.g., school) may become more reliable, and a sibling’s natural sense of value within the family may be at risk for being diminished. The need for validation within the family remains a primary, yet in some instances an inadequately met, need. Typically, the sibling facing this situation can strive for years to be good enough, to be a great helper, to not be in the way, and to be content with a perceived secondary status in the family. Sadly, this effort becomes quite difficult for many to sustain over time. Preadolescence seems to be associated with emerging mental health concerns, particularly depression, which if left unchecked, may result in serious and potentially life-long challenges. Alerting families to the risks for siblings and guiding them to healthy family functioning can be invaluable. Lobato and Kao (2002) highlight evidence suggesting that participation in a sibling support group may also be beneficial.
+
+Extended family members, especially grandparents, tend to follow one of several paths: (1) awareness of problems and supportive of needs, (2) dismissal of problems, often stemming from a lack of understanding of ASD and/or an inability to accept its existence within their family, and (3) an attempt to “take over” the situation, pushing ahead with a plan without the full engagement of the child’s actual parents. Dismissal of problems can undermine the parents’ efforts to secure and maintain appropriate services for the child. It also serves to diminish and invalidate the stress that the family encounters. At its worst, dismissal breeds irreparable contention and conflict among family members and results in a cut-off of relations. Sharpley, Bitsika, and Ephrimidis (1997) point out that time and effort extended to educate and inform in such circumstances serves to increase understanding, which in turn can decrease stress on families. And if the actual parents are pushed aside at this critical juncture, this may lead to their dependence on others and lack of active voice in a process that requires their decisive input. This often leads to a sense of powerlessness and ineffectual participation, thus delaying if not undermining altogether their becoming effective advocates for their children. From the standpoint of clinicians, this can be very confusing, as responsibility lies first with parents, and such confusion can lead to cross purposes.
+
+Protective or Resilience Factors
+
+There are many protective factors for family functioning that deserve to be cultivated and nurtured. A strong and committed partnership that supports the parental alliance as well as the marital bond is essential. A full understanding of the child’s needs and a shared focus on how to meet those needs is also essential. Having practical knowledge of educational guidelines, special education law, and advocacy skills certainly assists in meeting the child’s needs. And having an appreciation for the roles of the various family members and meeting their needs adequately can contribute to balanced and adaptive functioning of the entire family over time. A network of understanding and available friends can be protective, not only for emotional support but also for very practical needs such as child care and respite. Financial resources give families options regarding supplementary services and supports, which may contribute to a positive outcome for both child and family. The child’s steady progress is also protective, as it gives parents assurance that their efforts are effective; thus, they are less distracted by the often tempting yet questionable alternative therapies. Finally, and perhaps most important, is a sense of hope. When family members perceive that they can be effective and that their child can make progress, they are energized by a strong sense of hope for the future, which fuels their motivation to continue their diligent work in service of the needs they face. Hope is powerful and necessary, and its cultivation is crucial.
+
+CONCLUSIONS
+
+When pondering the complexities of ASD, it is important to keep in mind that this essentially social disability can have a powerful and disturbing impact on the entire family. Restoring and maintaining balance within the family often becomes the focal point of therapeutic effort, which helps families get past feelings of blame and guilt, or the compulsion to erase all traces of ASD, thus enabling them to set healthy priorities for all family members while finding ways to accept and even embrace a life with ASD. Such work is very challenging, yet so worthy of effort. An inspiring aspect of the work is the opportunity and privilege of witnessing families overcome very real and very difficult hurdles and come to view ASD as a “blessing in disguise” and an important factor in their sense of calling in life. Living with ASD opens many parents to self-discovery and personal growth that may not have happened otherwise. Countless numbers of families have noted that living with ASD has taught them to celebrate the little things in life, to not take anything for granted, to grow personally, to be brave, to be humble, and to be grateful. Many have an intense appreciation of the value and strength of family and actively reach out and support the health and advancement of the wider community of families living with ASD. Professionals working in the field of ASD have an important mission to help families cultivate hope by highlighting their strengths and the strengths of their children, and by encouraging habits that further strengthen their efficacy as loving parents and advocates for their children. Good practice alerts families to the potential risks that may be inherent in living with ASD and serves as a pivotal element in facilitating positive outcomes for children and families living with autism spectrum disorder.

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+that indicate a prerequisite or supporting skill met mastery should be transferred to the Skills Tracking Chart which provides the assessor with a visual reference of the learner’s progress (Sundberg 2008).
+
+The VB-MAPP Guide. The VB-MAPP Guide is the user manual for the VB-MAPP assessment. It provides background information on the overall assessment; a brief introduction to B. F. Skinner’s Verbal Behavior (1957); assessment tool administration guidelines; scoring instructions for the Milestones, Barriers, and Transition Assessments; and a guide for program development including suggested goals and objectives for Individualized Educational Plans (Sundberg 2014). An introductory review of verbal behavior is provided in the VB-MAPP Guide to orient the administrator to the necessary concepts of verbal behavior that are required to complete the assessment. The administration guidelines describe general strategies to increase the effectiveness of the assessment pro-cess. The scoring instructions provide additional information regarding the skill being assessed such as appropriate sample materials, an example of how to present the skill, and specific criteria for scoring. Suggested learning objectives are separated into levels that correspond to the levels on the Milestones Assessment (Sundberg 2014).
+
+Historical Background
+The VB-MAPP is based on Skinner’s Verbal Behavior (1957). Skinner used the term verbal behavior intentionally to differentiate his theory from more traditional theories of speech and lan-guage development. He wrote that the term speech was too limiting in that it excluded other types of communication such as sign language, exchanges via pictures, and writing. He also pro-posed that the word language was too broad as it encompassed entire languages (e.g., English). Traditional theories of language are broken pri-marily into three categories: receptive language, expressive language, and pragmatic language. However, Skinner identified several specific com-ponents of verbal behavior, referred to as verbal operants. Skinner categorized verbal behavior into two main categories – the behavior of the speaker and that of the listener (1957). The VB-MAPP assesses speaker behavior across sev-eral verbal operants including mands, tacts, intraverbals, echoics, motor imitation as related to sign language, textual, copying a text, and transcription. The VB-MAPP also assesses for listener skills such as following instructions (Sundberg 2008).
+
+Psychometric Data
+The VB-MAPP is a criterion-referenced assess-ment (Sundberg 2014). Criterion-referenced assessments are designed to provide information regarding a leaner’s performance on specific test-ing items over the course of time in order to identify progress (Powers et al. 2014). Although the VB-MAPP provides age ranges for each mile-stone skill level, the assessment is not designed to provide a standardized score, and therefore a limitation of this assessment tool includes the inability to compare the learner’s scores to their same-aged peer group.
+
+The Milestones Assessment. The Milestones Assessment Scoring Form is a color-coded visual grid that represents each of the three skill levels. Each level contains columns with a maximum score of 5 for each of the relevant developmental domains assessed in that level. Each individual milestone score is transferred to the Milestones Assessment Scoring Form and represented on the visual grid as either a blank (0), partially filled (1/2), or fully filled (1) scoring box. The total sum of each skill domain area across all three levels is then combined to determine the total assessment score which has a maximum score of 170. Progress is demonstrated and tracked on the Scoring Form for up to four testing administrations.
+
+The EESA. The EESA is initially scored sepa-rate from the Milestones Assessment; however, the EESA score is then incorporated into the Milestones Assessment Scoring. There are 100 possible points on the EESA. Points are assigned based on the production of correct sounds. Each item may be scored as (1) point for all correct sounds; (½) point for recognizable approximations with the correct vowel sounds, addition of syllables, and/or addition of consonant sounds; or (0) points for no response, unrecognizable approximations, omitted sylla-bles, and/or incorrect vowel usage (Sundberg 2014).
+
+The Barriers Assessment. The scores from the VB-MAPP Barriers Assessment should be trans-ferred to the corresponding Scoring Form. The Barriers Assessment includes a Likert scale (0–4) in which assessor scores the child on 24 potential barriers to learning. A score of 0 on the Likert scale indicates the barrier is not a con-cern, while a score of 4 indicates the barrier is of significant concern. A scores of 2, 3, or 4 warrant additional intervention to prevent the barrier from becoming worse (Sundberg 2014). There is a pos-sible total of 96 points on the VB-MAPP Barriers Assessment. Similar to the Milestones Assess-ment, each area has an individual grid to write the score for the current testing administration. Individual scores are transferred to the Scoring Form which is comprised of 24 individual smaller grids, separated by each barrier area, and can document scoring information for four testing administrations (Sundberg 2008).
+
+The Transition Assessment. The scores from the VB-MAPP Transition Assessment should be represented on the Transition Assessment Scoring Form. The Transition Assessment provides a Likert scale (1–5) with scores ranging between 4 and 5 indicating that the child may benefit from opportunities in a mainstream learning envi-ronment with a low student to teacher ratio; how-ever, it is recommended to use behavioral technology in coordination with more typically teaching methods. Scores within the 0–2 range indicate that the child should primarily receive an individualized program with one-to-one direct instruction, use of behavioral technology, and suf-ficient oversight by qualified professionals (Sundberg 2014). Individual scores are trans-ferred to the Scoring Form which is comprised of 18 individual smaller grids, separated by each barrier area, and can document scoring informa-tion for four testing administrations (Sundberg 2008).
+
+Clinical Use
+The VB-MAPP assessment is designed to identify skill deficits for learners with speech and language delays that impact their learning, regardless of age and/or diagnosis. Although a comprehensive knowledge of applied behavior analysis (ABA) and B.F. Skinner’s Verbal Behavior (1957) are not required, it is important that the administrator understands verbal behavior at a basic level. Addi-tionally, assessors should be able to identify any unintentional influences on responding evoked by inappropriate prompting procedures. Information obtained through the assessment will be beneficial for parents and educators in determining the learner’s skill acquisition needs and barriers to learning, which guides the learner’s educational team with planning for the individualized educa-tional needs of the child.
+
+See Also
+▶Applied Behavior Analysis (ABA)
+▶Intraverbals
+▶Mands
+▶Reinforcement
+▶Skinner’s Verbal Behavior
+▶Tact
+▶Verbal Behavior
+
+Verbal Communication
+Andrea McDuffie
+MIND Institute University of California-Davis,
+Sacramento, CA, USA
+
+Synonyms
+Fluent speech; Functional speech; Spoken language
+
+Definition
+Verbal communication refers to the production of spoken language to send an intentional message to a listener. Verbal and nonverbal communication abilities are considered to represent a core deficit in the diagnosis of autism. Indeed, the presence of fluent spoken language (in the form of regular and nonimitative use of multiword utterances) during the preschool years is a robust predictor of posi-tive long-term outcomes for children with autism. In the research literature, the acquisition of fluent spoken language is sometimes referred to as func-tional speech. The domain of verbal communica-tion can be divided into several component areas: semantics (vocabulary), syntax (grammar), and pragmatics (the social uses of language). Often, pragmatics is the area of spoken language that is most challenging for individuals with autism.
+
+See Also
+▶Expressive Language
+▶Speech
+
+Verbal Comprehension
+Andrea McDuffie
+MIND Institute University of California-Davis,
+Sacramento, CA, USA
+
+Synonyms
+Language comprehension; Language understand-ing; Receptive language
+
+Definition
+Verbal comprehension is the ability to understand spoken language. Early in typical development, a relative advantage of verbal comprehension over spoken language ability is observed when lan-guage abilities are measured using both parent report and direct assessment measures. An atypi-cal profile of language comprehension and pro-duction has been reported for young children with autism by several groups of researchers. Two atypical profiles have been described. In one pro-file, the relative advantage of receptive over expressive language is decreased (i.e., the receptive-expressive gap is smaller) for children with a diagnosis of autism, but not PDD. In the other profile, young children with autism actually demonstrate lower age-equivalent scores for lan-guage comprehension than they do for produc-tion. Many common assessment instruments include subtests designed to measure verbal com-prehension of language, including the Vineland Adaptive Behavior Scales, the infant subscale of the MacArthur-Bates Communicative Development Inventory, the Preschool Language Scales, and the Mullen Scales of Early Learning. Other authors report that, in slightly older children with autism, verbal comprehension no longer lags behind expressive language in that standard scores for language comprehension are found to be commensurate with scores for spoken lan-guage. It has been suggested that the comprehen-sion of language in a conversational context may provide challenges for individuals with autism that are not present within a standardized testing context. For example, the need to respond to non-verbal cues that support semantic meaning may interfere with the verbal comprehension ability of individuals with autism.
+
+Verbal Comprehension Index
+Shirley Poyau
+Clinical Psychology, University of Massachusetts
+Boston, Boston, MA, USA
+
+Definition
+The Verbal Comprehension Index (VCI) is one of the indices representing the major components of intelligence in two of the Wechsler Intelligence Scales: the Wechsler Adult Intelligence Scale (WAIS) and the Wechsler Intelligence Scale for Children (WISC). It is thought to provide a rela-tively pure measure of verbal abilities, free of the influences of auditory attention and concentration. In the fourth edition of the WAIS (WAIS-IV), the VCI is comprised of three main subscales, with an additional supplemental scale: Similarities, which measures abstract verbal reasoning; Vocabulary, in which words must be defined singly, without context; Information, which tests general knowl-edge of history, art, culture, and politics; and Comprehension (supplemental), which measures the ability to understand abstract or idiomatic expressions. In the fourth edition of the WISC (WISC-IV), the VCI consists of the Vocabulary, Similarity, and Comprehension subtests, with Information and Word Reasoning, a task which provides clues leading to a specific word, as sup-plemental scales. Research has consistently shown that children and adults diagnosed with Autistic Disorder gen-erally obtain scores consistently lower on the VCI than they do on nonverbal indices of the WAIS. In contrast, individuals with Asperger’s disorder tend to earn their two highest subtest scores in Information and Vocabulary, reflecting the VCI as a relative strength.
+
+See Also
+▶Processing Speed Index
+
+Verbal Dyspraxia
+▶Verbal Apraxia
+
+Verbal Fluency
+Laura B. Silverman
+Department of Pediatrics, University of
+Rochester, School of Medicine and Dentistry,
+Rochester, NY, USA
+
+Synonyms
+Category fluency; Controlled oral word associa-tion; FAS-test; Letter fluency; Phonemic fluency; Semantic fluency; Word fluency
+
+Definition
+Verbal fluency refers to the ability to spontane-ously generate as many words as possible within a given time period, according to a set rule. There are two types of verbal fluency that are commonly tested: phonemic fluency and semantic fluency. During a phonemic fluency task, a person is asked to generate as many words as possible beginning with a specific letter of the alphabet. During a semantic fluency task, the person is asked to generate as many words as possible from a specific semantic category (e.g., animals), irrespective of which letter of the alphabet the words begin with. Typically, there is a 60 s time limit to complete individual verbal fluency trials, and performance is the sum of correct words gen-erated within the allotted time period. A number of different errors can be calculated, including repetition and intrusion errors. Two underlying abilities govern verbal fluency performance: clus-tering and switching. Clustering is the tendency to produce a set of words within a particular sub-category (e.g., farm animals on a semantic fluency task requiring animal words). Switching refers to the ability to shift from one subcategory to the next in order to maximize performance (e.g., shifting from farm animals to pets, once genera-tion of farm animals slows). Performance on ver-bal fluency tasks is associated with frontal and temporal lobe functioning. Verbal fluency is impaired in individuals with high-functioning autism when their performance is compared to people without ASD. Conversely, individuals with Asperger syndrome appear to have intact verbal fluency abilities.
+
+See Also
+▶Executive Function (EF)
+
+Verbal Intelligence
+Michelle Dawson
+Hôpital Riviére des Prairies, Centre de recherche
+du CIUSS du Nord de l’île de Montréal et
+département de psychiatrie de l’Université de
+Montréal, Montréal, QC, Canada
+
+Definition
+Verbal intelligence refers to specific human language-based skills which are considered to reflect latent general abilities. Despite historical disagreement about the precise place and funda-mental nature of verbal intelligence, widespread agreement about its importance is evident in its omnipresence across all major hierarchical models of human intelligence. A person’s verbal intelligence is assessed through performance on one or more specific tests involving receptive and/or expressive spoken language. While these tests assess a limited range of specific verbal abilities, they are also intended to estimate, or to contribute to an estimation of, a person’s general intelligence. Verbal intelligence tests contrast with performance or nonverbal intelligence tests, which may in fact require verbal skills (e.g., the comprehension of spoken instructions) but primarily are considered measures of other abilities, such as visuospatial perception or pro-cessing speed. Scores on verbal and performance intelligence tests can be combined to generate full-scale IQ scores. The use of vocabulary tests in assessing verbal intelligence is nearly universal. In these tests, individuals are asked to demonstrate their under-standing of single words, for example, by provid-ing a spoken definition, by indicating one of several presented pictures in response to a spoken word, or by naming a single picture. Other kinds of verbal intelligence tests, often administered in addition to vocabulary tests, require specific skills in a variety of areas. Examples include identifying associations between words (verbal abstract reasoning), providing factual responses to general knowledge questions, and demonstrat-ing comprehension of social conventions and practical “common sense.”
+
+As is evident in autism research, verbal intelli-gence scores can be derived in many different ways and from many different tests. A single test of vocabulary, such as the Peabody Picture Vocab-ulary Test (PPVT) or its variants, may be used. Alternatively, intelligence test batteries combine several verbal subtests to produce a verbal index score or Verbal IQ (VIQ), as in Wechsler Scales and the Stanford-Binet. Some intelligence test batteries also provide abbreviated versions in which verbal intelligence scores are derived from a vocabulary subtest either alone or com-bined with another verbal subtest. It is important to note that tests which assess verbal intelligence are culture specific and evolve over time with respect to their specific content. Thus, whether a person has or has not been educated within a specific culture affects their measured verbal intel-ligence. What kinds of tests are or are not included when measuring verbal intelligence may also be subject to change. For example, Wechsler scales subtests once included as measures of VIQ were later indexed as measures of working memory (e.g., tests of arithmetic ability and digit recall). Verbal intelligence scores can be reported as IQs or other standard scores, as percentiles, age equivalents, or raw scores. Developmental tests designed for infants and preschool children (e.g., Mullen or Bayley scales) as well as tests of adaptive behavior (e.g., Vineland) are commonly used to assess autistic individuals and include language-related domain scores. However, these reflect developmental or adaptive levels rather than verbal intelligence. Further, the content of some language-related items in developmental and adaptive tests may be similar to diagnostic signs of autism and therefore overlap with items in autism diagnostic instruments.
+
+Historical Background
+Unusual patterns of verbal abilities are promi-nent throughout the first formal descriptions of autistic individuals. None of the 11 children described by Leo Kanner responded typically when spoken to; a majority presented as though deaf. The eight children who developed speech (with or without delays) and the three classified as mute did not, in Kanner’s view, fundamentally differ in their failure to engage in appropriate verbal communication. The speaking children displayed immediate and delayed echolalia, reversed pronouns, and a lack of spontaneous or typically meaningful sentences. Words were used in idiosyncratic or rigid and literal ways. Strong rote memory was evident, as were verbal strengths in naming objects, using plurals and tenses, and reciting information within formats or categories (e.g., poems, psalms, the alphabet, lists of animals, and presidents). As Kanner (p. 243) observed, “long and unusual words were learned and retained with remarkable facil-ity.” In later papers, he acknowledged that seem-ingly irrelevant or nonsense utterances in fact conveyed meaning via metaphors and other atypical but decipherable verbal associations. While autistic children showed marked resis-tance to teaching, regardless they learned – displaying improvements over time including spontaneous nonechoed sentences with correct pronouns. Even “mute” autistics sometimes spoke in full sentences in emergencies (Kanner 1973).
+
+The four autistic children described by Hans Asperger had verbal abilities at least as idiosyn-cratic as those encountered by Kanner. They spoke like adults, responded unpredictably or inappropriately – if at all – when spoken to, and were nearly impossible to teach. Both Kanner and Asperger relied far more on description than test scores in assessing verbal abilities; both doubted that autistic children’s intelligence could be fairly estimated via conventional testing. In 1945, Scheerer et al. reported an extended case study of “L,” an autistic savant (calendar calculation, music) who excelled in uninstructed memory of word, letter, and number sequences; and of names, places, and events associated with dates (e.g., birthdays). Several attempts to mea-sure L’s intelligence were made over time with varying degrees of success. As a child L – when testable – achieved normal range IQs but as an adolescent his Stanford-Binet IQ was 50. Verbal subtest performance ranged from strengths in immediate memory (of digits and sentences) to limited or idiosyncratic performance in tests of analogies, comprehension, reasoning, and vocab-ulary. For example, he defined “gown” as “some-thing I have on” (p. 9). Aspects of information he processed well were deemed outlandish and irrelevant, while his unusual verbal abilities suggested that “rote memory without comprehen-sion of social contents and symbols may operate astoundingly well” (p. 14) to the point that impair-ments are concealed.
+
+Anticipating important directions in later research, Scheerer et al. also questioned Kanner’s view of autism as primarily affective. Instead they proposed that L’s strengths and deficits were underpinned by a fundamental inability to under-stand or form concepts, symbols, and abstrac-tions. Similar views of autism flourished in the 1960s and 1970s, as summarized in Wing and Ricks’ 1975 overview of language and communi-cation in autism. Intelligence measures, including verbal tests, were important in the emerging char-acterization of autism as a cognitive phenotype. Hermelin and O’Connor (1970) reported a series of seminal experiments in which autistic and non-autistic children were matched according to intel-ligence test scores. In a core group of autistic children whose mean age was 10 years, perfor-mance scores (Merrill-Palmer) were higher than verbal scores (PPVT). However, two children had verbal age equivalents above 10 years, well exceeding their performance scores. Hermelin and O’Connor found that autistic children orga-nize and recall verbal information atypically, with diminished dependence on meaning, categories, and context.
+
+Within the same time period, Rutter and col-leagues published a series of papers reporting a longitudinal study of 63 individuals originally identified as autistic or “psychotic” in the 1950s. In adolescence (mean age 15 years), Wechsler scores ranged from untestable to above average. Performance (PIQ), VIQ, and full-scale means for testable individuals were all in the 70s – and in turn higher than receptive vocabulary (PPVT) and adaptive ability (Vineland) scores. More autistics had higher PIQ than VIQ compared to the reverse pattern, with a Block Design peak and a wide scatter of subtest scores (Lockyer and Rutter 1970). The highest verbal subtest score was Digit Span, the lowest Comprehension. Rutter (1966) noted that on verbal tasks, many autistics were untestable but a minority in contrast achieved their best scores, including three indi-viduals where this unexpected pattern was “very marked.” Yet these three, one of whom excelled in math and music, still had low Comprehension scores and a history of “very backward speech development” (p. 73). Across the full autistic sample, 31 spoke by age 5 and 7 developed speech afterward, as late as age 11; one-third received no education whatsoever, while a majority received 2 years or less. Individual VIQ scores changed (both increases and decreases) on average 17 points from IQ approximations recorded at preschool age, but the group mean did not change.
+
+To investigate the extent and specificity of verbal impairments in the autistic cognitive phe-notype, Bartak et al. (1975) characterized and tested small groups of autistic and nonautistic language disabled (“dysphasic”) boys. All the children had PIQ scores of at least 70 and were matched on this measure. The autistic boys (mean age 7 years) were distinct from their controls in achieving mean Wechsler PIQ (94–97) which dramatically exceeded their Wechsler VIQ (67, when testable), PPVT (52), and Vineland (70) scores. Their distinctively scattered Wechsler subtest scores included a relative Block Design peak and a greater range within the verbal sub-tests, which included an extremely low Compre-hension score. Interpreting this and his earlier follow-up study, Rutter proposed a primary role for a severe distinctive verbal impairment in autism, with scattered PIQ subtest scores (e.g., high Block Design, low Coding) speculated to result from test differences in verbal loading. A follow-up study of the boys in Bartak et al. (1975) into young adulthood found the autistics were distinctive in their trajectory of verbal abil-ities (Mawhood et al. 2000). Over time, the autis-tics’, but not the nonautistics’, Wechsler VIQ increased significantly – a group average of 16 points, with individual increases up to 50 points. The number of autistics who could be tested on all Wechsler subtests doubled (from 9 to 18, out of 19), while the PIQ-VIQ discrep-ancy was no longer significant. No subtest scores were reported. On PPVT, the autistic group also did significantly better over time, largely due to losses in the controls. While mean differ-ences between the two groups had diminished, the autistic adults displayed much higher variation in scores on the verbal tests (Wechsler VIQ, PPVT).
+
+Starting in the 1980s, studies using false belief and other social-specific tasks proliferated, raising questions about appropriate matching strategies and instruments, and whether inconsistent results could be attributed to inadequate accounting for group differences in verbal intelligence (Bowler 2007, for an overview). Meanwhile, studies including autistic Wechsler subtest and PIQ-VIQ profiles slowly accumulated, with 16 collected in Siegel et al. (1996), who also reported a new study. Overall, the PIQ-VIQ discrepancy pattern was inconsistent across studies. This is unsurpris-ing given that the studies spanned 31 years, included very small samples across different age ranges, had different inclusion criteria, used dif-ferent test versions, and encompassed different diagnostic definitions and practices. Nevertheless, Block Design robustly stood out as the highest Performance subtest score (all studies) and Com-prehension was similarly robust as lowest Verbal score (all but one study). The 1980s also marked the gradual introduc-tion of Asperger syndrome as a separate autistic spectrum diagnosis, a change formalized in the 1994 DSM-IV, followed shortly by Klin et al. (1995) report of no VIQ-PIQ discrepancy in autism, but a large Wechsler VIQ over PIQ dis-crepancy in Asperger syndrome. Ehlers et al. (1997) reported a similar result, with an Asperger subtest profile dominated by Verbal peaks (Vocabulary, Similarities, Information) and low scores in the Performance subtests Coding and Object Assembly.
+
+Current Knowledge
+Empirical Findings from Verbal Intelligence Tests
+The diagnosis of autism is consistent with all possible levels of measured verbal intelligence. In other words, verbal intelligence in autism, as assessed via different standard measures, spans the full range from a VIQ of 150 or more to an assigned score of “untestable.” High variance with respect to specific verbal abilities is durably characteristic of autism within and across individ-uals, encompassing multiple dimensions, and including sometimes dramatic incongruities between high and low abilities. Addressing such high variance by creating autism or autistic spectrum subgroups is frequently invoked as a high priority, but as yet, little consensus as to valid subgroups has emerged. Widespread dis-agreement over how and whether to distinguish Asperger syndrome from autism has led to a wel-ter of inconsistent findings regarding cognitive profiles, including patterns of verbal intelligence scores, across the autistic spectrum. DSM-IV autism subgroups, including Asperger syndrome, are now lumped together as a single autistic spec-trum diagnosis in the DSM-5, where “with or without language impairment” is one of several specifiers but a history of speech delay is not considered. However, regardless of diagnostic confusion and numerous other confounds (see “Historical Background” above), some findings persist, including autistics’ difficulty with the Wechsler verbal subtest Comprehension. In a population-based study of school-aged children, Charman et al. (2011) found Comprehension scores signif-icantly below Wechsler subtest means across var-ious subgroups as well as in the full group of autistic children who were testable. No other find-ing involving Wechsler subtests achieved such consistency in the broadly defined autistic spec-trum sample. Another persistent finding is adap-tive ability scores which are lower than measured verbal intelligence, a discrepancy that can be extreme in autistic individuals with average or high VIQ scores. For example, in a clinic-based sample of autistic spectrum children and adoles-cents whose VIQ was at least 70, mean VIQ was 107 (range 70–150) in contrast with mean Vineland composite scores of 55 (range 25–79; Klin et al. 2007). Large individual variations in trajectories of specific verbal abilities over time have also been reported as characteristic of autism. In a follow-up to mean age 29, Howlin et al. (2004, p. 219) reported “considerable movement in ver-bal IQ levels over time” in some individuals, with a small group-level improvement. Of the 68 autistics tested, 14 fell into the normal range of VIQ in childhood, while 32 scored at this level as adults. Thirteen of 31 individuals who as children had verbal intelligence scores of less than 30, or could not be tested at all, scored significantly higher as adults – nine of them in the normal range. Neither verbal intelligence mea-sured in childhood nor development of speech by 5 years of age – a milestone popularly claimed to be crucial – was a good predictor of a range of adult outcomes. A later follow-up of the same sample to mean age 44 found high variance in VIQ-PIQ discrepancies (from VIQ 50 points lower than PIQ, to 38 points higher) and a “small but steady increase” in VIQ across timepoints in testable individuals (Howlin et al. 2014).
+
+In the same direction, Anderson et al. (2007) investigated verbal ability trajectories within childhood, starting from an original diagnosis of autism, PDD-NOS, or other developmental disability at age 2, to outcomes at a mean age of 9 years. Children were assessed with a mix of developmental and IQ tests. The main finding was higher variance in verbal ability outcomes in the autistic spectrum children, particularly those whose specific diagnosis was autism. In these children, “improvement can range from minimal to dramatic” (p. 594) with many trajectories col-lected at either extreme. While group mean verbal abilities were lower for autistic than nonautistic children, the exceptionally wide range of autistic outcomes was such that a greater number of autis-tic children achieved average level or better verbal intelligence scores at age 9. Case studies of autistic individuals further add to the picture of remarkable variance in verbal intelligence and apparent incongruities across numerous dimensions. Autistics who have little in the way of functional speech and are therefore classified as “nonverbal” may also have high mea-sured verbal intelligence. For example, Bonneh et al. (2008) report a Wechsler VIQ of 128 while Gernsbacher (2004) reports a PPVT standard score of 160, in case studies of minimally speak-ing autistic children tested respectively at 12 and 6 years of age. Conversely, a nonspeaking autistic adult is reported unscorable on PPVT, suggesting verbal intelligence so low it cannot be measured. However, he also has outstanding performance (estimated IQ of 140) on Raven’s Matrices – a general intelligence test which does not include verbal material yet for nonautistics requires verbal abilities. The same autistic adult demonstrates outstanding calculation skills whose acquisition, even at a much more basic level, would depend on verbal abilities in nonautistics (Anderson et al. 1999). An incongruity between very low or untestable VIQ and normal-range or high Raven scores has also been displayed by some autistic children (Dawson et al. 2007; Courchesne et al. 2015). The important question of how to fairly assess verbal intelligence in minimally speaking autistics, who in addition may have difficulty pointing, has belatedly appeared in the literature (Kasari et al. 2013), but studies are as yet sparse and preliminary.
+
+Underlying Abilities and Theoretical Accounts
+With respect to early development, both echolalia and hyperlexia are well established as character-istic of autism and both indicate that autistics acquire verbal abilities in an atypical way. Autistics may enter into language by first learning its complex structure rather than by first learning its meaning, as is the typical pattern. For example, evidence suggests an early atypical developmen-tal pattern of discrepantly high expressive versus receptive verbal abilities, at least in what has been considered the specific diagnosis of autism (e.g., Hudry et al. 2010). However, find-ings may be in part dependent on adaptive and parent-report measures, and again are within a context of high individual variance across all ver-bal measures. Beyond early development, where autistics’ verbal abilities may be difficult to assess, numer-ous findings have confirmed that autistics of all ages perceive, organize, and recall verbal information in atypical ways. Autistics are characterized by strong phonological processing of language and by enhanced perception of speech. In neither case is there a consistently straightforward or predictable trade-off with other aspects of verbal information processing. Exceptional absolute pitch for speech, for instance, has been found in an autistic individual with a characteristic delay in speech development – but the person went on to become fluent in numerous languages (Heaton et al. 2008). In addition, although autistics may organize and recall verbal information without using typi-cal categories, they are also capable of forming categories when instructed to. The typicality, or not, of category formation displayed by autistics thus has depended on specific task instructions (Bowler 2007). Similarly, autistics’ use of context when processing verbal information atypically encompasses both local (e.g., immediately proxi-mate) and global (e.g., sentence-level) contexts. This does not imply an inability to use wider contexts but does demonstrate that more immedi-ate contextual possibilities are not discarded via typically mandatory top-down processes (Henderson et al. 2011). In turn, this is consistent with the idiosyncratic use of language in autism, including unusual vocabulary and word associa-tions. Further in this direction, autistics show a consistent pattern of difficulty with episodic but not semantic verbal memory, and also diffi-culty with organizing verbal information into expected, typical narratives (e.g., Diehl et al. 2006). Autistics would therefore be expected to perform poorly on tests which require verbal information to be organized into conventional or “common sense” type narratives, as in the Wechsler Comprehension subtest. Overall, existing findings suggest that aspects of verbal information which automatically take precedence in nonautistic information processing hierarchies do not play the same role in autistics (Mottron et al. 2006). For autistic individuals, depending on circumstances and task demands, this may result in both advantages and disadvan-tages, which in turn are evident in uneven devel-opment and verbal intelligence profiles within and across individuals.
+
+Future Directions
+• Individuals diagnosed with neurodeve-lopmental genetic syndromes (e.g., fragile X, Williams syndrome) may also meet autism criteria on various instruments. Whether these individuals are in fact autistic is an increasingly important research question which cognitive profiles, including verbal intelligence mea-sures, may help address.
+• The challenge of fairly testing verbal intelli-gence in all autistics, including those with atypical or minimal speech, was apparent in the first published descriptions of autism. Despite progress in technology – touch screens, smart phones, tablets, applications – this area remains neglected.
+• New technologies should also be explored as opportunities for autistics to develop and dis-play verbal abilities. For example, given the opportunity, autistics may demonstrate their ability to learn and adapt technologies for communication in both expected and unexpected ways.
+• There has been little interest in the atypical kinds, quantities, and arrangements of infor-mation – including verbal information – from which autistics learn well. Availability (or not) of verbal information remains overlooked as a plausible factor in the high variance of verbal abilities in autism.
+• Autistics with excellent adult outcomes, but atypical development and patterns of specific verbal abilities, have appeared in the literature throughout autism research history. Yet these individuals, and the patterns of verbal ability associated with successful autistic outcomes, remain understudied.
+• The relationship between speech delay and later verbal intelligence, including in adult-hood, remains understudied. Whether longer or shorter or no speech delay, or for that matter precocious speech, consistently leads to better or worse verbal (and other) autistic outcomes is an important but neglected research question.
+• High variance in verbal intelligence is widely regarded as an obstacle to autism research which ideally should be eliminated through subgrouping. However, attempts to compose consensual autism subgroups have not been successful. One future direction is to consider high variance in verbal outcomes not as an impediment to research but as informative about the nature of autism and the possibilities of autistic individuals.
+
+See Also
+▶Nonverbal Intelligence
+▶Savant Skills (in Autism)
+▶Vocabulary
+
+Verbal Semantic Coherence
+Laura B. Silverman
+Department of Pediatrics, University of
+Rochester, School of Medicine and Dentistry,
+Rochester, NY, USA
+
+Synonyms
+Weak central coherence
+
+Definition
+Verbal semantic coherence refers to the ability to use the broader context of a story or sentence, and the semantic relationships between words to aid understanding and interpretation of spoken and written language. The concept of verbal semantic coherence stems from Weak Central Coherence Theory, which posits that individuals with autism spectrum disorder (ASD) have a cognitive style that involves detail-focused processing, rather than global or gist processing of information. In other words, people with ASD are more likely to see the individual trees rather than the forest. This detail-focused cognitive style has been observed while individuals with ASD engage in verbal tasks. Examples of impaired verbal semantic coherence in autism include difficulties using sen-tence context to interpret the meaning of homo-graphs and heteronyms (words with one spelling, but two meanings and two pronunciations), poor recall of semantically related words, difficulties with pragmatic language, and poor construction of a narrative.
+
+See Also
+▶Auditory Verbal Learning
+▶Pragmatics
+▶Semantic Memory
+▶Weak Central Coherence
+
+Verbalization
+▶Speech
+
+Versed
+▶Midazolam
+
+Very Early-Onset
+Schizophrenia
+▶Childhood Schizophrenia
+
+Vestibular Function in
+Children with Autism
+Ruth Van Hecke1 and Leen Maes1,2
+1Department of Rehabilitation Sciences, Ghent
+University, Ghent, Belgium
+2Department of Otorhinolaryngology, Ghent
+University Hospital, Ghent, Belgium
+
+Synonyms
+Equilibrium; Sense of balance
+
+Definition
+Vestibular function refers to the ability to maintain balance and postural control, a stabilized vision during head movements, and spatial orientation. The vestibular system is a complex sensorimotor system which comprises the peripheral vestibular apparatus or labyrinth, the postural muscles, visual system, brainstem, cerebellum, and the cortex. The peripheral portion of the vestibular system is located in the inner ear and consists of three semi-circular canals and two otolith organs (i.e., saccule and utricle) providing complementary information about rotational and translational head movements relative to gravity. It gives together with centrally integrated proprioception and visual stimuli an internal representation of the environment and movements through it. In addition, a stabilized vision during head movements and postural control are reflexively maintained by the vestibulo-ocular, vestibulospinal, and vestibulo-collic reflexes. In case of a dysfunction in this complex system, pos-tural imbalance and dizziness are often the most reported complaints in adults. In contrast, these symptoms are frequently underestimated in chil-dren, since children are often not able to commu-nicate their complaints properly and a vestibular impairment often has a different clinical course compared to that in adults. Nevertheless, when a vestibular dysfunction occurs at birth or in early stages of life, an impact on motor, cognitive, psychosocial, and educational performances may be expected.
+
+Similar to children with a severe vestibular dysfunction, children with autism often present with vestibular-related complaints such as avoidance behavior, gross and fine motor impairment, static and dynamic postural insta-bility, delayed motor milestones, and/or poor motor coordination. Additionally, compared to typically developing children, research indicated that postural performances in children with autism were found to be the most aberrant in conditions were they could rely on their vestib-ular input only, which is also suggestive for a vestibular deficit. Therefore, it has been suggested that a (peripheral or central) vestibu-lar dysfunction may contribute to the phenotype or behavioral features in a subset of children with autism spectrum disorder, which has been supported by studies using vestibular function testing in this population. Moreover, some chil-dren with autism are known to have a preoccu-pation with spinning objects or repetitive movements, such as spinning or shaking their head or have difficulties in integrating vestibular input. However, due to heterogeneous findings in studies mainly focusing on the function of the horizontal semicircular canals only (only one fifth of the peripheral vestibular system), a more extensive and objective elaboration on whether an accompanying vestibular dysfunc-tion is present in this population is highly recommended.
+
+See Also
+▶Sensory Impairment in Autism
+▶Vestibular System in Autism
+
+Vestibular System in Autism
+Edward Ritvo
+UCLA School of Medicine, Los Angeles, CA,
+USA
+
+Definition
+Vestibular System
+A system in the body that is responsible for maintaining balance, posture, and the body’s ori-entation in space. This system also regulates loco-motion and other movements and keeps objects in visual focus as the body moves. The vestibular system is comprised of the ves-tibulo-cochlear nerve, and those parts of the brain that interpret and respond to information derived from these structures.
+
+Historical Background
+Abnormal modulation of sensory input (over and under responding) has been a pathognomonic fea-ture of autistic disorder (AD) since it was first described by Kanner (1968). Of particular note is the fact that response levels may not remain static, but can alternate over time in the same individual. This characteristic, called “Perceptual Inconstancy” (Ornitz and Ritvo 1968) underlies the diagnostic criteria for AD listed in the DSM-IV-TR (American Psychiatric Association 2000) as, “stereotyped and repetitive motor man-nerisms” (e.g., hand or finger flapping or twisting, or complex whole-body movements). The pro-posed DSM V (American Psychiatric Association 2010) lists these symptoms as, “Hyper- or hypo-reactivity to sensory input or unusual interest in sensory aspects of environment; (such as apparent indifference to pain/heat/cold, adverse response to specific sounds or textures, excessive smelling or touching of objects, fascination with lights or spinning objects).” Abnormal modulation affects all sensory sys-tems (Ritvo 2006; Leekam et al. 2007; Schoen et al. 2009, DSM-IV-TR and proposed DSM 5, Reynolds and Lane 2008; Minshew and Hobson 2008), for example, many AD infants and children are initially referred for evaluation of possible deafness. These children may not respond at times to speech or loud noises, and at other times cover their ears and cry in response to soft sounds. Alternating sensitivity to temperature is shown in AD children who demand wool sweaters and heavy coats in the summer, but will only wear light skimpy clothes in the winter. Response to pain varies from ultra sensitive to nonexistent, even with major injuries. Visual detailed scrutiny may alternate with bumping into things as if they were not seen. Hypersensitivity to the texture of food is responsible for the often-reported behavior of spitting out lumpy items, a problem many par-ents solve intuitively by using a blender. And, lastly, many AD children become preoccupied with obtaining certain specific odors, while at other times will ignore noxious fumes.
+
+Current Knowledge
+Symptoms Due to Abnormal Modulation of Vestibular Input
+Under responsiveness to vestibular input leads to several types of repetitive behaviors. For example, many children and adults with AD spend hours each day on swings, jumping on trampolines, and twirling themselves around. They do this without becoming dizzy or nauseous, and report that it is “soothing” and “comforting” (Ritvo 2006). Staring at rotating objects such as phonograph records and fans, head shaking, body rocking, and finger flapping in front of the eyes are other com-mon ways AD individuals induce nystagmus and vestibular input. Over responsiveness to vestibular input leads to the opposite kind of behaviors. Maintaining rigid postures, placing themselves in unusual prone or upside down positions, refusing to get out of bed, and hiding in dark places are behaviors that AD individuals use to diminish vestibular input and maintain equilibrium. Symptoms due to both over and under modu-lation of vestibular input, like those in the other sensory systems, tend to decrease in frequency and duration with age, although, in some, they remain lifelong (Ritvo 2006).
+
+Neurophysiologic Studies
+Several neurophysiologic studies begun in the 1970s attempted to identify objective measures of abnormal vestibular processing. One pilot study identified a subset of AD children who showed decreased post rotator nystagmus (PRN) in the light, when visual fixation is possible, but not in the dark (Ritvo et al. 1969). However, replication studies failed to identify specific PRN differences between large groups of AD and age and sex matched non-AD children that could be of diagnostic significance (Ornitz 1970). A review of the recent literature failed to find subsequent stud-ies that identified specific pathology in the vestib-ular system (peripheral or central) pathognomonic of autism, but concluded that more research in this area is warranted by previous findings (Rogers and Ozonoff 2005; Reynolds and Lane 2008).
+
+Therapeutic Implications of Abnormal Vestibular Modulation
+Based on the clinical observations of Jane Ayers in the 1970s (Ayres 1974), occupational therapists and sensory integration therapists developed interventions aimed at assessing and improving symptoms of abnormal vestibular modulation. These interventions have gained worldwide appli-cation based on positive clinical experience, but to date no objective studies of their efficacy have been published (Kinnealey et al. 1995; Watling et al. 2001; Tomchek and Dunn 2007).
+
+Victimization
+Guillermo Montes
+St. John Fisher College, Rochester, NY, USA
+
+Definition
+There is no consensus on what victimization is or what it should cover. The dictionary definition includes victimization of crime, of unjust punish-ment and of fraud. Victimization is often classified by the source of the offense or the nature of the attack. Two forms of victimization receive dispro-portionate amount of attention by the research community, child abuse and peer victimization or bullying, while other forms of victimization are not systematically studied. This entry deals with those unstudied sources of victimization fol-lowing the dictionary definition of crime, unjust punishment, and fraud.
+
+Historical Background
+Although undoubtedly victimization of persons with ASD is as old as the disease itself, there has been and continues to be an almost complete paucity of research on the topic.
+
+Current Knowledge
+Much of what is known about criminal victimiza-tion is not specific to persons with ASD. People with disabilities are two to three times more likely to be victims of violent crime (rape/sexual assault, robbery, simple, and aggravated assault). In addi-tion, people with ASD are also at higher risk of financial exploitation (Volkmar et al. 2005). About 15% of crime victims with disabilities report that they suspect they were targeted because of their disability (Harrell and Rand 2010). Because of their inability to perceive or understand social clues, people with ASD have been called the “per-fect victims” (Debbaudt 2002). Victimization by direct caregivers (e.g., par-ents) is often treated as a form of child abuse, although such victimization may occur beyond the age of 18. Peer victimization has been studied repeatedly mostly among children with ASD in the school context (see “▶Bullying”). Little is known about peer victimization among adults with ASD. Victimization by educators and professional caregivers was recently documented in a report from the US Government Accountability Office on the use of seclusions and restraints that resulted in deaths or abuse (United States Government Accountability Office 2009). Given that there is no systematic research on victimization by school personnel, the prevalence of this form of victimi-zation is unknown. Additionally, persons with ASD can be victim-ized by law enforcement. When the police comes into contact with persons with ASD spectrum disorders because they are witnesses, perpetrators, or victims of crime, untrained officers can easily mistake ASD symptomatology for suspicious behavior. ASD symptomatology (e.g., absence of eye contact, repeating what an officer says, not understanding body language, not following commands, confessing to crimes they did not commit, persistent interest on aspects of a situa-tion others believe irrelevant) can be easily con-fused with indications of guilt by officers unfamiliar with ASD during informal questioning, arrest, and formal interrogation. Sometimes such mistakes may lead to criminal convictions of innocent persons with ASD disor-ders; other times the information provided by a person with ASD is thought to be unreliable (Debbaudt 2002). Consequently, the Federal Bureau of Investigation and a number of ASD advocacy organizations (in particular the Autism Society) are working with police and other first respondents to educate them about ASD (Debbaudt and Rothman 2001).
+
+As is the case for many other people with incurable diseases, many persons with ASD and their families become victims of those who mar-ket untested remedies and effective treatments or cures for ASD. Distinguishing between legiti-mate and responsible complementary and alter-native medicine (CAM) practice and quackery can be very difficult, particularly for desperate parents of children with ASD at a time when the use of CAM is increasing among the general population. Quackery is characterized by exag-gerated claims about the likely benefits of the product or service for the typical patient, as well as its higher likelihood to result in financial, emotional or physical harm, including unwarranted delays in seeking appropriate diag-nosis and treatment. In the United States, both the Federal Drug Administration and the Federal Trade Commis-sion have responsibility for consumer protection, yet both agencies lack adequate resources to police the many attempts to promote services and therapies of questionable value to people with ASD. In rare cases, the FDA or the FTC has sent warnings to marketers of unapproved therapies or products (e.g., unapproved chelation products (Federal Drug Administration 2010)). Yet, many CAM approaches are outside of the jurisdiction of the FDA and current regulatory practice in the United States requires the govern-ment to prove evidence of harm. Additionally, many of these approaches are not subject to adverse event reporting of any kind. Thus, it is unsurprising that many cases of misrepresenta-tion of alleged benefits are not investigated or prosecuted, as they often go unreported by the victims. As in other forms of victimization, there are no scientific studies on the prevalence and nature of financial, emotional, or physical harms that occur due to quackery, nor any studies to determine if people with ASD or their close relatives can distinguish between legitimate CAM and quackery. There are however many published accounts showing that quackery and charlatanism related to ASD therapies and “cures” is widespread (Fitzpatrick 2008; Offit 2008; Schreibman 2007). Finally, persons of ASD are also victims of fraud when researchers commit scientific mis-conduct on ASD-related studies. The most famous cause was the Andrew Wakefield’s 1998 study published in The Lancet expressing concerns that the MMR vaccination was linked to ASD. The dissemination of the claim through the media resulted in a loss of confidence in the MMR vaccination program in the United States and United Kingdom. For many families with children with ASD, the study also became a justification to seek treatment and cures outside of the medical establishment, thus delaying appropriate and timely treatment and exposing them to an increased risk of financial, emotional, and physical harm. In addition, many families decided not to vaccinate children who did not have ASD for fear of contracting ASD. In 2004, many of the Dr. Wakefield’s coauthors retracted the problematic interpretation (Murch et al. 2004). The UK’s General Medical Counsel erased Dr. Wakefield’s name from the medical register in 2010 and the original paper was sub-sequently retracted from the Lancet (Greenhalgh 2010).
+
+Future Directions
+There is no question that persons with ASD are at higher risk of various forms of victimization, yet scientific studies have not been systematically conducted to determine the prevalence of the var-ious types of victimizations for persons with ASD and their families. With the exception of bullying and child abuse, there are no systematic efforts to determine the prevalence, causes, and remedies of the various forms of victimization presented in this entry. People with ASD will continue to be at higher risk of these various forms of victimiza-tion until studies elucidate their plight.
+
+See Also
+▶Bullying
+
+Video Feedback and
+Feedforward
+Peter W. Dowrick1 and Nur‘aini Azizah2
+1University of Auckland, Auckland, New Zealand
+2Faculty of Psychology, Universitas Islam Negeri
+Sunan Gunung Djati, Bandung, Indonesia
+
+Definition
+Video feedback entails the replay of a recorded event with the intent that participants will learn improved behavior for that situation. For exam-ple, a child may be offered a choice of a toy bear and a toy truck with which to play, but then pushes the toys away without making a choice. Simply replaying the video would be feedback and prob-ably ineffective, so a replay would be used as an opportunity to teach an adaptive response. Thus the feedback itself might not be educational or therapeutic, but may serve to present “teaching moments,” which may be effective if develop-mentally appropriate. Video feedforward entails the portrayal of an event as a future success, beyond current capabil-ity, achieved through filming and editing tech-niques. That is, a person will see a video of themselves succeeding in something where they normally fail. For example, the child above would be also be offered, say, a truck, filmed from a different angle, maybe even prompted to say “thank you.” The separate shots are then edited together to show the child adaptively choosing and receiving a toy, in this case, a truck. So the “teaching moment” is shown on video and the feedback errors are not used. Thus feedback is effective when it allows feedforward to take place, either through addi-tional training or therapy or through appropriately recorded and edited video. With individuals for whom learning comes easily, just seeing and hear-ing the video may be enough to create spontane-ous feedforward and future adaptive responses to the event.
+
+Historical Background
+The first use of the term “feedback” was in the 1920s, in reference to that dreadful oscillation in sound systems turned in on themselves. Then feed-back progressed through physics and biology, later psychology and education, to reference information about the impact on the environment of systems and organisms, and then the potential influence of that information in a “feedback loop.” The Concise Oxford Dictionary (2006, p. 521) defines feedback in psychology and biology as the “modification or control of a process or system by its results or effects.” A common biological example is the blink reflex, by which the eyelid moistens a drying eyeball. Examples in psychology are seldom so clear cut. Feedback is routine in sports, where a video may be shown with analysis of what worked and more emphasis on what did not work. Any feedback may be disputed or have a negative emo-tional impact. To be used constructively, is it still feedback? Or does it become “feedforward?” Feedforward is even more recent terminology. Although the terminology was not used, the con-cept had its most famous exploration in the 1940s by British air force scientists, led by Norbet Wie-ner. London was being attacked by German air-craft who tried to make their approach unpredictable with sudden changes of speed and direction (see Galison 1994). Wiener developed systems to predict the unpredictable so that a missile could be launched to intercept the plane on its future path. It was not until the 1970s that cybernetics embraced feedforward (e.g., Lee 1980) and psychology flirted with it (Björkman 1972). In cognitive behavioral psychology, feedforward is recognized as a personal image of future successful behavior, not previously achieved (Dowrick 1991, 2012).
+
+Current Knowledge
+Video feedback. While there are many studies using different forms of video with individuals with autism, there are not many using video feed-back. For example, a review in 2005 (Ayres and Langone 2005) of 15 studies for social and daily living skills in children with autism, all used var-iations of video modelling, not feedback. There were peer and self-models, adults, multimedia, and point-of-view modeling. All were successful, although it should be noted that most included other strategies such as discussions and prompts. None used video feedback. More recently, a study by Robinson (2011) found video feedback effec-tive in interventions with the communication skills of four young boys with autism – when the video was used with paraprofessionals delivering the skills training – not used with the children. Lofthouse et al. (2012) list 19 “complementary and alternative treatments for autism,” from mel-atonin and B12 to acupuncture and massage. There is no mention of “video feedback,” either because it is “mainstream” rather than “alternative” or because there are no reports of it. The review notes the “most empirical support” for (mainstream) applied behavior analysis. How-ever, that requires “30–40 h of treatment per week for several years,” thus the impetus for alternative treatments. There are studies using oral or social feedback but with little promise (unless one considers rein-forcement “feedback”). For example, Bedford et al. (2013) found a failure to learn from social feedback in children with autism who had been held back in their vocabulary development around age 2 years. However, they did not mention the use of video or other possibilities. Reviews of video feedback with general clini-cal populations do not help with autism. For example, there are numerous reviews of Video Interaction Guidance (e.g., Kennedy et al. 2011) around issues of interest in autism – for example, family studies including child clinical partici-pants. But neither the data nor the references seem to mention “autism.” The authors do provide overall evidence that video feedback can be used effectively to improve parenting skills and parent–child interactions. It seems likely that video feed-back, when used constructively for parenting, classroom management, etc., can benefit children and adults with autism. But there is little evidence that video feedback can benefit individuals with autism directly for skills or behavior develop-ment, unless it provides feedforward.
+
+Video feedforward. Previous studies have dem-onstrated the effectiveness of video feedforward to improve various psychological conditions, for example, reading skills (Dowrick et al. 2006; Robson et al. 2015), sport performance (Ste-Marie et al. 2011), communication (Smith et al. 2014), and presentation skills (Murphy and Barry 2016). These are self-modeling interventions that acknowledge the procedure as “feedforward.” Most studies have focused on primary school-age children, often chil-dren with special needs, although some have included adults (see Dowrick 2012) and those with elite skills, such as gymnastics (Dowrick 1989) and swimming (Clark and Ste-Marie 2007). In a thesis study of four children aged from 9 to 17 years, Holly Smith produced remarkable results using video feedforward. The youth were all on the autistic spectrum, some severe, with serious phobias of dogs. At first, two were treated with peer models, two with VSM: the first showed no effects; the second, moderate effects. Then all videos were redone to feature feedforward – with immediate and dramatic results. Phobic reactions disappeared, replaced by enjoyment, in some cases patting the test dogs and one request to own a similar dog (Smith 2018). In all conditions, the speed of behavior change is most notable. In particular, the children with autism in the Smith et al. study, mentioned above, transformed their ability to use picture communi-cation in a matter of days or short weeks. Such communication had previously proved so chal-lenging, they had not gained skills in years of instruction. Dowrick et al. (2006) implemented video feedforward on children with special needs who have difficulties in reading. Robson et al. (2015) support Dowrick et al.’s (2006) findings by show-ing the benefit of video feedforward on reading fluency. The participants were 11 children who watched the edited video of themselves reading fluently. The findings showed that feedforward helped them to read more fluently, as measured by accuracy, comprehension, and speed. Ste-Marie and colleagues (2011) expanded the use of video feedforward to help children improving their performances in sport, including trampoline skills. Smith et al. (2014) used video feedforward to enhance the picture-based communication skills of two children with autism and one adult with Down syndrome in their social environments. Fur-thermore, Murphy and Barry (2016) showed video feedforward contributions in improving presenta-tion skills of university students. Those findings indicate that video feedforward has been used in various settings for target behaviors which show promise for further research and applications to expand the use in other psychological aspects and settings.
+
+Future Directions
+Recognition of feedforward will improve as more self-modeling studies acknowledge the “feedforward”/“positive self-review” distinction. Clearly, interventions with feedforward show remarkably superior speed and efficacy of behav-ior change. The literature will be notably advanced by the proper recognition of feedforward as distinct from the carefree or care-less reference to feedback as a catch-all category for all video or other recorded replay.
+
+Video Games and Violence
+Laurie A. Sperry
+Department of Psychiatry, School of Medicine,
+Yale University, New Haven, CT, USA
+
+Definition
+Adolescents with autism spectrum disorder (ASD) spend a considerable amount of their lei-sure time playing video games to the exclusion of other pursuits. Often the content of these games is violent. It is unclear what impact the consumption of violent video games (VVG) has on the behavior of adolescents with ASD or whether this results in increased risk for contact with the juvenile justice system (JJS).
+
+Historical Background
+Following the mass shooting of children and school personnel in Newtown, Connecticut, there is an emerging body of research specifically focused on video game use in children and ado-lescents with ASD. The shooter, Adam Lanza, was a young man with ASD who was known to play violent video games for several hours a day, including a first-person-shooter game entitled School Shooter (Sedensky 2013). The object of School Shooter (Checkerboard Studios 2011) is to murder as many schoolchildren and staff members as possible, using weapons similar to those uti-lized in the Columbine massacre. After complet-ing the shootings, the gamer is prompted to commit suicide before being apprehended by law enforcement. Adam Lanza walked into Sandy Hook Elementary School, shot and killed 20 schoolchildren and six staff members, and then committed suicide prior to being apprehended by police. Norwegian mass shooter Anders Breivik received a diagnosis of ASD after murdering 77 people. Breivik wrote in his 1500 page mani-festo that he utilized the video game Call of Duty: Modern Warfare 2 and World of Warcraft as train-ing videos for the attack. He photographed him-self dressed in garb emulating characters from the games including a military uniform, a hazardous material suit, and a special operations soldier. At his trial, he made a statement related to how his-tory would view him which was very similar in nature the script from the game Call of Duty. He reported playing both games for up to 15 h a day in preparation for the massacre (Breivik 2009). This raises the question, are violent video games serving as video model for mass shootings perpe-trated by people with ASD? Adolescents with autism spectrum disorders (ASD) spend approximately 4.4 h per day watching television and playing video games. This amount is greater than the time spent pursu-ing any other leisure activities (Mazurek and Wenstrup 2013). Parents reported considerable difficulty related to the behaviors displayed by their children with ASD when efforts were made to monitor and/or restrict access to media and video games. Longitudinal research by Willoughby et al. (2012) on the effects of VVG consumption on behavior in neurotypical adolescents suggests that continual violent video game play predicts the slope of aggression at a level reaching statistical significance. Moreover, sustained playing of violent video games reliably predicted higher levels of aggression over time. Their findings sug-gest that continual nonviolent video (NVG) game play does not significantly predict scores of aggression.
+
+Current Knowledge
+Vulnerability to the Impact of Violent Video Games
+Specific cognitive, social, and sensory functions are involved in the interaction with video game play (Durkin 2010). Each of these areas is impacted when a person has a diagnosis of ASD (DSM-5, APA 2013). Markey and Markey (2010) examined the personality traits that may make an individual more vulnerable to the effects of vio-lent video games (VVGs). These researchers pos-ited that preexisting dispositions increase the susceptibility of certain individuals to the impact of VVGs. Using the five-factor model of person-ality traits (McCrae and Costa 1999), they devel-oped a spherical model to determine how the confluence of specific traits may render a person most vulnerable to the deleterious effects of VVGs. The model suggested that three orthogonal traits were moderating variables of VVGs. These traits included high neuroticism, low levels of agreeableness, and low conscientiousness. It is possible that the corollary to this model in the population of people with ASD is ToM deficits, belief persistence, difficulties with syllogistic rea-soning, and difficulties with emotional regulation. As a result, they may be particularly susceptible to allegory or images of violent hyperbole, and it is possible that they may take the visual information presented in a VVG at face value and emulate it. Comorbid psychiatric disorders could poten-tially serve as a moderating variable exacerbating the person’s susceptibility to VVGs.
+
+Violent Video Games and Aberrant Behavior
+Anderson et al. (2004) completed a meta-analysis on the robust body of literature that demonstrates a relationship between consumption of virtual violence and actual aggression. The mechanism that accounts for this relationship is the subject of intense interest. A possible explanation for the relationship between VVG and increases in mea-sures of aggression is that virtual violence is abstract and therefore pleasurable, allowing for a level of moral disengagement with violent actions. Many gamers describe a lack of remorse over engaging in virtual violence and derive great pleasure from playing VVGs (Ladas 2002). As computer graphics become more sophisti-cated, the characters, both protagonists and antag-onists, become more identifiable as social entities. These enhanced graphics allow for a level of immersion that has been linked to heightened enjoyment in game play (Skalski et al. 2006). Some VVGs come with a “blood on” feature, which allows the gamer to control whether they see the results of their carnage. Research has dem-onstrated that the “blood on” condition results in higher scores on measures of aggression than the “blood off” condition (Farrar et al. 2006). Hartmann and Vorderer (2010) have hypothesized that enjoyment of virtual violence may be contin-gent upon maximizing rewarding aspects of the game (feelings of power and/or success) while minimizing negative costs (guilt). This cost-benefit equation is likely impacted by both the amount of play and the role violence itself plays within the game. High-use gamers may become inured to images of violence and the resultant suffering displayed within the game (Carnagey et al. 2007). Gamers who are able to justify their behaviors because the antagonist is engaging in morally reprehensible acts may feel absolved of the guilt and distress associated with acts of vir-tual violence (Opotow 1990). Another possible antecedent to this increase in aggressive behaviors may be the denial of humanness to the victim. Greitemeyer and McLatchie (2011) examined the role ascribing or denying humanness to the victim in video games played in measures of violence and aggression. Visual hyperbole is often used to portray the antagonists in VVGs. These larger-than-life characters are imbued with nonhuman characteristics which may lead to the gamer engaging in cognitive distortions, allowing themselves to justify the intensity of their game-playing violence to vanquish the enemy (Smith et al. 2003). Greitemeyer and McLatchie (2011) hypothesized that this dehumanization and justification of violence could transfer from the screen to real life through the type of moral disengagement that assuages guilt and provides a pathway for aggression toward victims (Bandura et al. 1996).
+
+These concepts of desensitization, feelings of power, and denying humanness to victims may have particular relevance to gamers with ASD. Many adolescents with ASD have been the vic-tims of bullying and marginalization (Little 2002). They spend the majority of their leisure time playing video games that may provide their sole source of feelings of empowerment. In the game One Life (Chalk 2015), an FPS game, players only have one life and losing it means they are locked out of the game forever. This adds an element of reality to game play heretofore unseen. Players also have the ability to extend mercy or humiliate their victims prior to vanquishing them. The game boasts that no other game will provide players with the feelings of power and control over others. Players are rewarded for ignoring the cries of pain, rage, and appeals for mercy from their victims. In an ultimate act of degradation, players can urinate on their victims. From a behavior analytic per-spective, the immediate and continuous schedules of reinforcement for aggression followed by inter-mittent schedules of reinforcement as the gamer advances through levels have the potential to build extremely durable aggressive mind-sets for the gamer with ASD. People with ASD often have a concrete and literal sense of right and wrong and as such may find it easier to morally disengage than the neurotypical gamer. The ability to morally disen-gage requires the gamer to deny humanness to the victim, an act that is facilitated by viewing the victim’s acts as repugnant, thus justifying the vanquishing of the victim. This abates feelings of guilt and distress associated with virtual vio-lence (Hartmann and Vorderer 2010). Greitemeyer and McLatchie (2011) com-pleted a series of two experiments designed to determine if type of video game (violent, neutral, pro-social) impacted measures of aggressive behavior. Playing VVG increased dehumaniza-tion toward others and was linked to increased measures of aggressive behavior. Specifically, the researchers found that players of VVG ascribed fewer human-centric emotions to others, reducing the opposing gamers, in a sense, to the level of animals, machines, or objects. This concept has interesting implica-tions for gamers with ASD, many of whom expe-rience difficulty in facial emotional recognition as the result of the social difficulties inherent in their diagnosis (O’Connor et al. 2005).
+
+The role of VVG as a factor in conferring increased risk of aberrant behavior in adolescents with ASD is unclear. The answer to this question may serve to explain the distress and problem behavior exhibited by people with ASD when efforts are made to place parameters on their gam-ing (Mazurek and Wenstrup 2013). Engelhardt et al. (in press) recently completed a study in which adults with ASD as well as adults who were neurotypical were randomized to different video game playing conditions: VVG versus NVG. Measures of aggressive thought accessibil-ity, aggressive affect, and aggressive behavior were taken following the consumption of VVGs. Com-pared with their neurotypical peers, adults with ASD were not differentially impacted by viewing VVG (Engelhardt et al. in press). These findings may have limited applicability to the current study as the adults were exposed to only three brief video game sessions (15, 10, and 10 min) for a total of 35 min of game play, and this figure is considerably less than the average 2.4 h of daily game play reported by parents of teenagers with ASD (Mazurek and Wenstrup 2013). Therefore, there may be something of a dose-response relationship in which long-term consumption of VVGs has a different effect on gamers with ASD than acute, short-term consumption. A study by Krcmar and Lachlan (2009) exam-ined the association between length of VVG play and aggression and found a positive relationship between habitual game play and physical aggres-sion. A study which examined the impact of VVG on measures of aggression found that there were significant increases from pre-exposure baselines after participants played first-person-shooter games (Barlett et al. 2007). Thus, long-term expo-sure to violent images may lead to an increase in aggression as the result of regularly primed aggressive mind-sets.
+
+Internet Addiction
+There is a growing body of literature on the Inter-net gaming disorder (IGD), which has been iden-tified in the emerging measures and models section of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a disorder that merits additional clinical research prior to being included in the main text (American Psychiatric Association 2013). People with IGD experience clinically significant levels of distress when limits are put on their gaming, but they also experience this distress when playing compulsively, which compromises other activities of daily life includ-ing work and school. In one of the largest studies to date (N ¼ 7069), Grüsser et al. (2006) found that nearly 12% of study participants met diagnostic criteria for addiction (including craving, feelings of relief when playing, and increasing game play) relative to their gaming behaviors. Based on their responses to an online questionnaire, participants fell into two groups: pathological gamers who played an average of 4.70 h per day and non-pathological gamers who played an average of 2.49 h per day. There were statis-tically significant between-group differences, with the pathological gamers reporting greater anticipated relief of withdrawal when playing. They also demonstrated statistically significant higher levels of craving than non-pathological gamers. Addiction may be the result of the neu-rological response to the positive reinforcement inherent in the games and the success the indi-vidual experiences while playing. During their time away from gaming, they experience feelings associated with drug withdrawal.
+
+Circumscribed interests may be part of the constellation of characteristics that can render people with ASD more vulnerable to the impact of VVG. Klin et al. (2007) first posited the idea that video game addiction may be an example of a circumscribed interest. In a study by Romano et al. (2014), the relationship between scores on the autism spectrum quotient questionnaire (AQ; Baron-Cohen et al. 2001), scores on the Spielberger trait anxiety inventory (STAI-T; Spielberger 1983), scores on the Beck depression index (BDI; Beck et al. 1961), and scores on the Internet addiction test (IAT; Young 1998) were examined. The IAT measures the extent to which activities of daily living are compromised by time spent on the Internet. Statistically, significant rela-tionships were found between scores on the AQ, STAI-T, and degree of Internet addiction. People with higher AQ scores, meaning they had more autistic traits, tended to score high on the IATwith anxiety serving as a moderating variable. Interest-ingly, participants with high AQ scores and low STAI-T scores reported the highest levels of Inter-net addiction. As the IAT does not specifically measure how time is spent on the Internet, it is difficult to determine if the participants with high AQ scores and high anxiety were pursuing special interests, engaging in social media, or playing VVGs. In other words, some of the aforemen-tioned activities may have a soothing effect while others may serve to escalate anxiety. Mazurek et al. (2012) completed a study based on data from the National Longitudinal Transition Study-2 (NLTS-2). The vast majority of adoles-cents with ASD (64.2%) spent the bulk of their free time consuming television and video games. When video games and television viewing were separated, 41.4% spent the greater part of their free time consuming video games. When com-pared to adolescents with other disabilities, teens with ASD spent more time playing video games, and these differences reached the level of statisti-cal significance. The amount of consumption cre-ated considerable interference with their activities of daily living and functioning. Only 28% of neurotypical teens qualify as high consumers of video games compared to 60.3% of adolescents with ASD. The greatest predictors of high use for teens with ASD included a higher IQ and access to technology in their homes.
+
+Game Genres
+In 2014, half of the ten top-selling video games were classified as VVG (Kain 2015). These include first-person-shooter games (FPSG) and role-playing games (RPG). First-person-shooter games (FPSG) allow gamers to immerse them-selves into a three-dimensional world with the console screen serving as their means of experiencing the action from the point of view of the shooter (Schneider 2004). Contrary to popular belief, FPSGs are not always played in isolation. A study by Jansz and Tanis (2007) found that the majority of gamers who played FPSGs (80%) joined a clan, which allowed them to collaborate and compete with gamers at simi-lar skill levels. This suggests there is a social motive to the play of neurotypical gamers, though it is unclear if people with ASD play FPSG in the same way or for the same purposes. Barlett et al. (2007) found significant increases in levels of aggression in participants after playing FPSGs for a mere 15 min. They propose that priming for aggression occurs when a person is exposed to VVG which triggers aggressive thoughts and ultimately aggressive actions. This high level of primed aggression is exacerbated by the amount of blood and gore that accompany an aggressive action within the game. Role-playing games (RPGs) differ from FPSGs in that they allow the person with ASD to immerse themselves in the game by taking on an avatar within a fantasy world, setting out on quests, and exploring against the backdrop of a story line or dialogue. Gamers who engage with RPGs tend to play longer (23–25 h a week versus 16 h per week for FPS gamers) (Ng and Wiemer-Hastings 2005; Jansz and Tanis 2007) and develop substantial attachments to their character (Lewis et al. 2008). These character attachments are formed as the gamer’s actions shape the devel-opment of the story line, and the gamer becomes more immersed and integrated with his/her avatar. In their study on character attachment, Lewis et al. (2008) found that gamers with stronger character attachments were motivated by fantasy seeking, social interaction, and diversion. This raises a question regarding the degree to which a gamer with ASD would form attachments with a charac-ter in an RPG (Kowert and Oldmeadow 2014). Does a competent avatar allow a person with ASD to feel more competent and less socially anxious? Does the avatar become the surrogate friend in the absence of real-world friendships, thus limiting real-world social experiences and exacerbating social impairments?
+
+Mazurek and Engelhardt (2013) examined the relationship between video game play and scores on measures of problem behavior in a sample of 169 boys with a diagnosis of ASD. Video games were identified as fitting into the category of RPG or FPSG. In this study, the amount of video game consumption did not demonstrate an association with maladaptive behaviors. However, there was a statistically significant positive relationship between type of game (RPGs) and scores on measures of oppositional behavior. There was also a positive relationship, though not reaching the level of statistical signif-icance, between FPSGs and oppositional behaviors. Additionally, there was a statistically significant positive relationship between FPSGs and addictive gaming behaviors. Chung et al. (2015) examined the effects of active video game play (AVG) on the social behaviors of children with ASD. In this single-subject design study, children with ASD played with their siblings with measures taken on joint positive affect, reciprocal conversation, and aggression. Primary analysis suggested that AVGs have an inconsistent effect on the sociali-zation of children with ASD and their siblings. Of particular interest, however, was a finding in the supplementary analysis of the augmented reality (AR) condition. The AR was originally conceived to provide a bridge between sedentary and active game play and to introduce the more cooperative style of play with the sibling. Rather than using an avatar, the AR condition used a silhouette or photo-realistic image of the gamer. The AR con-dition was statistically significantly more pre-ferred by the majority of participants with ASD, and they reported a more positive experience with the AR condition.
+
+Violent Video Games as Video Models
+Social learning theory (Bandura and Walters 1977; Bandura 1997) has demonstrated the impact of modeling on children’s acquisition of social, emotional, behavioral, and functional skills. According to Bandura’s social learning theory, children pay particular attention to models who share their salient demographic characteristics and models perceived to be competent. These models can continue to influence the child beyond the context of the original observation and do not require reinforcement for behavioral modeling to occur. Social learning theory may explain why the AR conditions in the Chung et al. (2015) study were preferable to the children with ASD. It may also begin to explain the underlying mechanism for VVGs as potential video models of aberrant behavior in adolescents with ASD. Ferguson and Kilburn (2010) have raised the issue of selection to explain the relationship between VVG play and aggression. It is possible that peo-ple who are naturally more aggressive play more violent video games. Przybylski et al. (2009) found that while the level of violence in a video game did not enhance enjoyment, those with higher scores on measures of trait aggression strongly preferred VVGs over NVG, and this was strongly predictive of their future choices of VVGs. Alternately, violent video games may result in increases in aggressive behavior. There is ample research that supports the finding that observation of violence in the home, community, and school is detrimental to children and increases their risk of engaging in violent behavior (Cummings et al. 2010; Henrich and Shahar 2013). Bushman and Huesmann (2013) support the position that there is an interaction between aggression-prone personalities and social cogni-tive learning, including observational learning. Randomized, controlled studies have demon-strated that decreasing access to media violence and teaching children rules against mimicking violence they see have both shown a decrease in aggression (Moller et al. 2012). Video modeling is an evidence-based strategy utilized to teach pro-social behaviors to people with ASD that capitalizes on the strong visual learning skills of this population. Point-of -view modeling enables the person with ASD to see what the behavior looks like from the standpoint of the person engaging in the target behavior. These pro-social behaviors can include ending a conversation appropriately, asking for help, or entering into an ongoing conversation (Bellini and Akullian 2007). Video modeling allows for the repeated and consistent use of models that do not vary from time to time as they would likely do with a live model and has been successful in teaching and maintaining imitation skills in chil-dren with ASD (Cardon and Wilcox 2011). Video modeling is an effective tool for teaching pro-social behaviors, though it is not clear to what extent VVGs are impacting aberrant behavior by providing antisocial models for adolescents with ASD. Strong visual skills, circumscribed inter-ests, difficulties in syllogistic reasoning, deficits in ToM, and problematic/addictive gaming behav-iors may serve as a confluence of vulnerabilities rendering the adolescent with ASD vulnerable to emulating the behaviors viewed during hours of VVG playing.
+
+Future Directions
+Future research should consider the factors that may be contributing to mass shootings perpetrated by young men with ASD and comorbid psychiat-ric disorders who spend significant amounts of time playing VVGs. Does their propensity toward visual learning make them more vulnerable to violent images? Are deficits in perspective taking that make it difficult for them to consider how their behavior impacts others, being exacerbated by the dehumanization of victims within video games? Is this violent modeling generalizing to real-life behaviors? Are some people with ASD more impacted by violent images than others and if so, what variables moderate that impact? Are the feelings of empowerment provided by video games serving to galvanize aberrant behavior? It is incumbent upon the ASD community, including people with ASD, advocates, families, and professionals, to demonstrate an understand-ing of the impact of violence consumption on people with ASD through the development of reasonable game play guidelines. Components of VVGs that may make them appealing to the player (pace, level of competitiveness, graphics, fre-quency of rewards) could be identified, and alter-native games could be substituted that would still allow the person with ASD access to an equivalent gaming experience without subjecting them to graphic violence. This may serve as a mechanism for families to provide acceptable gaming alterna-tives thus avoiding the resultant problem behav-iors that come with placing restrictions on their video game play. While these questions are being investigated, we must consider the amount and type of violence adolescents with ASD are routinely consuming and work toward developing nonviolent alterna-tive interests both within and outside of the digital world.
+
+See Also
+▶Aberrant Behavior Checklist
+▶Internet Safety
+▶Video Modeling/Video Self-Modeling
+▶Violence and ASD
+
+Video Games, Use of
+Frederick Shic
+School of Medicine, Yale Child Study Center,
+Yale University, School of Medicine, New Haven,
+CT, USA
+
+Definition
+Video games are electronic media with three key attributes: they display content visually, they are interactive, and they are typically used for enter-tainment or have entertainment-like qualities. Video games encompass a wide array of formats, applications, and designs, ranging from simple hand-held gaming devices depicting black and white rasterized characters, to fully immersive virtual reality simulators. In general, video games are separate from elec-tronic screen media, because electronic screen media do not necessarily require an interactive component. Video games are separate from computer-assisted instruction and computer-based intervention, as video games are typically associated with entertainment, and computer-assisted instruction and computer-based interven-tion involve a therapeutic or didactic element or intention. Similarly, a distinction can be drawn between software, in general, and video games (which can be a specific type of software), as the goal in most software is typically to facilitate an external goal in the most efficient way possible (e.g., summing numbers, looking up information on the internet, or reading an Encyclopedia of Autism entry); the goal for video games typically lies within the game itself or is defined by the game (e.g., to “win” the game, to solve a puzzle, or to “beat” another player). It is important to note that the line between video games and other forms of interactive elec-tronic media is somewhat indistinct. It could be argued that some computer-based interventions are, in fact, video games, since they rely on games (in particular, other video games) as a basis for design (e.g., the Memory game, played with emotional faces, for individuals with autism). Nevertheless, the term “video game” is sometimes avoided by researchers who wish to highlight the advantages and potential of this media format while avoiding the perceived negative connota-tions associated with, for example, video game violence or obsessive video game play that serves no positive remedial goal.
+
+Historical Background
+History of Video Games
+One of the first reports of video games was filed in a patent, “Cathode-ray tube amusement device,” in 1947 by Goldsmith and Ray (1948; Mitra 2010). This patent described a game system in which a cathode-ray tube was used as the face of the console, displaying a single moving point, representing a missile fired toward drawn figures overlaid upon the tube. Over the next two decades, other video games slowly emerged, driven by advancements in computer technologies and increasing sophistication with computer programming. Notable games during this time included “OXO,” a tic-tac-toe program written as part of a University of Cambridge Ph.D. dis-sertation, by Alexander Douglas in 1952; “Tennis for Two,” a tennis game played on an oscilloscope screen at Brookhaven Nation Labs, by William Higinbotham in 1958; and “Spacewar!,” a two-player vector-based space combat game designed to showcase the Digital Equipment Corporation’s recently developed computer, the PDP-1, by MIT students Steve Russell and others in 1962 (Burnham 2001; Donovan 2010). Through the 1960s, video games were primar-ily viewed as sophisticated but impractical nov-elties, due to their size and expense. However, as the cost and size of electronics and microproces-sors began to rapidly decrease, video games became more viable as a form of entertainment. In the 1970s, beginning with the successes of coin-operated arcade systems such as Pong, video games came into vogue and quickly became significant segments of the entertain-ment market (Donovan 2010). The late 1970s saw commercial successes by companies such as Atari, and by the early 1980s video games had moved into the home in the form of consoles and personal computers, ushering in the modern era where video games have never diminished from public consciousness (Donovan 2010; Kent 2001). Video games have continued to innovate and redefine themselves (Donovan 2010). In the early 2000s, internet games became popular, allowing players to participate in games with tens or thou-sands of other players at the same time. Simulta-neously, mobile devices like cell phones began to allow gaming on devices that were cheap and portable and therefore ubiquitous. Today, this interconnectivity and ubiquity of access define some of the most modern aspects, and modern challenges, of video game technology.
+
+Types of Video Games
+The taxonomy of video games is a complex sub-ject (e.g., see Apperley 2006; Wolf 2002). How-ever, one useful classification scheme involves the discussion of video game “genres.” A review by Yuan et al. (2010) identifies the following (non-exhaustive and non-mutually exclusive) cat-egories of games: (1) first-person shooter, (2) strat-egy games, (3) sports games, (4) role-playing games, (5) puzzle games, (6) racing games, (7) dance/rhythm games, and (8) adventure games. Another distinction in video games is between “serious video games” versus “non-serious video games.” Serious video games represent games intended to fulfill a role other than (or in addition to) pure entertainment, with the typical assump-tion that they will foster some specific skill acqui-sition or broader learning (Rego et al. 2010; Susi et al. 2007). Others, however, argue that such a distinction is artificial, and that even games not originally intended to be “serious” can be used for serious intentions, such as education

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+Preface to Second Edition
+Eight years have now passed since the first edition of this Encyclopedia. During that time the field has continued to grow – almost exponentially in some areas! In doing this second edition of the Encyclopedia, we are mindful of the growth as well as some of the advantages for updating entries and including new ones in this much used reference work. It has been gratifying to see this resource being heavily used with around 400,000 downloads since it first appeared!
+In this second edition we have added nearly 400 new entries and updates on over 450 previous entries reflecting activity in the field since the first edition. As with the first edition we have attempted to be comprehensive in scope with entries on a range of topics including not only research issues but biographies of important contributors to the field, legal and social policy issues, educational, behavioral, and medical interventions, treatments, and advances in basic sciences of behavior, communication, neurobiology, genetics, epidemiology, and so forth. For this edition, we have also included a new set of entries on countries giving brief overviews of the history of autism work and the current state of the field in both developed and developing countries. This latter group of entries also reflects the growing interest in autism around the world specifically in developing countries where infrastructure for both service, teaching, and research has become increasingly important. With the addition of our new entries, we have reached nearly 1800 entries in total.
+As with the first edition we hope that this work provides an invaluable resource for parents, students, educators, researchers, and professionals alike. Even though these volumes appear in hard copy, in this new second edition we continually update entries and add new ones as these are needed. For this addition, I particularly thank our supporters at Springer – Judy Jones, Tina Shelton, and Sindhu Ramachandran, at Yale my helpful assistants Lori Klein and Monica Mleczek, and at the Autism Center at Southern Connecticut State University my assistant Eileen Farmer. I also particularly thank my Associate Editor Dr. Michael Powers who has assumed an important leadership role in the production of this edition. All of us hope you find this unique resource a valuable and helpful one. We are delighted to welcome you to this second edition.
+New Haven, CT, USA
+Fred R. Volkmar
+September 1, 2020
+
+Preface to First Edition
+Why an encyclopedia of autism? There are several answers to this question. They include the need to provide a comprehensive and current guide to the diverse knowledge now available. There has been a significant upsurge in research in autism during the past two decades. Several hundred papers were published in 1991 compared to more than 2,000 articles during 2011. The quantity of research (not even counting non-peer-reviewed publications) has increased so dramatically that it is difficult, if not impossible, for researchers and clinicians to keep up. Access to a reference work that provides an introduction to relevant information is clearly needed.
+Although several excellent handbooks and textbooks have been published in recent years, these are, almost intrinsically, fated to become increasingly out of date more and more quickly. Fortunately, many of the same technological advances that have been adapted for use with individuals with autism have uses for those of us who support them. The ability to produce both a print reference work as well as an online version with additional content was a major attraction for us in undertaking this project. It also can be updated easily and will have additional content. The electronic format also provides for an extensive cross-referencing system, which is designed to facilitate rapid searching and information retrieval.
+With contributions on a range of topics from leaders in the field, this reference work breaks new ground as a resource. The Encyclopedia contains several thousand entries relevant to autism and related conditions, including new research findings; entries on development and behavior; assessment methods and instruments; treatments and educational interventions; biographies of leaders in the field; and information relevant to epidemiology, social policy, and treatment planning.
+Both I and the associate editors of this work hope that you will benefit from using the encyclopedia and welcome your feedback. By the time the print publication of this work appears, the online edition will already have had entries added reflecting new knowledge in various areas. We hope that this resource enhances the work of clinicians and researchers alike.
+New Haven, CT, USA
+Fred R. Volkmar M.D.
+September 2012
+
+About the Editor
+Fred R. Volkmar is the Irving B. Harris Professor of Child Psychiatry, Pediatrics, and Psychology at the Yale Child Study Center, Yale University School of Medicine, and the Dorothy Goodwin Family Chair of Special Education at Southern Connecticut State University. An international authority on Asperger’s disorder and autism, Dr. Volkmar was the primary author of the DSM-IV autism and pervasive developmental disorders section. He has authored several hundred scientific papers and has coedited numerous books, including Asperger Syndrome, Healthcare for Children on the Autism Spectrum: A Guide to Medical, Nutritional, and Behavioral Issues, and the recently released third edition of the Handbook of Autism and Pervasive Developmental Disorders. He serves as associate editor of the Journal of Autism, the Journal of Child Psychology and Psychiatry, and the American Journal of Psychiatry. He also serves as co-chairperson of the autism/MR committee of the American Academy of Child and Adolescent Psychiatry. Since 2007 he has served editor of the Journal of Autism and more recently of the Encyclopedia of Autism.
+
+List of Field Editors
+George M. Anderson Laboratory of Developmental Neurochemistry, Yale Child Study Center, New Haven, CT, USA
+Nirit Bauminger-Zviely School of Education, Bar-Illan University, Ramat-Gan, Israel
+Susan Y. Bookheimer Cognitive Neuroscience, UCLA School of Medicine, Los Angeles, CA, USA
+Alice S. Carter Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
+Tony Charman Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
+Katarzyna Chawarska Yale Child Study Center, New Haven, CT, USA
+Joshua J. Diehl Child and Adolescent Services, LOGAN Community Resources, Inc., South Bend, IN, USA
+Peter Doehring ASD Roadmap, Chadds Ford, PA, USA
+Andrew L. Egel Dept. of Counseling, Higher Education and Special Education, University of Maryland, College Park, MD, USA
+Inge-Marie Eigsti Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Ruth Eren Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
+Adam Feinstein Autism Cymru and Looking Up, London, UK
+Eric Fombonne Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
+Michael Fitzgerald Department of Psychiatry, Trinity College, Dublin, Ireland
+Grace Gengoux Child and Adolescent Psychiatry, Stanford University, Stanford, CA, USA
+Howard Goldstein College of Behavioral and Community Sciences, University of South Florida, Tampa, FL, USA
+Francesca Happé SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
+Pamela Heaton Department of Psychology, University of London, London, UK
+Patricia Howlin Institute of Psychiatry, Psychology and Neuroscience King’s College, London, UK
+Susan Hyman Developmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA
+Gagan Joshi Psychiatry, Massachusetts General Hospital, Boston, MA, USA
+Connie Kasari Human Development and Psychology GSE&IS, Center for Autism Research and Treatment Semel Institute, UCLA, Los Angeles, CA, USA
+Lauren Kenworthy Department of Pediatrics, Neurology, Psychiatry, George Washington University Medical School, Center for Autism Spectrum Disorders, Division of Pediatric Neuropsychology, Children’s National Health System, Rockville, MD, USA
+Robert L. Koegel Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
+Ronald Leaf Autism Partnership Foundation, Seal Beach, CA, USA
+Ann S. Le-Couteur Population Health Sciences Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK
+Luc Lecavalier Nisonger Center, Ohio State University, Columbus, OH, USA
+Rachel Loftin AARTS Center, Rush University Medical Center, Chicago, IL, USA
+James W. Loomis Center for Children with Special Needs, Glastonbury, CT, USA
+Catherine Lord UCLA, Los Angeles, CA, USA
+Christopher J. McDougle Lurie Center for Autism, Massachusetts General Hospital, Lexington, MA, USA
+James C. McPartland Yale Child Study Center, New Haven, CT, USA
+Nancy J. Minshew Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA, USA
+Thomas Morgan Vanderbilt Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Nashville, TN, USA
+Hope Morris Communication Sciences and Disorders, University of Vermont, Burlington, VT, USA
+Paul A. Offit Division of Infectious Diseases, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
+Kristen M. Powers Cognitive Behavioral and Occupational Therapy Services, CCSN, Glastonbury, CT, USA
+Kevin A. Pelphrey Yale Child Study Center, New Haven, CT, USA
+Patricia Prelock University of Vermont, Burlington, VT, USA
+Brian Reichow University of Florida, Gainesville, FL, USA
+Lawrence Scahill Children’s Healthcare of Atlanta, Marcus Autism Center, Atlanta, GA, USA
+Tristram Smith Department of Pediatics, University of Rochester Medical Center, Rochester, NY, USA
+Wendy L. Stone Department of Psychology, UW READi Lab, University of Washington, Seattle, WA, USA
+John W. Thomas Quinnipiac University School of Law, Hamden, CT, USA
+Geralyn Timler Speech Pathology and Audiology, Miami University, Oxford, OH, USA
+Rutger Jan van der Gaag Department of Psychiatry and Karakter University Center for Child and Adolescent Psychiatry, Radboud University Medical Centre, Utrecht, The Netherlands
+Ernst O. VanBergeijk Threshold Program, Lesley University, Cambridge, MA, USA
+Gerrit van Schalkwyk Butler Hospital, Brown University, Providence, RI, USA
+Ty W. Vernon Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA
+Giacomo Vivanti Early Detection and Intervention Program, AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
+Deborah Weiss Department of Communication Disorders, SCSU Faculty Senate, Judaic Studies, Southern Connecticut State University, New Haven, CT, USA
+Jeffrey J. Wood Department of Psychiatry, UCLA/Geffen School of Medicine, Los Angeles, CA, USA
+Marc Woodbury-Smith Translational and Clinical Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
+Sara J. Webb Seattle Children’s Research Institute, University of Washington, Seattle, WA, USA
+Mary Jane Weiss Institute for Applied Behavioral Sciences, Endicott College, Beverly, MA, USA
+Virginia C.N. Wong Division of Paediatric Neurology, Developmental Behavioural Paediatrics and Paediatric Neurohabilitation, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China
+Tristram Smith: deceased.
+
+Associate Editor
+Michael Powers The Center for Children with Special Needs (CCSN), Glastonbury, CA, USA
+Yale Child Study Center, Yale University School of Medicine, New Haven, CA, USA
+
+Contributors
+Benjamin Aaronson Psychiatry and Behavioral Sciences, UWAutism Center, University of Washington, Seattle, WA, USA
+Ahmed A. Abdel-Rahman Department of Neuropsychiatry, Faculty of Medicine, Assiut University, Assiut, Egypt
+Sebiha M. Abdullahi Child Study Center, Yale University, New Haven, CT, USA
+Amy Accardo Department of Interdisciplinary and Inclusive Education, College of Education, Rowan University, Glassboro, NJ, USA
+Pasquale Accardo Virginia Commonwealth University, Richmond, VA, USA
+Silvia Adaes Quinnipiac University School of Law, Hamden, CT, USA
+Catherine Adams Human Communication Development and Hearing/School of Health Sciences, University of Manchester, Manchester, UK
+Gail Fox Adams Department of Applied Linguistics, University of California, Los Angeles, CA, USA
+Lynn Adams New Orleans, LA, USA
+Ryan Adams Division of Developmental and Behavioral Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
+Ayodola A. Adigun Yale Child Study Center, New Haven, CT, USA
+Albert J. Solnit Children’s Center, Middletown, CT, USA
+Ralph Adolphs Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
+Bill Ahearn The New England Center for Children, Southborough, MA, USA
+Rashid Akbari Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Abdulrahman A. Al-Atram Department of Psychiatry, College of Medicine, Majmaah University, Majmaah, Kingdom of Saudi Arabia
+Mohamd A. Alblihed Department of Medical Biochemistry, School of Medicine, Taif University, Taif, Kingdom of Saudi Arabia
+Lilia Albores Gallo Research in Genetic, Clinical and Community Epidemiology, Hospital Psiquiátrico Infantil Dr. Juan N. Navarro, México City, Mexico
+Kimberly Aldinger Department of Cell and Neurobiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
+Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA, USA
+Mashal Salman Aljehany University of Jeddah, Jeddah, Makkah, Kingdom of Saudi Arabia
+Mariam Aljunied Special Educational Needs Division, Ministry of Education, Singapore, Singapore
+Melissa L. Allen Department of Psychology, Lancaster University Fylde College, Lancaster, UK
+Shirley Alleyne School of Clinical Medicine and Research, The University of the West Indies, Cave Hill, St. Michael, Barbados
+Samira Al-Saad Kuwait Center for Autism, Kuwait, Kuwait
+Fouad A. W. Alshaban Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar
+Christine Alter Vocational Independence Program, New York Institute of Technology, Old Westbury, NY, USA
+D. O. Alvi Azad Yale Child Study Center, The Edward Zigler Center in Child Development and Social Policy, Yale University, New Haven, CT, USA
+Michael G. Aman Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA
+Evdokia Anagnostou Department of Peadiatrics, University of Toronto, Clinician Scientist, Bloorview Research Institute, Toronto, ON, Canada
+Allan M. Andersen Department of Psychiatry, University of Iowa, Iowa City, IA, USA
+Connie Anderson Post-Baccalaureate Certificate Program in Autism Studies, College of Health Professions, Towson University, Towson, MD, USA
+Cynthia M. Anderson May Institute, Randolph, MA, USA
+George M. Anderson Laboratory of Developmental Neurochemistry, Yale Child Study Center, Yale University, New Haven, CT, USA
+Ligia Antezana Department of Psychology, Virginia Tech, Blacksburg, VA, USA
+Karthikeyan Ardhanareeswaran Autism Program, Child Study Center, Yale School of Medicine, New Haven, CT, USA
+Program in Neurodevelopment and Regeneration, Yale School of Medicine, New Haven, CT, USA
+Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA
+Jennifer Arnold Department of Psychology, University of North Carolina, Chapel Hill, NC, USA
+Larry Arnold Autism Centre for Education and Research, University of Birmingham, Edgebaston, Birmingham, UK
+Sudha Arunachalam New York University, New York, NY, USA
+Miya Asato Pediatrics and Psychiatry, Division of Child Neurology, School of Medicine, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
+Kristen Ashbaugh Koegel Autism Center, University of California, Santa Barbara, CA, USA
+Chris Ashwin Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK
+Danielle Asklar Southern Connecticut State University, New Haven, CT, USA
+Takeshi Atsumi Department of Medical Physiology, Faculty of Medicine, Kyorin University, Mitaka/Tokyo, Japan
+Karla K. Ausderau Department of Kinesiology, Occupational Therapy Program, Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
+Sarita Austin Unlocking Language, London, UK
+Bonnie Auyeung Autism Research Centre, University of Cambridge, Cambridge, UK
+Mitrah E. Avini Yale Child Study Center, New Haven, CT, USA
+Alvi Azad Yale Child Study Center, The Edward Zigler Center in Child Development and Social Policy, Yale University, New Haven, CT, USA
+Gazi F. Azad Center for Autism and Related Disorders, Kennedy Krieger Institute’s, Baltimore, MD, USA
+Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
+Muhammad Waqar Azeem Sidra Medical and Research Center, Cornell Weill Medical College, Doha, Qatar
+Department of Psychiatry, Sidra Medicine, Doha, Qatar
+Weill Cornell Medicine, Doha, Qatar
+Marina Azimova ABA Services of CT, West Hartford, CT, USA
+Nur‘aini Azizah Faculty of Psychology, Universitas Islam Negeri Sunan Gunung Djati, Bandung, Indonesia
+Inmaculada Baixauli Catholic University of Valencia, Valencia, Spain
+Savana M. Y. Bak University of Minnesota, Twin Cities, Educational Psychology, Minneapolis, MN, USA
+Muideen O. Bakare Child and Adolescent Unit, Federal Neuropsychiatric Hospital, Enugu, Enugu, Nigeria
+Childhood Neuropsychiatric Disorders Initiatives (CNDI), Enugu, Nigeria
+Bruce L. Baker Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
+Jason K. Baker Department of Child and Adolescent Studies, California State University, Fullerton, Fullerton, CA, USA
+Vanessa Hus Bal University of Michigan, Ann Arbor, MI, USA
+Michelle Sondra Ballan Columbia University School of Social Work, New York, NY, USA
+Abigail Bangerter Department of Neuroscience, Janssen Research and Development, LLC, Titusville, NJ, USA
+Claudio Banzato Psychiatry, University of Campinas – Unicamp, Campinas, São Paulo, Brazil
+Grace T. Baranek Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University of Southern California (USC), Los Angeles, CA, USA
+Aurélie Baranger Autism-Europe, Bruxelles, Belgium
+Gregory Barnes Department of Neurology, School of Medicine, Vanderbilt University, Nashville, TN, USA
+Mihaela Barokova Center for Autism Research Excellence, Boston University, Boston, MA, USA
+Simon Baron-Cohen Autism Research Centre, University of Cambridge, Cambridge, UK
+Monica Barreto Yale Child Study Center, New Haven, CT, USA
+Amy C. Barrett Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA
+Anjali Barretto Department of Special Education, Gonzaga University, Spokane, WA, USA
+Kevin Barry Quinnipiac University School of Law, Hamden, CT, USA
+Lawrence Bartak Faculty of Education, Monash University, Clayton, VIC, Australia
+Christine Barthold Center for Disabilities Studies, University of Delaware, Newark, DE, USA
+Erin E. Barton University of Colorado Denver, Denver, CO, USA
+Marianne Barton Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Ran Barzilay Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
+Association for Children at Risk (R.A.), Tel Aviv, Israel
+Magali Batty Université de Toulouse, CERPPS, Toulouse, France
+Nirit Bauminger-Zviely School of Education, Bar-Illan University, Ramat-Gan, Israel
+Kimberly M. Bean Department of Special Education, Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
+Yvette F. Bean Department of Educational Psychology, University of Georgia, Athens, GA, USA
+Allison Bean Ellawadi Speech and Hearing Science, The Ohio State University, Columbus, OH, USA
+Luke Beardon The Autism Centre, Institute of Education, Sheffield Hallam University, Sheffield, South Yorkshire, UK
+Emily Beaudoin McGill University, Montreal, QC, Canada
+Kelly B. Beck Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, USA
+Daniel F. Becker Department of Psychiatry, University of California, San Francisco, USA
+Cynthia Beesley Benhaven, Inc., North Haven, CT, USA
+Marlene Behrman Department of Psychology, Carnegie Mellon University Center for the Neual Basis of Cognition, Pittsburgh, PA, USA
+Jennifer S. Beighley Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
+Sara Beltran Southern Connecticut State University, New Haven, CT, USA
+Julie Bender Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
+Stephanie Bendiske The Center For Children With Special Needs, Glastonbury, CT, USA
+Esther Ben-Itzchak Bruckner Center for Research in Autism, Department of Communication Disorders, Ariel University, Ariel, Israel
+Kyle D. Bennett Department of Teaching and Learning, Florida International University, Miami, FL, USA
+Matthew Bennett The University of Wollongong, Wollongong, NSW, Australia
+Randi Bennett Child Neuroscience Laboratory, Yale Child Study Center, New Haven, CT, USA
+Terry Bennett Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
+Loisa Bennetto Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA
+Eric Benninghoff Yale University, New Haven, CT, USA
+Betsey A. Benson Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA
+Carmen Berenguer University of Valencia, Valencia, Spain
+Michael Berger Department of Psychology, Royal Holloway University of London, Egham, Surrey, UK
+Ella Maja Viktoria Bergman Department of Education, UiT – The Arctic University of Norway, Tromsø, Norway
+Thomas Bergmann Berlin Treatment Center for Mental Health in Developmental Disabilities, Ev. Krankenhaus Königin Elisabeth Herzberge, Berlin, Germany
+Thomas P. Berney Institute of Health and Society, Sir James Spence Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK
+Raphael Bernier Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
+Armando Bertone McGill University, Montreal, QC, Canada
+Frank Besag Child and Adolescent Mental Health Services, SEPT. (South Essex Partnership University NHS Foundation Trust), Bedford, UK
+Chad Beyer Faculty of Medicine and Health Sciences, Stellenbosch University, Parow, South Africa
+Linas A. Bieliauskas Department of Psychiatry (F6248, MCHC-6), University of Michigan Health System, Ann Arbor, MI, USA
+Elizabeth E. Biggs Department of Special Education, University of Illinois, Urbana-Champaign, Champaign, IL, USA
+Dorothy Bishop Department of Experimental Psychology, University of Oxford, Oxford, UK
+Somer Bishop Department of Psychiatry, University of California, San Francisco, CA, USA
+Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
+Vicki Bitsika Faculty of Humanities and Social Sciences, Bond University, Robina, QLD, Australia
+Jan Blacher Graduate School of Education, University of California, Riverside, Riverside, CA, USA
+Caitlyn Black Southern Connecticut State University, New Haven, CT, USA
+Melissa H. Black School of Occupational Therapy, Social Work and Speech Pathology, Faculty of Health Sciences, Curtin Autism Research Group, Curtin University, Perth, WA, Australia
+Amanda Blackwell School of Behavioral and Brain Sciences, Callier Center for Communication Disorders, University of Texas-Dallas, Dallas, TX, USA
+Bryan J. Blair Institute for Behavioral Studies, The Van Loan School, Endicott College, Beverly, MA, USA
+Long Island University, Brooklyn, NY, USA
+Michael Bloch Yale OCD Research Clinic, New Haven, CT, USA
+Sarah Boland Yale Child Study Center, New Haven, CT, USA
+Danielle Bolling Yale Child Study Center, New Haven, CT, USA
+Sven Bölte Center of Neurodevelopmental Disorders (KIND), Department of Women’s and Children’s Health and Child and Adolescent Psychiatry, Center for Psychiatry Research, Karolinska Institutet and Stockholm County Council, Stockholm, Sweden
+Laura Bonazinga Bouyea Vermont Speech Language Pathology, University of Vermont, South Burlington, VT, USA
+Alex Bonnin Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
+Susan Y. Bookheimer Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, CA, USA
+Susan Boorin School of Nursing, Yale University, West Haven, CT, USA
+Hilary Boorstein Children’s Mercy Hospital, Kansas, MO, USA
+Kerri Booth Center for Children with Special Needs, Glastonbury, CT, USA
+Tereza-Maria Booules-Katri Department of Clinical and Health Psychology, Psychopathology and Neuropsychology Research Unit, Universitat Autonoma de Barcelona, Barcelona, Spain
+Jill Boucher Developmental Psychology, Autism Research Group, City University, London, UK
+Gordon Bourland Trinity Behavioral Associates, Arlington, TX, USA
+Linda Bowers LinguiSystems, Inc, East Moline, IL, USA
+Dermot Bowler Autism Research Group, City University London, London, UK
+Lisa Bowman-Perrott Texas A&M University, College Station, TX, USA
+Jessica Bradshaw Clinical Psychology, UCSB Koegel Autism Center, University of California, Santa Barbara, Santa Barbara, CA, USA
+John Bradshaw Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
+Meghan Brahm Department of Special Education, Southern Connecticut State University, New Haven, CT, USA
+Marcel Brass Ghent University, Ghent, Belgium
+Helena Brentani Department of Psychiatry, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil
+Neil Brewer Flinders University, Adelaide, SA, Australia
+Jennifer Brielmaier Laboratory of Behavioral Neuroscience, National Institute of Mental Health, NIH, Porter Neuroscience Research Center, Bethesda, MD, USA
+Kate O’. Brien Mary Immaculate College, Limerick, Ireland
+Darlene Brodeur Department of Psychology, Acadia University, Wolfville, NS, Canada
+Erik Bromberg University of California, Santa Barbara, Santa Barbara, CA, USA
+Kabie Brook Autism Rights Group Highland (ARGH), Inverness, Scotland, UK
+Rechele Brooks Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, USA
+Whitney T. Brooks Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA
+Jeffrey P. Brosco Department of Pediatrics, Miller School of Medicine, University of Miami, Mailman Center for Child Development, Miami, FL, USA
+Mark Brosnan Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK
+Gregory Brower School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
+Ted Brown Department of Occupational Therapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University – Peninsula Campus, Frankston, VIC, Australia
+Lauren Turner Brown Department of Psychiatry, Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Ted Brown School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University – Peninsula Campus, Frankston, VIC, Australia
+Pamela Brucker Special Education and Reading, Southern Connecticut State University, New Haven, CT, USA
+Crystal I. Bryce School of Social and Family Dynamics, Arizona State University, Tempe, AZ, USA
+Paulina L. Buffle Faculty de Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
+Jacob A. Burack Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada
+Shakeia Burgin Division of Speech and Hearing Sciences, Department of Allied Health Sciences, University of North Carolina-Chapel Hill, School of Medicine, Chapel Hill, NC, USA
+Mack D. Burke Texas A&M University, College Station, TX, USA
+Meghan M. Burke Department of Special Education, University of Illinois at Urbana-Champaign, Champaign, IL, USA
+Karen Burner Department of Psychology, University of Washington, Seattle, WA, USA
+Courtney Burnette University of Nebraska, Medical Center Munroe-Meyer Institute, Omaha, NE, USA
+Anthony Burns Department of Psychiatry, AARTS Center, Rush University Medical Center, Chicago, IL, USA
+Casey Burrows Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
+Sarah Butler Center for Autism and the Developing Brain, New York-Presbyterian Hospital/Westchester Division, White Plains, NY, USA
+Eilidh Cage Department of Psychology, University of Stirling, Stirling, Scotland, UK
+Ru Ying Cai Autism Spectrum Australia (Aspect), Aspect Research Centre for Autism Practice, Flemington, VIC, Australia
+Department of Educational Studies, Macquarie University, Sydney, NSW, Australia
+Marina Calac Center for Early Intervention Volnickel, Chisinau, Republic of Moldova
+Susan Calhoun Psychiatry, Penn State Health and College of Medicine, Hershey, PA, USA
+Claudia Califano Yale-New Haven Hospital, New Haven, CT, USA
+Kevin Callahan University of North Texas, Kristin Farmer Autism Center, Denton, TX, USA
+Daniel Campbell Yale Child Study Center, Yale University, New Haven, CT, USA
+Ricardo Canal-Bedia Clinical Psychology Department, Department of Personality, Assessment, and Psychological Treatment, Centro de Atención Integral al Autismo (INFOAUTISMO), University Institute of Community Integration (INICO), University of Salamanca, Salamanca, Spain
+Allison R. Canfield Department of Pediatrics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
+Maria Canon Yale Child Study Center, New Haven, CT, USA
+Lindsey Capece Quinnipiac University, Hamden, CT, USA
+Matthew R. Capriotti Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
+Laurie Cardona Yale Child Study Center, Yale University, New Haven, CT, USA
+Michael Carley Green Bay, WI, USA
+L. Lee Carlisle Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
+Joana C. Carmo Faculdade de Psicologia, Universidade de Lisboa, Lisbon, Portugal
+Departamento de Psicologia e Ciências da Educação, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, Faro, Portugal
+Christi Carnahan University of Cincinnati, Cincinnati, OH, USA
+Staci Carr UniqueKids Inc, Moseley, VA, USA
+Themba Carr University of Michigan Center for Human Growth and Development, Ann Arbor, MI, USA
+Alice S. Carter Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
+Mark Carter School of Education, Macquarie University, Sydney, NSW, Australia
+Manuel Casanova Department of Psychiatry, University of Louisville, Louisville, KY, USA
+Carissa J. Cascio Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
+Jane Case-Smith Division of Occupational Therapy, School of Health and Rehabilitation Sciences, Columbus, OH, USA
+Arlette Cassidy The Gengras Center, University of Saint Joseph, West Hartford, CT, USA
+Lisa Castagnola Child Study Center, The Edward Zigler Center in Child Development and Social Policy, School of Medicine, Yale University, New Haven, CT, USA
+A. Charles Catania Department of Psychology, UMBC (University of Maryland, Baltimore County), Baltimore, MD, USA
+Paul K. Cavanagh Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA
+Antonio Cerasa Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Mangone, Italy
+S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy
+Paige Cervantes Department of Child and Adolescent Psychiatry, Child Study Center, NYU Langone Health, New York, NY, USA
+Raymond Won Shing Chan ASD Services, New Life Psychiatric Rehabilitation Association, Kowloon, Hong Kong
+Marie Moore Channell Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
+S. Michael Chapman TEACCH Autism Program, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
+Marjorie H. Charlop Claremont McKenna College, Claremont, CA, USA
+Tony Charman Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, University of London, London, UK
+Marek Chawarski Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
+Liam R. Chawner University of Leeds, Leeds, UK
+Kuan-Lin Chen Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
+Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
+Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
+Karen Chenausky Boston University, Boston, MA, USA
+Tessa Chesher Tulane University, New Orleans, LA, USA
+Coralie Chevallier SGDP Centre, Institute of Psychiatry, King’s College, London, UK
+Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
+Stephanie N Child May Institute, Randolph, MA, USA
+Youngsun T. Cho Yale Child Study Center, New Haven, CT, USA
+Sylvia Henn Tean Choo Department of Child Development, KK Women’s and Children’s Hospital, Singapore, Singapore
+Nick Chown Palau-solità i Plegamans, Lliçà de Vall, Barcelona, Spain
+Rob Christian Department of Psychiatry, The Carolina Institute for Developmental Disabilities, University of North Carolina School of Medicine, Chapel Hill, NC, USA
+Domenic V. Cicchetti Departments of Psychiatry and Biometry, Yale Child Study Center, Yale University, New Haven, CT, USA
+Marina Ciccarelli School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+Joseph H. Cihon Autism Partnership Foundation, Seal Beach, CA, USA
+Emily Coderre Department of Communication Sciences and Disorders, University of Vermont, Burlington, VT, USA
+Keith A. Coffman Department of Pediatrics, School of Medicine, Pittsburgh, PA, USA
+Jared Cohen Yale Child Study Center, Yale University, New Haven, CT, USA
+Carla Colomer Universitat Jaume I, Castellon, Spain
+Emma Condy Neurodevelopmental and Behavioral Phenotyping Service, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
+Caitlin M. Conner Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
+John N. Constantino Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
+Barbara A. Cook Department of Communication Disorders, Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
+Elaine Coonrod Department of Psychiatry, School of Medicine, TEACCH, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Kelly D. Coons-Harding Department of Psychology, Laurentian University, Sudbury, ON, Canada
+Judith Cooper NIDCD (National Institute on Deafness and Other Communication Disorders), National Institute of Health EPS – Executive Plaza South, Rockville, MD, USA
+Eugenia Corbett Franklin County Home Health, St. Albans, VT, USA
+Cara Cordeaux Child Neuroscience Lab, Yale Child Study Center, New Haven, CT, USA
+Joseph A. Cornett Psychology and Global Health, Yale College, Yale University, New Haven, CT, USA
+Lauren Cornew Radiology Department, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
+Christoph U. Correll Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
+Christina Corsello Department of Psychiatry, Child and Adolescent Services Research Center, University of San Diego, San Diego, CA, USA
+Elin Cortijo-Doval Bioethics Center, Yale University, New Haven, CT, USA
+Kleio Cossburn Keele University, Keele, Newcastle-under-Lyme, UK
+Andreia P. Costa Institute for Health and Behavior, University of Luxembourg, Esch-sur-Alzette, Luxembourg
+Kirsty Coulter Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Emma Craig Queen’s University Belfast, Belfast, UK
+Kym Craig Heriot-Watt University, Edinburgh, Scotland, UK
+Madison Crandall Vanderbilt University, Nashville, TN, USA
+Laura Crane Centre for Research in Autism and Education (CRAE), UCL Institute of Education, University College London, London, UK
+Department of Psychology, Goldsmiths, University of London, New Cross, London, UK
+Hayley Crawford Coventry University, Coventry, UK
+Jacqueline N. Crawley Laboratory of Behavioral Neuroscience, National Institute of Mental Health, NIH, Porter Neuroscience Research Center, Bethesda, MD, USA
+Lisa Croen Autism Research Program, Kaiser Permanente Division of Research, Oakland, CA, USA
+Michael J. Crowley Developmental Electrophysiology Laboratory, Yale Child Study Center, New Haven, CT, USA
+Alyson Crozier School of Health Sciences, University of South Australia, Adelaide, SA, Australia
+Kristen D’Eramo The Center for Children with Special Needs, Glastonbury, CT, USA
+Sarah Dababnah University of Maryland School of Social Work, Baltimore, MD, USA
+Yael Dai Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Tamara C. Daley Westat, Durham, NC, USA
+Paulo Dalgalarrondo University of Campinas Cidade Universitária “Zeferino Vaz”, Campinas, São Paulo, Brazil
+Jeffrey Danforth Department of Psychology, Eastern Connecticut State University, Willimantic, CT, USA
+John T. Danial Psychological Studies in Education, University of California, Los Angeles, Los Angeles, CA, USA
+Clarissa Dantas Department of Psychiatry, Faculty of Medical Sciences, University of Campinas (Unicamp), Campinas, São Paulo, Brazil
+Catherine Davies Indiana Resource Center for Autism Indiana University, Bloomington, IN, USA
+Cheryl Davis 7 Dimensions Consulting, Worcester, MA, USA
+Luann Ley Davis University of Memphis, Memphis, TN, USA
+Naomi Davis Institute for Social Development, Cary, NC, USA
+Leann Smith DaWalt Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
+Geraldine Dawson Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
+Michelle Dawson Hôpital Rivière des Prairies, Centre de recherche du CIUSS du Nord de l’île de Montréal et département de psychiatrie de l’Université de Montréal, Montréal, QC, Canada
+Talena C. Day School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
+Annelies de Bildt Child and Adolescent Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
+Concetta de Giambattista Child Neuropsychiatry Unit, University of Bari “Aldo Moro”, Bari, Italy
+Maretha de Jonge Department of Psychiatry, University Medical Center, Utrecht, Netherlands
+Ad De Jongh Department of Social Dentistry and Behavioral Sciences, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands
+School of Health Sciences, Salford University, Manchester, UK
+Institute of Health and Society, University of Worcester, Worcester, UK
+School of Psychology, Queen’s University, Belfast, Ireland
+Naama de la Fontaine Yale Child Study Center, New Haven, CT, USA
+Ashley B. de Marchena Department of Behavioral and Social Sciences, University of the Sciences, Philadelphia, PA, USA
+Oana De Vinck Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA
+Petrus J. de Vries Division of Child & Adolescent Psychiatry, University of Cape Town, Rondebosch, South Africa
+Rebecca DeAquair The Center for Children with Special Needs, Glastonbury, CT, USA
+W. Thornton N. Deegan Yale Child Study Center, New Haven, CT, USA
+Michelle DeFelice Southern Connecticut State University, New Haven, CT, USA
+Emma Delemere School of Social Sciences, Education and Social Work, Queen’s University Belfast, Belfast, UK
+Kristin Dell’Armo The Ohio State University Nisonger Center – UCEDD, Columbus, OH, USA
+Lara Delmolino Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
+Elizabeth A. DeLucia Yale Child Study Center, New Haven, CT, USA
+Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
+K. Mark Derby Department of Special Education, Gonzaga University, Spokane, WA, USA
+Mieke Dereu Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
+Whitney J. Detar Gevirtz Graduate School of Education, The University of California Center for Special Education, Disabilities, and Development, Santa Barbara, CA, USA
+Gabriel S. Dichter UNC Departments of Psychiatry, Psychology and Neuroscience, UNC-Chapel Hill, Carolina Institute for Developmental Disabilities, Chapel Hill, NC, USA
+Joshua J. Diehl Autism Services, LOGAN Community Resources, Inc., South Bend, IN, USA
+Department of Psychology, University of Notre Dame, Notre Dame, IN, USA
+Carolyn DiGuiseppi Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
+Anthony DiLollo Wichita State University, Department of Communication Sciences and Disorders, Wichita, Kansas, USA
+Nicholas M. DiLullo Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Ilan Dinstein Psychology Department, Carnegie Mellon University, Pittsburgh, PA, USA
+Amiris Dipuglia Pennsylvania Training and Technical Assistance Network, Harrisburg, PA, USA
+Leyla Akoury Dirani Division of Child and Adolescent Psychiatry, Department of Psychiatry, American University of Beirut Medical Center, Beirut, Lebanon
+Cheryl Dissanayake Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
+Mark R. Dixon Behavior Analysis and Therapy Program, Southern Illinois University, Carbondale, IL, USA
+Peter Doehring Foundations Behavioral Health, Doylestown, PA, USA
+ASD Roadmap, Chadds Ford, PA, USA
+Sam Doernberg Cornell University, Ithaca, NY, USA
+Rebecca Doggett Koegel Autism Center, Gevirtz Graduate School of Education University of California, Santa Barbara, Santa Barbara, CA, USA
+Elizabeth Howell Dohrmann Treatment and Research Institute for Autism Spectrum Disorders (TRIAD), Nashville, TN, USA
+Department of Psychiatry and Biobehavioral Sciences, Child and Adolescent Psychiatry Fellowship Program, Semel Institute for Neuroscience and Human Behavior, Resnick Neuropsychiatric Hospital, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
+J. Don Richardson Department of Psychiatry, University of Western Ontario, London, ON, Canada
+John Donvan Washington, DC, USA
+Michael F. Dorsey Institute for Behavioral Studies, The Van Loan School, Endicott College, Beverly, MA, USA
+Amego Inc., The Best Clinical Network, Attleboro, MA, USA
+Constance Doss Department of Psychology, University of Alabama-Birmingham, Birmingham, AL, USA
+Katerina Dounavi School of Social Sciences, Education and Social Work, Queen’s University Belfast, Belfast, UK
+Peter W. Dowrick University of Auckland, Auckland, New Zealand
+Carolyn A. Doyle Indiana University School of Medicine, Indianapolis, IN, USA
+Jessica Dreaver School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+Curtin Autism Research Group, Curtin University, Perth, WA, Australia
+Katerina M. Dudley Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Ana D. Dueñas Education and Human Services, Lehigh University, Bethlehem, PA, USA
+Jodi M. Duke Division of Special Education and disability Research, Fairfax, VA, USA
+Eric Duku Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
+Amie Duncan Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
+Ed Duncan Children’s Centre, La Trobe University, Melbourne, VIC, Australia
+Debra Dunn The Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
+Patrick Dwyer Centre for Autism Research, Technology and Education, Department of Psychology, Victoria, Canada
+Department of Psychology, University of California, Davis, Davis, CA, USA
+Kathleen Dyer River Street Autism Program at Coltsville, Capitol Region Education Council/Elms College, Hartford, CT, USA
+Endicott College, Bloomfield, CT, USA
+Jaclyn M. Dynia Crane Center for Early Childhood Research and Policy, The Ohio State University, Columbus, OH, USA
+Shaun M. Eack School of Social Work and Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
+Maureen Early Christian Sarkine Autism Treatment Center, Indianapolis, IN, USA
+Lisa Edelson-Fries Department of Psychology, Boston University, Boston, MA, USA
+Neurocognition, Department of Brain Health, Nestlé Institute for Health Sciences, Lausanne, Switzerland
+Elizabeth R. Eernisse Department of Language and Literacy, Cardinal Stritch University, Milwaukee, WI, USA
+Shaunessy Egan Center for Children with Special Needs, Glastonbury, CT, USA
+Inge-Marie Eigsti Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Svein Eikeseth Department of Behavioral Science, Oslo and Akershus University College, Lillestrøm, Norway
+Ingólfur Einarsson The State Diagnostic and Counseling Center, Kópavogur, Iceland
+Martin Eisemann Department of Psychology, UiT – The Arctic University of Norway, Tromso, Norway
+Naomi V. Ekas Department of Psychology, Texas Christian University, Fort Worth, TX, USA
+Rob El Fattal Cultivate Behavioral Health and Education, Bee Cave, TX, USA
+Ismail El Hailouch School of Public Health, Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Paul El-Fishawy State Laboratory, Child Study Center, Yale University, New Haven, CT, USA
+Amira Elhoufey Department of Community Health Nursing, Faculty of Nursing, Assiut University, Assiut, Egypt
+Department of Community Health Nursing, Sabia University College, Jazan University, Jazan, Kingdom of Saudi Arabia
+Stephen N. Elliott Sanford School of Social and Family Dynamics, Learning Sciences Institute, Arizona State University, Tempe, AZ, USA
+Kimberly Ellison Yale Child Study Center, New Haven, CT, USA
+Eric Emerson Centre for Disability Research, Lancaster University, Lancaster, LA, UK
+Centre for Disability Research and Policy, University of Sydney, Lidcombe, NSW, Australia
+Paul Edward Engelhardt School of Psychology, University of East Anglia, Norwich, Norfolk, UK
+Peter Enticott Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
+Ruth Eren Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
+Patricio Erhard University of Texas at Austin, Austin, TX, USA
+Craig A. Erickson Christian Sarkine Autism Treatment Center, Indianapolis, IN, USA
+Department of Psychiatry, University of Cincinnati School of Medicine, Cincinnati, OH, USA
+Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
+Gianluca Esposito Nanyang Technological University, Wako, Saitama, Singapore
+University of Trento, Rovereto, TN, Italy
+Kuroda Research Unit, RIKEN Brain Science Institute, Wako-shi Saitama, Japan
+Joshua Ewen Kennedy Krieger Institute, Baltimore, MD, USA
+Mariah Eykelhoff Southern Connecticut State University, New Haven, CT, USA
+Reina S. Factor Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
+Virginia Tech Autism Clinic and Center for Autism Research, Blacksburg, VA, USA
+Michelle D. Failla Department of Psychiatry and Behavioral Science, Vanderbilt University Medical School, Nashville, TN, USA
+Terry S. Falcomata University of Texas at Austin, Austin, TX, USA
+Marita Falkmer School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+Torbjörn Falkmer School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+Pain and Rehabilitation Centre, and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
+Megan Farley Psychiatry, University of Utah School of Medicine, University Neuropsychiatric Institute, Salt Lake City, UT, USA
+Cristan Farmer The National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
+Nisonger Center Psychology, Ohio State University, Columbus, OH, USA
+Janet Farmer Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, USA
+Miranda Farmer Yale Child Study Center, New Haven, CT, USA
+Jesslyn N. Farros Endicott College, Beverly, MA, USA
+Deborah Fein Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Adam Feinstein Autism Cymru and Looking Up, London, UK
+Maurice Feldman Department of Child and Youth Studies and Department of Applied Disability Studies, Brock University, St. Catharines, ON, Canada
+Eunice Feng Koegel Autism Center, Eli and Edythe L. Broad Center for Asperger Research, University of California, Santa Barbara, CA, USA
+Rachel M. Fenning Department of Child and Adolescent Studies, California State University, Fullerton, Fullerton, CA, USA
+Jenny Ferguson School of Social Sciences, Education and Social Work, Queen’s University Belfast, Belfast, UK
+Julia L. Ferguson Autism Partnership Foundation, Seal Beach, CA, USA
+Thomas Fernandez Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Summer Ferreri Department of Counseling, Educational Psychology and Special Education, College of Education Michigan State University, East Lansing, MI, USA
+Sean Field The School at Springbrook, Oneonta, NY, USA
+Carlos N. Filipe Faculdade de Ciências Médicas, NOVA Medical School, Universidade Nova de Lisboa, Lisbon, Portugal
+Joseph J. Fins Division of Medical Ethics, Weill Cornell Medical College, New York, NY, USA
+Michael B. First Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, NY, USA
+Nicole Fischer Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
+Patricio Fischman Yale University Child Study Center, New Haven, CT, USA
+Private Practice, Santiago, Chile
+Ronit Fischman Child and Adolescent Psychologist, Private Practice, Santiago, Chile
+Veronica P. Fleury Florida State University, Tallahassee, FL, USA
+Renee Folsom Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA) The Help Group/UCLA Neuropsychology Program, Los Angeles, CA, USA
+Laura Fontil Department of Educational and Counselling Psychology, School/Applied Child Psychology, McGill University, Montreal, QC, Canada
+Joy Fopiano Department of Elementary Education, Southern Connecticut State University, New Haven, CT, USA
+Danielle Forbes Psychology, University of Massachusetts Boston, Boston, MA, USA
+Solandy Forte The Center for Children with Special Needs, Glastonbury, CT, USA
+Milestones Behavioral Services, Inc., Milford, CT, USA
+Jennifer H. Foss-Feig Department of Psychiatry, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
+Richard M. Foxx University of Pennsylvania, Harrisburg, PA, USA
+Christina Fragale University of Texas at Austin, Austin, TX, USA
+Kathleen B. Franke The Unumb Center for Neurodevelopment, Columbia, SC, USA
+Thomas Frazier Autism Speaks, New York, NY, USA
+Cleveland Clinic Children’s, Cleveland, OH, USA
+Stephanny Freeman Center for Autism Research and Treatment (CART), University of California, Los Angeles, Los Angeles, CA, USA
+Megan Freeth Department of Psychology, University of Sheffield, Sheffield, UK
+Hannah Friedman Yale Child Study Center, New Haven, CT, USA
+Uta Frith Division of Biosciences, Institute of Cognitive Neuroscience UCL, London, UK
+Cori Fujii Division of Psychological Studies in Education, University of California, Los Angeles, Los Angeles, CA, USA
+Daniel Shuen Sheng Fung Department of Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore
+Rosaria Furlano Department of Psychology, Queen’s University, Kingston, ON, Canada
+Maria Fusaro Department of Psychiatry and Behavioral Sciences, UC Davis M.I.N.D. Institute, Sacramento, CA, USA
+Cheryl Smith Gabig Department of Speech, Language, and Hearing Sciences, Lehman College/The City University of New York, Bronx, NY, USA
+Sebastian Gaigg Autism Research Group, City University London, London, UK
+Eynat Gal Department of Occupational Therapy, University of Haifa, Haifa, Israel
+Cédric Galera Department of Child and Adolescent Psychiatry, Université de Bordeaux, Bordeaux, France
+Jennifer Gallup Idaho State University, Pocatello, ID, USA
+Tanuja Gandhi Child Study Centre, Yale School of Medicine, New Haven, CT, USA
+Cristina García-López Joint Research Institute National University for Distance Education and Health Institute Carlos III (IMIENS), Madrid, Spain
+Hospital Sant Joan de Déu, UTAE, Barcelona, Spain
+Lauren Gardner Child Development and Rehabilitation Center, Johns Hopkins All Children’s Hospital, Saint Petersburg, FL, USA
+Dana Rose Garfin Sue & Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, USA
+Gabriela Garrido Department of Child and Adolescent Psychiatry, ASD Department, Pereira Rossell Hospital – ASSE, Universidad de la República, School of Medicine, Montevideo, Uruguay
+Beth Garrison Hartford Hospital Pain Treatment Center, Bristol, CT, USA
+Grant Gautreaux Nicholls State University, Thibodaux, LA, USA
+David C. Gavisk College of Contemporary Liberal Studies, Department of Education, Regis University, Denver, CO, USA
+Erin Gelinas Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
+Grace W. Gengoux Child and Adolescent Psychiatry, Stanford University School of Medicine, Lucile Packard Children’s Hospital, Stanford, CA, USA
+Danielle Geno The College of Arts and Sciences, The University of Vermont, Burlington, VT, USA
+Stelios Georgiades Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
+Sima Gerber Department of Linguistics and Communication Disorders, Queens College, Flushing, NY, USA
+Jennifer Varley Gerdts Department of Psychology, University of Washington, CHDD, Seattle, WA, USA
+Meital Gewirtz Yale University, New Haven, CT, USA
+Golnaz Ghaderi Department of Social Sciences, University of Ottawa, Ottawa, ON, Canada
+Ahmad Ghanizadeh School of Medicine, Research Center for Psychiatry and Behavioral Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
+Parisa Ghanouni Occupational Science and Occupational Therapy Department, University of British Columbia, Vancouver, Canada
+Occupational Science and Occupational Therapy Department, Dalhousie University, Halifax, NS, Canada
+Mohammad Ghaziuddin University of Michigan, Ann Arbor, MI, USA
+Jenna Gilder Claremont Graduate University, Claremont, CA, USA
+Christopher Gillberg Department of Child and Adolescent Psychiatry, Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
+Madelyn A Gillentine Department of Genome Sciences, University of Washington, Seattle, WA, USA
+Walter Gilliam Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Regina Gilroy Quinnipiac University School of Law, Hamden, CT, USA
+Sonya Girdler School of Occupational Therapy, Social Work and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
+Curtin Autism Research Group, Curtin University, Perth, WA, Australia
+Ivy Giserman Kiss Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
+Jalisa Gittens McGill University, Montreal, QC, Canada
+Theresa R. Gladstone Yale Child Study Center, New Haven, CT, USA
+Jeffrey Glennon Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
+Tara J. Glennon Occupational Therapy, Quinnipiac University, Hamden, CT, USA
+Centre of Pediatric Therapy, Fairfield and Wallingford, Wallingford, CT, USA
+Dorie Glover Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
+Lindsay B. Glugatch Department of Special Education and Clinical Sciences, University of Oregon, Eugene, OR, USA
+Nitin Gogtay Division of Child and Adolescent Psychiatry, National Institutes of Mental Health, Bethesda, MD, USA
+Tze Jui Goh Department of Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore
+Ofer Golan Department of Psychology, Bar-Ilan University, Ramat Gan, Israel
+Association for Children at Risk (R.A.), Tel Aviv, Israel
+Melissa C. Goldberg Kennedy Krieger Institute, Baltimore, MD, USA
+Wendy A. Goldberg Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
+Yael Goldfarb Department of Occupational Therapy, University of Haifa, Haifa, Israel
+Rachel L. Goldin Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
+Tina R. Goldsmith Center for Development and Disability, University of New Mexico, Albuquerque, NM, USA
+Howard Goldstein Human Development and Family Science, The Ohio State University, Columbus, OH, USA
+Sam Goldstein Neurology Learning and Behavior Center, University of Utah, Salt Lake City, UT, USA
+Peyman Golshani David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
+José Luis Cuesta Gómez Faculty of Education, Universidad de Burgos, Burgos, Spain
+Ana Maria Gonzalez-Barrero Department of Psychology, Concordia University, Montreal, QC, Canada
+Emma Goodall The University of Wollongong, Wollongong, NSW, Australia
+Cara Damiano Goodwin Virginia Institute of Autism, Charlottesville, VA, USA
+Amanda E. Gordon Quinnipiac University School of Law, Hamden, CT, USA
+Ilanit Gordon Child Study Center, Yale University, New Haven, CT, USA
+Judith Gould NAS Lorna Wing Centre for Autism, Bromley, UK
+Michele Goyette-Ewing Yale Child Study Center, New Haven, CT, USA
+Richard B. Graff The New England Center for Children, Southborough, MA, USA
+Catherine Grainger Psychology, University of Stirling, Stirling, Scotland, UK
+Temple Grandin Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
+Kylie M. Gray Centre for Developmental Psychiatry and Psychology, Department of Psychiatry, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
+Sarah A. O. Gray Department of Psychology, Tulane University, New Orleans, LA, USA
+Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
+Ashley Dawn Greathouse Combined-Integrated Clinical and Counseling Psychology Doctoral Program, University of South Alabama, Mobile, AL, USA
+Kirstin Greaves-Lord Jonx Department of (Youth) Mental Health and Autism, Lentis Psychiatric Institute, Groningen, The Netherlands
+Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, Rotterdam, The Netherlands
+Yulius Autisme, Dordrecht, The Netherlands
+Emma Green Department of Psychology, University of Waterloo, Waterloo, ON, Canada
+Evelynne Green The University of Vermont, Burlington, VT, USA
+Shulamite A. Green Department of Psychology, University of California, Los Angeles, CA, USA
+Alissa Greenberg Juvo, Sacramento, CA, USA
+Jan S. Greenberg Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
+Alyse Greer Quinnipiac University School of Law, Hamden, CT, USA
+Frank M. Gresham Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
+Elena L. Grigorenko University of Houston, Houston, TX, USA
+Yale Child Study Center, Psychology, and Epidemiology and Public Health, Yale University, New Haven, CT, USA
+Jemma Grindstaff Chapel Hill TEACCH Center, Carrboro, NC, USA
+Roy Grinker Anthropology, The George Washington University, Washington, DC, USA
+Roseann R. Groh Center for Children with Special Needs, Glastonbury, CT, USA
+Mark Groskreutz Special Education and Reading Department, The Center of Excellence on Autism Spectrum Disorders, Southern Connecticut State University, New Haven, CT, USA
+Matthew Grover Otterbein University, Westerville, OH, USA
+Manon Grube Center for Music in the Brain, Faculty of Health, Aarhus University, Aarhus, Denmark
+Rinatte Gruen Yale Child Study Center, New Haven, CT, USA
+Ouriel Grynszpan Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur, LIMSI, CNRS, Université Paris-Sud, Orsay, France
+Rebecca Grzadzinski Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
+Amanda C. Gulsrud UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
+Yuqing Guo Sue & Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, USA
+Abha R. Gupta Developmental-Behavioral Pediatrics, Child Study Center, Yale University, New Haven, CT, USA
+Nouchine Hadjikhani Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA
+Gillberg Neuropsychiatry Center, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
+Eileen Haebig Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA
+Deborah Hales Division of Education, American Psychiatric Association, Arlington, VA, USA
+Jane Hamilton Quinnipiac University School of Law, Hamden, CT, USA
+Daniela Han Private Practice, Santiago, Chile
+Yu Han Neuroscience, University of Vermont, Burlington, VT, USA
+Gregory P. Hanley Western New England University, Springfield, MA, USA
+Robin Hansen Pediatrics, Center for Excellence in Developmental Disabilities, UC Davis M.I.N.D. Institute, Sacramento, CA, USA
+Francesca Happé MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
+Antonio Y. Hardan Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
+Sarah Hardy Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
+Annville Psychological Services, Annville, PA, USA
+Toya Harmon Texas State University, San Marcos, TX, USA
+Sandra Harris Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
+Ashley J. Harrison Department of Educational Psychology, University of Georgia, Athens, GA, USA
+Catharina Hartman Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
+Ahmad Hassan Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
+Wassim Hassan Department of Neuroscience, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
+Tyler A. Hassenfeldt Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
+Kathleen Hastings Southern Connecticut State University, New Haven, CT, USA
+Megan Hatfield School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+Susan M. Havercamp Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA
+Brett Heasman Centre for Research in Autism and Education (CRAE), UCL, London, UK
+Pamela Heaton Department of Psychology, University of London, London, UK
+Amy Heberle Clinical Psychology, University of Massachusetts, Boston, MA, USA
+Darren Hedley School of Psychological Science, Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
+John P. Hegarty Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
+Stephen Hegedus School of Education, Southern Connecticut State University, New Haven, CT, USA
+S. M. J. Heijnen-Kohl Mondriaan Geriatric Mental Health Care, Heerlen-Maastricht, The Netherlands
+Sascha Hein Freie Universität Berlin, Berlin, Germany
+David T. Helm Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA
+Heather A. Henderson Department of Psychology, University of Waterloo, Waterloo, ON, Canada
+Department of Psychology, University of Miami, Coral Gables, FL, USA
+Dawn Hendricks Department of Special Education and Disability Policy, VCU Autism Center for Excellence, Virginia Commonwealth University, Richmond, VA, USA
+Susan Hepburn Department of Psychiatry and Pediatrics, JFK Partners, University of Colorado at Denver, Aurora, CO, USA
+Colorado State University, Department of Human Development and Family Services, Fort Collins, CO, USA
+Katelyn Herchel Center for Children with Special Needs, Glastonbury, CT, USA
+Irva Hertz-Picciotto Department of Public Health Sciences and the MIND Institute, University of California, Davis, Davis, CA, USA
+Amaia Hervas Child and Adolescent Mental Health Unit, University Hospital Mutua of Terrassa, Barcelona, Spain
+Sean Hess Rehabilitation Services, Wesley Woodlawn Hospital & ER, Wichita, KS, USA
+Ashley Durkee Hester Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Steven D. Hicks Penn State College of Medicine, Hershey, PA, USA
+Trenesha L. Hill Department of Psychology, Tulane University, New Orleans, LA, USA
+Manon H. J. Hillegers Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands
+Ashleigh Hillier Department of Psychology, University of Massachusetts Lowell, Lowell, MA, USA
+Jennifer Hillman Applied Psychology Program, The Pennsylvania State University, Berks College, Reading, PA, USA
+Claudia Hilton Occupational Therapy Department, University of Texas Medical Branch, Galveston, TX, USA
+Kimberly Ho Misiaszek Yale Child Study Center, New Haven, CT, USA
+Michal Hochhauser Department of Occupational Therapy, Ariel University, Ariel, Israel
+Ginny Hodge Chapel Haven, Inc, New Haven, CT, USA
+Abby Hodges University of Denver, Denver, CO, USA
+Sandra Hodgetts Pediatrics, University of Alberta, Edmonton, AB, Canada
+Kristin Hodgson UNC TEACCH Autism Program-Charlotte, Charlotte, NC, USA
+Ellen J. Hoffman Albert J. Solnit Integrated Training Program, Yale Child Study Center, Program on Neurogenetics, Yale School of Medicine, New Haven, CT, USA
+Abigail L. Hogan Department of Psychology, University of South Carolina, Columbia, SC, USA
+Kerry Hogan Wilmington Psych, Wilmington, NC, USA
+Thomas P. Hogan Department of Psychology, University of Scranton, Scranton, PA, USA
+Katherine C. Holman Department of Special Education, Towson University, Towson, MD, USA
+Anne Holmes Eden Autism Services, Princeton, NJ, USA
+David L. Holmes Lifespan Services, Princeton, NJ, USA
+Jinkuk Hong Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
+Lisa Honigfeld Child Health and Development Institute of Connecticut, Farmington, CT, USA
+Stephen R. Hooper Department of Allied Health Sciences, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
+Daniel W. Hoover Center for Child and Family Traumatic Stress, Kennedy Krieger Institute, Baltimore, MD, USA
+M. S. Hope Morris Communication Sciences and Disorders, The University of Vermont, Burlington, VT, USA
+Andrea Horvath Department of Paediatrics, The Medical University of Warsaw, Warsaw, Poland
+Ernst Horwitz Department of Psychiatry, Groningen University Medical Center, Groningen, The Netherlands
+Katherine Howells Deakin Child Study Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, VIC, Australia
+Patricia Howlin Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
+Youjia Hua Department of Curriculum, Instruction and Special Education, Curry School of Education and Human Development, University of Virginia, Charlottesville, VA, USA
+Kristelle Hudry Olga Tennison Autism Research Centre, School of Psychological Science, La Trobe University, Bundoora, VIC, Australia
+Marisela Huerta Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, NY, USA
+Samantha Huestis Yale Child Study Center, New Haven, CT, USA
+Rosemary Huisingh LinguiSystems, Inc, East Moline, IL, USA
+Laura Hull Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
+Kara Hume University of North Carolina, Chapel Hill, NC, USA
+Rachel Hundley Division of Developmental Medicine, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
+Hillary Hurst Department of Psychology, University of Massachusetts Boston, Boston, MA, USA
+Vanessa Hus Department of Psychology, University of Michigan, Ann Arbor, MI, USA
+Tiffany Hutchins Department of Communication Sciences and Disorders, The University of Vermont, Burlington, VT, USA
+Ted Hutman Department of Psychiatry and Biobehavioral Science, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
+Semel Institute of Neuroscience, Los Angeles, CA, USA
+Soonjo Hwang Massachusetts General Hospital, Boston, MA, USA
+Wei-Chin Hwang Department of Psychology, Claremont McKenna College, Claremont, CA, USA
+Susan Hyman Developmental and Behavioral Pediatrics, Division Chief Neurodevelopmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA
+Suzannah Iadarola Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
+Dorothea A. Iannuzzi Division of Academic Pediatrics, Autism Intervention Research Network on Physical Health (AIR-P), Autism Treatment Network (ATN), Mass General Hospital for Children, Boston, MA, USA
+Karim Ibrahim Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA
+Masakazu Ide Department of Disabilities of Brain Functions, Research Institute of National Rehabilitation Center for Persons with Disabilities, Tokorozawa/Saitama, Japan
+Nazish Imran Child and Family Psychiatry Department, King Edward Medical University/Mayo Hospital, Lahore, Pakistan
+Sheree Incorvaia Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA
+Brooke Ingersoll Department of Psychology, Michigan State University, East Lansing, MI, USA
+Barry Ingham Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle, UK
+Irma Isasa Child and Adolescent Service, Polyclinic Gipuzkoa, San Sebastián, Spain
+Andrew Iskandar Division TEACCH, CB 7180, UNC-CH, TEACCH Early Intervention Program, Chapel Hill, NC, USA
+Scott Luther James Jackson Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Laudan B. Jahromi School of Social and Family Dynamics, Arizona State University, Tempe, AZ, USA
+Mark Jaime Division of Science, Indiana University-Purdue University, Columbus, Columbus, IN, USA
+T. Rene Jamison Center for Child Health and Development, University of Kansas Medical Center, Kansas City, KS, USA
+Sara Jelinek Department =of Psychology, Michigan State University, East Lansing, MI, USA
+Heather H. Jia Illinois State University, Normal, IL, USA
+Ronnie Jia Illinois State University, Normal, IL, USA
+Cynthia R. Johnson Pediatrics, Psychiatry, and Education, University of Pittsburgh, Pittsburgh, PA, USA
+Ellen Johnson Section of Social Work, Mayo Clinic, Rochester, MN, USA
+Kimberly Johnson Neurodevelopmental and Behavioral Pediatrics, Children’s Hospital Colorado, Aurora, CO, USA
+Kristin Johnson Yale University, New Haven, CT, USA
+Catherine R. G. Jones Department of Psychology, University of Essex, Colchester, UK
+Emily Jones Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
+Rebecca Jordan Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA
+Rita Jordan School of Education, University of Birmingham, Edgbaston, Birmingham, UK
+Roger J. Jou Child Study Center, Yale University School of Medicine, New Haven, CT, USA
+Martha Bates Jura Department of Psychiatry, UCLA/Geffen School of Medicine, Los Angeles, CA, USA
+Tobi Gilbert Juris Quinnipiac University School of Law, Hamden, CT, USA
+Aaron Kaat Nisonger Center, Ohio State University, Columbus, OH, USA
+Allison Kahl New York University School of Law, New York, NY, USA
+Martha D. Kaiser Child Neuroscience Laboratory, Yale Child Study Center, New Haven, CT, USA
+Luke Kalb Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Kennedy Krieger Institute’s Center for Autism and Related Disorders, Baltimore, MD, USA
+Rajesh Kana Department of Psychology, University of Alabama-Birmingham, Birmingham, AL, USA
+Xin Kang Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
+Steve Kanne Department of Health Psychology, School of Health Professions Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, USA
+Sara Kaplan-Levy Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA
+Annette Karmiloff-Smith Birkbeck College, London, UK
+Christie P. Karpiak Department of Psychology, University of Scranton, Scranton, PA, USA
+Connie Kasari Graduate School of Education and Information Studies and the Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
+Juli Katon Department of Special Education, University of Maryland, College Park, MD, USA
+Alice Kau Intellectual and Developmental Disabilities (IDD) Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
+Elizabeth Kauffman AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
+Alan S. Kaufman Yale University School of Medicine, New Haven, CT, USA
+Carson Kautz Yale Child Study Center, Yale University, New Haven, CT, USA
+Brandon Keehn Department of Speech, Language, and Hearing Sciences, Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
+Jacqueline Kelleher Education, Sacred Heart University Isabelle Farrington School of Education, Southern Connecticut State University, Fairfield, CT, USA
+Annemarie M. Kelly College of Health and Human Services, Eastern Michigan University, Ypsilanti, MI, USA
+Daniel P. Kennedy Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
+Maureen C. Kenny Department of Counseling, Recreation and School Psychology, Florida International University, Miami, FL, USA
+Danielle Geno Kent The College of Arts and Sciences, The University of Vermont, Burlington, VT, USA
+Connor M. Kerns Department of Psychology, University of British Columbia, Vancouver, BC, Canada
+Stephenie Koon Miang Khoo Autism Resource Centre, Singapore, Singapore
+Meena Khowaja Nemours/A.I. duPont Hospital for Children, Wilmington, DE, USA
+Emily Kilroy Mayes Lab, Yale Child Study Center, New Haven, CT, USA
+Jinah Kim Department of Creative Arts Therapy, College of Cultural Convergence, Jeonju University, Jeonju, Jeollabukdo, Republic of Korea
+Mina Kim College of Education Temple University, Philadelphia, PA, USA
+So Hyun Sophy Kim Department of Psychiatry, Weill Cornell Medicine, White Plains, NY, USA
+Department of Psychology, University of Michigan, Ann Arbor, MI, USA
+Sunny Kim Koegel Autism Center, University of California, Santa Barbara, Santa Barbara, CA, USA
+Young-Shin Kim Yale Child Study Center, New Haven, CT, USA
+Yael Kimhi Education, Levinsky College of Education, Tel-Aviv, Israel
+Jessica Lynn Kinard The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Bryan King Department of Psychiatry and Behavioral Sciences and Seattle Children’s Hospital, University of Washington, Seattle, WA, USA
+Robert King School of Applied Psychology, University College Cork, Cork, Ireland
+Usha Kini Consultant Clinical Geneticist, Oxford Radcliffe Hospitals NHS Trust University of Oxford, Oxford, UK
+Anne V. Kirby Department of Occupational and Recreational Therapies, University of Utah, Salt Lake City, UT, USA
+Raymond M. Klein Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada
+Harvey J. Kliman Reproductive and Placental Research Unit, Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, CT, USA
+Laura G. Klinger TEACCH Autism Program, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Vicki Madaus Knapp Applied Behavior Analysis (ABA) Program, Daemen College, Amherst, NY, USA
+Rebecca Knickmeyer Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
+Victoria Knight Faculty of Education, University of British Columbia, Vancouver, BC, Canada
+Ryan Knighton The Center for Children with Special Needs, Glastonbury, CT, USA
+Newton Public Schools, Newton, MA, USA
+Jordan A. Ko Koegel Autism Center/Department of Counseling, Clinical, and School Psychology, University of California Santa Barbara, Santa Barbara, CA, USA
+Brittany L. Koegel University of California, Santa Barbara, Santa Barbara, CA, USA
+Lynn Kern Koegel Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
+Koegel Autism Center, Eli and Edythe L. Broad Center for Asperger Research, University of California, Santa Barbara, CA, USA
+Robert L. Koegel Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
+Koegel Autism Center/Clinical Psychology, Gevirtz Graduate School of Education, University of California, Santa Barbara, CA, USA
+Frances L. Kohl Department of Special Education, University of Maryland, College Park, MD, USA
+Natasha Kolivas Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
+Judah Koller Seymour Fox School of Education, Clinical Child Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel
+Koorosh Kooros Pediatric Gastroenterology and Nutrition, Rady Children’s Hospital, San Diego, University of California San Diego, San Diego, CA, USA
+Jonathan Kopel Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA
+Kellie Kotwicki Applied Behavior Analysis, Daemen College, Amherst, NY, USA
+Positive ABA, LLC, Queen Creek, AZ, USA
+Klara Kovarski Fondation Ophtalmologique A. de Rothschild, Institut de Neuropsychologie, Neurovision et NeuroCognition, Paris, France
+CNRS (Integrative Neuroscience and Cognition Center, UMR 8002), Paris, France
+Université Paris Descartes, Sorbonne Paris Cité, Paris, France
+David J. Krainski Vocational Independence Program, New York Institute of Technology, Central Islip, NY, USA
+Cate Kraper Clinical Psychology, University of Massachusetts Boston, Boston, MA, USA
+Anna M. Krasno The Gevirtz School, UC Santa Barbara Koegel Autism Center, Santa Barbara, CA, USA
+Jennifer M. D. Kremkow Department of Communication Sciences and Disorders, Elmhurst College, Elmhurst, IL, USA
+M. Kristen Center for Children with Special Needs, Glastonbury, CT, USA
+Kimberly Kroeger-Geoppinger Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
+Steve Kroupa School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
+Graduate School of Human-Environment Studies, Kyushu University, Fukuoka, Japan
+Lydia Kruse Human Development and Family Science, Schoenbaum Family Center, The Ohio State University, Columbus, OH, USA
+S. Jay Kuder Department of Interdisciplinary and Inclusive Education, College of Education, Rowan University, Glassboro, NJ, USA
+Grace Kuravackel Pediatrics, University of Louisville, Louisville, KY, USA
+Sarah Kuriakose Department of Counseling, Clinical, and School Psychology (CCSP), University of California, Santa Barbara, CA, USA
+Hiroshi Kurita Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
+Onur Kurt Research Institute for Individuals with Disabilities, Anadolu University, Eskisehir, Turkey
+Emily S. Kuschner Center for Autism Spectrum Disorders, Division of Neuropsychology, Children’s National Medical Center, Washington, DC, USA
+Metehan Kutlu Department of Special Education, Hakkari University, Hakkari, Turkey
+Jennifer M. Kwon Department of Neurology and Pediatrics (SMD), University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA
+Hidemi Kyotani Centre for Autism Research, Technology and Education, Department of Psychology, Victoria, Canada
+Szu-Shen Lai Department of Physical Medicine and Rehabilitation, Taoyuan Chang Gung Memorial Hospital, Taoyuan City, Taiwan
+Chee Meng Lam Autism Resource Centre, Singapore, Singapore
+Kristen Lam UNC Neurodevelopmental Disorders Research Center, UNC-Chapel Hill, Chapel Hill, NC, USA
+Rebecca Landa Center for Autism and Related Disorders, Kennedy Krieger Institute’s, Baltimore, MD, USA
+Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
+Chloe Lane Department of Psychology, University of Sheffield, Sheffield, UK
+Russell Lang Clinic for Autism Research Evaluation and Support, Texas State University, San Marcos, TX, USA
+Traci Lanner The School at Springbrook, Oneonta, NY, USA
+Kyle Lanning Quinnipiac University School of Law, Hamden, CT, USA
+Nathaniel Laor Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
+Department of Medical Education, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
+Yale Child Study Center, New Haven, CT, USA
+Association for Children at Risk (R.A.), Tel Aviv, Israel
+Amanda P. Laprime The Center for Children with Special Needs, Glastonbury, CT, USA
+University of Rochester Medical Center, Rochester, NY, USA
+Kenneth Larsen Oslo University Hospital, Oslo, Norway
+Robert H. LaRue Douglass Developmental Disabilities Center, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
+Susan Latham Department of Communication Disorders, St. Mary’s College (IN), Notre Dame, IN, USA
+Elizabeth Laugeson UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
+Margaret Holmes Laurie Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
+Tara A. Lavelle Center for Value and Risk in Health (CEVR), Tufts Medical Center, Boston, MA, USA
+J. Kiely Law Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, USA
+Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
+Kathy Lawton Special Education and Nisonger Center, The Ohio State University, Columbus, OH, USA
+Kathy Leadbitter Social Development Research Group, University of Manchester, Manchester, UK
+Geraldine Leader Irish Centre for Autism and Neurodevelopmental Research (ICAN), National University of Ireland, Galway (NUI Galway), Galway, Ireland
+Justin B. Leaf Autism Partnership Foundation, Seal Beach, CA, USA
+Ronald Leaf Autism Partnership Foundation, Seal Beach, CA, USA
+Eli R. Lebowitz Yale School of Medicine, Child Study Center, Yale University, New Haven, CT, USA
+Emma Lecarie Yale Child Study Center, New Haven, CT, USA
+Luc Lecavalier Nisonger Center, Ohio State University, Columbus, OH, USA
+Ann S. Le-Couteur Institute of Health and Society, Sir James Spence Institute, Newcastle University, Royal Victoria Infirmary, Newcastle upon Tyne, UK
+Katherine Ledbetter-Cho Clinic for Autism Research Evaluation and Support, Texas State University, San Marcos, TX, USA
+Elinda Ai Lim Lee School of Occupational Therapy, Social Work and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
+Curtin Autism Research Group, Curtin University, Perth, WA, Australia
+Evon Batey Lee Pediatrics, Kennedy Center/Vanderbilt University, Nashville, TN, USA
+Hoe Lee School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+James Hyun Lee Mayo Clinic School of Medicine, Rochester, MN, USA
+Jordan Lee Southern Connecticut State University, New Haven, CT, USA
+Michelle Lee NYU School of Medicine, New York, NY, USA
+Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
+Su Mei Lee Child Neuroscience Lab, Yale Child Study Center, New Haven, CT, USA
+Susan Leekam School of Psychology, Cardiff University, Cardiff, UK
+Jiedi Lei Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, UK
+Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
+Michelle Lestrud The Gengras Center, University of Saint Joseph, West Hartford, CT, USA
+Cecilia Nga Wing Leung The Jockey Club iREACH Social Competence Development and Employment Support Center, New Life Psychiatric Rehabilitation Association, Kowloon, Hong Kong
+Bennett Leventhal Nathan Kline Institute for Psychiatric Research (NKI), Orangeburg, NY, USA
+Harriet Levin University of Connecticut, Storrs, CT, USA
+Philip Levin The Help Group – UCLA Neuropsychology Program, Los Angeles, CA, USA
+Michael Levine Quinnipiac University School of Law, Hamden, CT, USA
+Brianna Lewis Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
+Laura Foran Lewis College of Nursing and Health Sciences, University of Vermont, Burlington, VT, USA
+Mark Lewis College of Medicine, University of Florida, Gainesville, FL, USA
+Michael Lewis Department of Pediatrics, Institute for the Study of Child Development, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
+Moira Lewis Speech-Language Pathologist, Marcus Autism Center Children’s Healthcare of Atlanta, Atlanta, GA, USA
+Boxing Li Neuroscience Program, Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
+Ya-Min Li Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
+Yong-Jiang Li Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
+Diane M. Lickenbrock Human Development and Family Studies, The Pennsylvania State University, University Park, PA, USA
+Rebecca Lieb NeuroDevelopmental Science Center, Akron Children’s Hospital, Akron, OH, USA
+Joan Lieber Counseling, Higher Education and Special Education, University of Maryland, College Park, MD, USA
+Nataly Lim University of Texas at Austin, Austin, TX, USA
+Sok Bee Lim Department of Child Development, KK Women’s and Children’s Hospital, Singapore, Singapore
+Yi Huey Lim School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
+Charlotte Limosani Department of Communication Disorders, Southern Connecticut State University, New Haven, CT, USA
+Christie Enjey Lin Departments of Education and Psychiatry, Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
+Sigvard Lingh Uppsala, Sweden
+Karen M. Lionello-DeNolf Psychology Department, Assumption College, Worcester, MA, USA
+Paul H. Lipkin Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, USA
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+Guodong Liu Division of Health Services and Behavioral Research, Department of Public Health Sciences, The Pennsylvania State University, College of Medicine, Hershey, PA, USA
+Ting Liu Department of Health and Human Performance, Texas State University, San Marcos, TX, USA
+Patricia Sánchez Lizardi School of Psychology, Universidad Panamericana, Mexico City, Mexico
+Ella Lobregt-van Buuren Dimence Institute of Mental Health, Deventer, The Netherlands
+Rachel Loftin AARTS Center, Rush University Medical Center, Chicago, IL, USA
+Andrew Lolli Quinnipiac University School of Law, Hamden, CT, USA
+Michael Lombardo Autism Research Centre, University of Cambridge, Cambridge, UK
+Steven Long Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA
+James W. Loomis Center for Children with Special Needs, Glastonbury, CT, USA
+Amaia Lopetegui GAUTENA, Donostia, Gipuzkoa, Spain
+Catherine Lord Center for Autism and the Developing Brain, New York-Presbyterian Hospital/Westchester Division, White Plains, NY, USA
+UCLA, Los Angeles, CA, USA
+Erin Loring Yale Department of Genetics, New Haven, CT, USA
+Molly Losh The Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
+Susan Luger Susan Luger Associates, New York, NY, USA
+James Luiselli May Institute, Randolph, MA, USA
+Jan Łukasik Department of Paediatrics, The Medical University of Warsaw, Warsaw, Poland
+Joyce Lum UNC TEACCH Autism Program-Charlotte, Charlotte, NC, USA

assets/Books Chunks/Encyclopedia of autism spectrum disorders/book0_cleaned_chunk_100.txt

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+reports include defensive reactions and lower pleasantness ratings to tactile stimuli (Tomchek and Dunn 2007). Abnormal responses to touch in ASD seem to be present, prodomally, in the first year of life (Kadlaskar et al. 2019). Specifically, infants who are later diagnosed with ASD fail to shift their attention in response to caregiver touch, and even when they do attend to their mother’s touch, they seem to orient away from it. In addition, infants who attend less to caregiver’s touch at 12 months old present more ASD symptomatology as measured by the autistic diagnostic observation tool (ADOS) at age 3 (Kadlaskar et al. 2019). There seems to be evidence of tactile disfunction early in ASD, but the abnormal responses are heterogeneous and may have different etiologies. Missing tactile stimuli in the environment (sensory hyporesponsiveness) has been found previously in young children with ASD (Foss-Feig et al. 2012). However, there are other mechanisms by which sensory impairments may relate to symptoms of ASD. For example, the degree to which a child seeks (sensory seeking) or is bothered by tactile stimuli (sensory hyperresponsiveness) can also result in failing to attend to appropriate stimuli in the environment and consequently result in missed learning opportunities (Baranek et al. 2007). Atypical behavioral responses to touch may directly impact the way children and adolescents interact with others, both contemporaneously, and over time. It is hypothesized that children engaging in sensory seeking behaviors are too distracted with enhancing their sensory experiences and therefore miss social cues in their environment (Foss-Feig et al. 2012). Others suggest that hyperresponsiveness/tactile defensiveness is the pattern that mostly affects the ability to socialize in ASD (Miguel et al. 2017) and is in fact one of the patterns of behavior that best differentiates ASD from typically developing children. However, hyporesponsiveness to touch seems to be the pattern that gathers greater consensus among researchers on the cascading effects on development over time (Foss-Feig et al. 2012; Miguel et al. 2017). Children who miss tactile experiences due to hyporesponsiveness will have fewer opportunities to engage meaningfully and learn from their physical environment and social experiences.
+
+The relationship between tactile and social processing in ASD may occur at multiple levels, and it is not yet well understood where in the biological hierarchy (e.g., sensory receptors, perception, attention, cognition) there is a breakdown that results in ASD behavioral characteristics (phenotype). Some studies point to differences in how tactile stimuli are processed at a peripheral level, namely, touch detection and stimuli discrimination, but findings are inconclusive (Riquelme et al. 2016). Others suggest differences in neurotransmitters – specifically GABAergic function (the major inhibitory neurotransmitter in the central nervous system), crucial for balance between excitatory and inhibitory tactile input at a cortical level (Tavassoli et al. 2016). A specific class of tactile mechanoreceptors, C-tactile (CT) neurons, involved in encoding the valence/pleasantness of a stimulus (Loken et al. 2009), have been hypothesized to underlie the atypical responses in ASD. By encoding the valence of a stimulus, these fibers are intrinsically related to social cognition and our ability to bond with others. Children with ASD present more aversive reactions to touch delivered to CT-innervated body regions, such as the face and arm, compared to non-CT-innervated regions, such as the palm of the hand (Cascio et al. 2016). Moreover, at a brain level, children with ASD show diminished responses in social-affective brain networks compared to typically developing children following CT-targeted touch stimulation (Kaiser et al. 2016). Touch processing seems to be altered in ASD; however, how heterogeneous behavioral responses to touch are related to social-communication problems in ASD is under deliberation. Atypical touch processing, whether characterized as hyporesponsiveness, sensory seeking, or hyperresponsiveness, can result in reduced exposure to important stimuli causing an altered trajectory of the developing social brain in infancy. Altered tactile processing emerges before many of the social and communicative impairments associated with ASD and has strong theoretical grounding as a developmental precursor to social-communicative behavior. By understanding the behaviors and mechanisms that underlie atypical touch processing, we can design and improve diagnostic and treatment tools for these individuals.
+
+Acknowledgements
+This research was supported (in part) by the Intramural Research Program of the NIMH (1ZICMH00296) and by the Intramural Research Program of NICHD.
+
+References and Reading
+APA. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.
+Baranek, G. T., Boyd, B. A., Poe, M. D., David, F. J., & Watson, L. R. (2007). Hyperresponsive sensory patterns in young children with autism, developmental delay, and typical development. American Journal of Mental Retardation, 112(4), 233–245. https://doi. org/10.1352/0895-8017(2007)112[233:HSPIYC] 2.0.CO;2.
+Cascio, C. J., Gu, C., Schauder, K. B., Key, A. P., & Yoder, P. (2015). Somatosensory event-related potentials and association with tactile behavioral responsiveness patterns in children with ASD. Brain Topography, 28(6), 895–903. https://doi.org/10.1007/s10548-015-0439-1.
+Cascio, C. J., Lorenzi, J., & Baranek, G. T. (2016). Self-reported pleasantness ratings and examiner-coded defensiveness in response to touch in children with ASD: Effects of stimulus material and bodily location. Journal of Autism and Developmental Disorders, 46(5), 1528–1537. https://doi.org/10.1007/s10803-013-1961-1.
+Foss-Feig, J. H., Heacock, J. L., & Cascio, C. J. (2012). Tactile responsiveness patterns and their association with Core features in autism Spectrum disorders. Research in Autism Spectrum Disorder, 6(1), 337–344. https://doi.org/10.1016/j.rasd.2011.06.007.
+Gallace, A., & Spence, C. (2010). The science of interpersonal touch: An overview. Neuroscience and Biobehavioral Reviews, 34(2), 246–259. https://doi.org/10. 1016/j.neubiorev.2008.10.004.
+Hertenstein, M. J., Verkamp, J. M., Kerestes, A. M., & Holmes, R. M. (2006). The communicative functions of touch in humans, nonhuman primates, and rats: A review and synthesis of the empirical research. Genetic, Social, and General Psychology Monographs, 132(1), 5–94. Retrieved from http://www.ncbi.nlm.nih. gov/pubmed/17345871.
+Hilton, C. L., Harper, J. D., Kueker, R. H., Lang, A. R., Abbacchi, A. M., Todorov, A., & LaVesser, P. D. (2010). Sensory responsiveness as a predictor of social severity in children with high functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 40(8), 937–945. https://doi.org/10.1007/ s10803-010-0944-8.
+Kadlaskar, G., Seidl, A., Tager-Flusberg, H., Nelson, C. A., & Keehn, B. (2019). Atypical response to caregiver touch in infants at high risk for autism Spectrum disorder. Journal of Autism and Developmental Disorders, 49(7), 2946–2955. https://doi.org/10.1007/s10803- 019-04021-0.
+Kaiser, M. D., Yang, D. Y., Voos, A. C., Bennett, R. H., Gordon, I., Pretzsch, C., et al. (2016). Brain mechanisms for processing affective (and nonaffective) touch are atypical in autism. Cerebral Cortex, 26(6), 2705–2714. https://doi.org/10.1093/cercor/bhv125.
+Lane, A. E., Young, R. L., Baker, A. E., & Angley, M. T. (2010). Sensory processing subtypes in autism: Association with adaptive behavior. Journal of Autism and Developmental Disorders, 40(1), 112–122. https://doi. org/10.1007/s10803-009-0840-2.
+Loken, L. S., Wessberg, J., Morrison, I., McGlone, F., & Olausson, H. (2009). Coding of pleasant touch by unmyelinated afferents in humans. Nature Neuroscience, 12(5), 547–548. https://doi.org/10.1038/nn.2312.
+Miguel, H. O., Sampaio, A., Martinez-Regueiro, R., Gomez-Guerrero, L., Lopez-Doriga, C. G., Gomez, S., et al. (2017). Touch processing and social behavior in ASD. Journal of Autism and Developmental Disorders, 47(8), 2425–2433. https://doi.org/10.1007/s10803-017- 3163-8.
+Morrison, I., Loken, L. S., & Olausson, H. (2010). The skin as a social organ. Experimental Brain Research, 204(3), 305–314. https://doi.org/10.1007/s00221-009-2007-y.
+Riquelme, I., Hatem, S. M., & Montoya, P. (2016). Abnormal pressure pain, touch sensitivity, proprioception, and manual dexterity in children with autism Spectrum disorders. Neural Plasticity, 2016, 1723401. https:// doi.org/10.1155/2016/1723401.
+Tavassoli, T., Miller, L. J., Schoen, S. A., Nielsen, D. M., & Baron-Cohen, S. (2014). Sensory over-responsivity in adults with autism spectrum conditions. Autism, 18(4), 428–432. https://doi.org/10.1177/1362361313477246.
+Tavassoli, T., Bellesheim, K., Tommerdahl, M., Holden, J. M., Kolevzon, A., & Buxbaum, J. D. (2016). Altered tactile processing in children with autism spectrum disorder. Autism Research, 9(6), 616–620. https://doi. org/10.1002/aur.1563.
+Tomchek, S. D., & Dunn, W. (2007). Sensory processing in children with and without autism: A comparative study using the short sensory profile. The American Journal of Occupational Therapy, 61(2), 190–200. https://doi. org/10.5014/ajot.61.2.190.
+
+Touch Sensitivity
+M. Kristen
+Center for Children with Special Needs, Glastonbury, CT, USA
+
+Synonyms
+Sensory integration and praxis tests (SIPT); Sensory processing; Tactile sensitivity
+
+Definition
+Touch sensitivity, or sensitivity to tactile stimuli, has been noted in children with autism spectrum disorders (Baranek et al. 1997). Children with tactile hypersensitivity may show a reluctance to manipulate various toys, materials, or foods based on their texture or may exhibit exaggerated or persistent responses to unexpected touch occurrences and may even avoid situations in which these events or unusual textures may occur. Some children may be hypervigilant to the potential threat of unexpected touch which may ultimately compromise successful participation in a designated activity. Clothing selection, play materials, and food choices may also be restricted if they are perceived to be unpleasant to the individual. Some individuals may exhibit hyposensitivity to touch or an underreaction to touch occurrences. These individuals may display a more passive response to tactile stimuli and may not notice when clothing is twisted on their body or food is left on their face. Tool usage may also be affected as the individual may not have adequate feedback in the hand for effective manipulation.
+
+See Also
+▶Tactile Defensiveness
+
+References and Reading
+Ayres, A. (1972). Sensory integration and learning disabilities. Los Angeles: Western Psychological Services.
+Ayres, A. (1973). Sensory integration and learning disorders. Los Angeles: Western Psychological Services.
+Ayres, A. (1979). Sensory integration and the child. Los Angeles: Western Psychological Services.
+Ayres, A. J. (1985). Developmental Dyspraxia and adult onset apraxia. Torrance: Sensory Integration International.
+Baranek, G. T., Foster, L. G., & Berkson, G. (1997). Tactile defensiveness and stereotypic behaviors. American Journal of Occupational Therapy, 51, 91–95.
+Parham, L. D., & Mailloux, Z. (2001). Sensory integration. In J. Case-Smith (Ed.), Occupational therapy for children (4th ed.). St. Louis: Mosby.
+Tomchek, S. D. (2010). Sensory processing in individuals with an autism spectrum disorder. In H. M. Kuhaneck & R. Watling (Eds.), Autism: A comprehensive occupational therapy approach (pp. 135–161). Bethesda: AOTA Press.
+
+Tourette Disorder
+▶Tourette Syndrome
+
+Tourette Syndrome
+Michael Bloch
+Yale OCD Research Clinic, New Haven, CT, USA
+
+Synonyms
+Gilles de la Tourette syndrome; Tourette disorder; Tourette’s syndrome
+
+Short Description or Definition
+Tourette syndrome is a childhood-onset neuropsychiatric disorder characterized by multiple motor and vocal tics that occur for at least a year in duration.
+
+Categorization
+Tic Disorder
+
+Epidemiology
+Transient tics, which have a duration of less than 6 months, are common in childhood. Estimates suggest that 4–24% of school-aged children experience tics (Khalifa and von Knorring 2003; Scahill et al. 2005). Roughly one-quarter of children experience chronic motor or vocal tics. Chronic tics have a duration of at least 1 year. Once thought to be much rarer, the current lifetime prevalence estimates for Tourette syndrome ranges from 0.1% to 1%. Tic disorders are much more common in children with pervasive developmental disorders and in special education populations than would be expected by chance (Canitano and Vivanti 2007; Kurlan et al. 2001). Studies have estimated that the occurrence of Tourette syndrome may be greater than 10% among autistic children (Canitano and Vivanti 2007). Prevalence of tic disorders peak during the late first decade and early second decades of life due to the clinical course of the disorder and are roughly one-third as prevalent in adulthood (Bloch and Leckman 2009; Bloch et al. 2006; Leckman et al. 1998). There is a male predominance of TS with boys about twice as likely to be affected by tic disorders as girls (Scahill et al. 2005).
+
+Natural History, Prognostic Factors, and Outcomes
+The onset of TS is usually characterized by the appearance of simple, transient motor tics that affect the face (typically eye blinking) around the age of 5–7 (Leckman et al. 1998). Over time, these simple motor tics generally progress in a rostrocaudal direction affecting other areas of the face, followed by the head, neck, arms, and, lastly and less frequently, the lower extremities. With time, vocal tics often appear, tics become increasingly complex, and premonitory urges appear. Premonitory urges are feelings of tightness, tension, or itching that are accompanied by a mounting sense of discomfort or anxiety that can be relieved only by the performance of a tic (Leckman et al. 1993). With increasing awareness of premonitory urges, TS patients begin to exhibit a variable degree of voluntary control over tic performance. However, this voluntary control should be likened to that governing control of eye blinking. Eye blinking and tics can both be inhibited voluntarily, but only for a limited period of time and only with mounting discomfort. Thus, some adult TS patients are able to demonstrate nearly complete control over when expression of their tics will occur. However, when complete or near complete control of tics is present, resistance to the mounting tension of premonitory urges can produce mental and physical exhaustion even more impairing and distracting than the tics themselves.
+
+The severity of tics in TS waxes and wanes throughout the course of the disorder (Peterson and Leckman 1998). Tics are highly variable from minute to minute, hour to hour, day to day, week to week, and month to month. Tic episodes occur in bouts, which in turn also tend to cluster. Tic symptoms, however, can be exacerbated by stress, fatigue, extremes of temperature, and external stimuli (i.e., in echolalia tics). Intentional movements attenuate tic occurrence over the affected area, and intense involvement and concentration in activities tends to dissipate tic symptoms. TS symptoms generally peak in severity between the ages of ten and twelve. Tic severity typically begins to decrease with the onset of adolescence (Bloch et al. 2006; Leckman et al. 1998). Reduction in tic severity generally ends by the early twenties. Although a small minority of TS patients does experience catastrophic outcomes in adulthood, on the whole, individuals rarely experience either a sustained worsening or improvement of their symptoms after their mid-20s. One-half to two-thirds of individuals with TS experience a marked reduction of symptoms by their late teens and early twenties, with one-third to one-half of these patients becoming virtually asymptomatic in adulthood.
+
+Evaluation and Differential Diagnosis
+A diagnosis of Tourette syndrome or chronic tic disorders is made through clinical history and observation. A childhood onset, the waxing-and-waning severity, and changing nature of tic symptoms are critical in establishing the diagnosis. In older children, the presence of premonitory urges and the exacerbation of tics in response to stress and fatigue are also helpful clues in establishing the diagnosis. In children, tics often need to be distinguished from stereotypies of stereotyped movement disorders or pervasive developmental disorders. Stereotypies typically have an earlier age of onset than tics, are bilateral rather than unilateral, and have a soothing quality. Table 1 contrasts the attributes of tics and stereotypies.
+
+| Tics | Stereotypies |
+|---|---|
+| Typical age of onset | 4–8 Years | 2–3 Years |
+| Course of symptoms | Waxing and waning | Constant |
+| Timing of movements | Brief and sudden | Continuous, rhythmic, and prolonged |
+| Typical movements | Eye blinking, facial grimace, throat clearing | Arm flapping, rocking |
+| Characteristics of movements | Typically unilateral | Often bilateral |
+| Exacerbated by. . . | Stress, fatigue | Excitement |
+| Premonitory urges | Present | Absent |
+| Suppressibility | Often for short periods of time | Rare |
+| Comorbid conditions | OCD, ADHD | PDD, autism spectrum disorders |
+| Treatment | Neuroleptic, α2 agonists | No response to medication |
+
+Complex tics often also need to be distinguished from compulsions of obsessive-compulsive disorder. Making this particularly difficult is the high comorbidity between the two conditions. Compulsions are usually performed in response to an obsession and preceded by anxiety, worry, or concern, whereas tics are generally performed in response to a physical sensation or premonitory urges. Compulsions typically are more elaborate than tics and are more likely to resemble “normal” behavior. Often, both a diagnosis of OCD and a tic disorder are warranted in the same individual. The tics, which are the most prominent feature of TS, are often neither the first nor the most impairing psychological disturbance TS patients endure. Thus, a thorough evaluation for common comorbid conditions is also critical (Scahill et al. 2006). In screening for comorbidities in TS, ADHD, OCD, disruptive behaviors, and pervasive developmental disorders are of principle importance. Children with TS have higher rates of OCD, ADHD, PDD, and disinhibited speech and behavior compared to the general population. In the natural course of comorbid psychiatric illness in TS, ADHD symptoms, when they occur typically, precede the onset of tic symptoms by a couple of years, whereas OC symptoms typically present around the age of 12–13 after tics have reached their peak severity (Bloch et al. 2006). Approximately half of children with Tourette syndrome experience comorbid ADHD and an even greater proportion of children with tic disorders reaching clinical attention. Roughly one-third to one-half of Tourette syndrome patients will experience clinically significant OCD symptoms during the course of their lifetime.
+
+Educating the patient, his family members, teachers, and peers is among the most important interventions available to the clinician. It should be undertaken for nearly all patients with tic disorders. Family psychoeducation should focus on:
+1. The tics are not voluntary or meant to be intentionally provocative. They typically occur in “bouts” when tics will appear in rapid succession followed by a tic-free interval.
+2. Premonitory urges – the physical sensations like an itch or before a sneeze that many patients experience prior to performing tics. TS is a sensorimotor disorder. There is also a momentary sense of relief that follows the completion of the tic.
+3. Suppressibility of tics – many patients are able to suppress their tics over the short term, but often this is at the expense of increasing discomfort and distraction due to the premonitory urges. So it is not uncommon for a child to have relatively minor tics at school and then return home to let loose a bout of tics.
+4. The natural waxing-and-waning course of tic symptoms – that there will be increases and decreases in tic severity over time. Due to the natural ebb and flow of symptoms in the illness, any interventions started during an exacerbation of tic severity may appear successful due to a natural decline in symptom severity.
+5. The natural history of tic disorders – the illness tends not to be relentlessly progressive and symptoms usually improve by adulthood. This information often contradicts the impressions gained from the available lay literature on TS that typically focuses on the most extreme cases – which do typically occur in adulthood.
+6. Exacerbating and alleviating factors – tics tend to be exacerbated by fatigue, sleeplessness, stress, excitement, and possibly changes in temperature and streptococcal infections. Tics often tend to be alleviated when a child is deeply engaged in a motor activity such as sports, playing musical instruments, and dancing. The exact exacerbating and alleviating factors are highly individualized to the patient. Healthy habits such as good sleep hygiene and regular exercise can only improve tic symptoms.
+7. Education on comorbid ADHD, OCD, learning disabilities, and disruptive or disinhibited behaviors when present. Improving these disorders often leads to an improvement in the tics.
+8. Provide resources to help educate parents and teachers about tic disorders. These are available at the Tourette Syndrome Association website: http://www.tsa-usa.org and http:// www.tourettesyndrome.net.
+
+Treatment
+Given the waxing and waning course of tics and the decline in tic severity that usually occurs in adolescence, any intervention performed in response to symptom exacerbation will usually be followed by clinical improvement. The goal of treatment should be minimizing the level of social and educational impairment caused by tics rather than completely eliminating tics, which will be unsuccessful in the majority of cases. Many cases of TS can be successfully managed without medication. When coexisting conditions such as ADHD, OCD, depression, or ODD are present, it is usually better to treat these “comorbid” conditions first, as successful treatment of these disorders often will diminish tic severity (Scahill et al. 2006).
+
+Pharmacologic Interventions
+Antipsychotic medications, dopamine D2 receptor antagonists, are the most effective tic-suppressing medications (Scahill et al. 2006; Singer 2010). Among the typical antipsychotics, haloperidol and pimozide have the most studied and have the most convincing data demonstrating efficacy. Although antipsychotics have the greatest efficacy in treating tics, they are generally not recommended as first-line treatment because of poor tolerability. Common potential side effects include tardive dyskinesia, acute dystonic reactions, sedation, depression, school and social phobias, and/or weight gain. In many instances by starting at low doses and adjusting the dosage upward slowly, clinicians can avoid these side effects. The goal should be to use as little of these medications as possible to render the tics “tolerable.” Efforts to stop the tics completely often risk overmedication and increased side effects. Due to the extrapyramidal side effects associated with typical antipsychotics, atypical antipsychotics, such as risperidone, olanzapine, and ziprasidone, are now the most widely used medications to treat tic symptoms. These agents have potent 5-HT2 blocking effects as well as more modest blocking effects on dopamine D2. There are currently double-blind clinical trials that have supported the efficacy of risperidone, olanzapine, and ziprasidone (Singer 2010). Atypical antipsychotics, especially olanzapine and risperidone, are associated with significant weight gain and sedation and increased risk of diabetes. Ziprasidone use can be associated with QT prolongation in children; so serial monitoring with electrocardio-grams may be necessary. Due to the significant metabolic side effects associated with atypical antipsychotics, even though they are more effective at treating tics than other classes of medications, they are not recommended as a first-line intervention.
+
+Clonidine and guanfacine are potent α2 receptor agonists that are thought to reduce central noradrenergic activity. Although less effective in relieving tics compared to the antipsychotic medication, α2 receptor agonists have the advantage of being better tolerated and also improving comorbid ADHD symptoms in patients with tics (Scahill et al. 2006; Singer 2010). The principal side effect associated with clonidine use is sedation which occurs in 10–20% of subjects and which usually abates with continued use. Other side effects include dry mouth, transient hypotension, and rare episodes of worsening behavior. Clonidine should be tapered and not withdrawn abruptly, to reduce the likelihood of symptom or blood pressure rebound. Guanfacine is generally preferred to clonidine because it is less sedating and not associated with rebound hypertension following withdrawal. When comorbid ADHD is the most impairing illness in a patient presenting with comorbid tics, the stimulants methylphenidate and dextroamphetamine derivatives are still first-line agents for the medical management of ADHD. Although clinical trials of meta-analysis of psychostimulant use in children with ADHD and comorbid tics suggest that these medications do not worsen tic severity, there still exists an FDA warning against the use of psychostimulants in children with tic disorders or a family history of TS (Bloch et al. 2009). This FDA warning was exclusively based on data from clinical case reports and case series that reported worsening of tics when children were exposed to psychostimulants. For this reason, non-stimulant treatments are often used in the treatment of children with ADHD and comorbid tics. α2 receptor agonists have the advantage of demonstrated efficacy for both ADHD and tic symptoms. Furthermore, the combination of clonidine and methylphenidate has been demonstrated to be more effective in treating ADHD symptoms in children with comorbid tics than either medication alone. Atomoxetine has also been demonstrated to be effective in the treatment of ADHD symptoms in children with comorbid tics and may also be modestly helpful in the treatment of tics.
+
+Behavioral
+Behavioral therapy has shown significant promise in the treatment of tic disorders. Behavioral therapy has been demonstrated to be significantly more effective than supportive therapy in reducing tic severity in children with TS in a large, randomized, multicenter trial using blinded raters (Piacentini et al. 2010). Behavioral therapy has also been shown to significantly reduce tic symptoms in adults with TS when compared to supportive therapy in randomized and unblended trials (Deckersbach et al. 2005; Wilhelm et al. 2003). The measured effects of behavioral therapy in reducing tic severity have been similar to the most effective pharmacological agents used to treat tics. Behavioral therapy for tics consists of two main components: (1) awareness training and (2) competing response practice (Woods 2001). Awareness training consists of 4 components designed to increase an individual’s awareness of his own tics. These components include the following: (1) response description, in which the patient learns how to describe tic movements and reenacts them into a mirror; (2) response detection, in which the therapist aids the patient in tic detection by pointing out each tic immediately after it occurs in the session; (3) early warning procedure, in which an individual learns how to identify the earliest signs of tic occurrence; and (4) situational awareness training, in which an analysis is conducted to identify the high-risk situations where tics are most likely to occur. Competing response practice involves teaching individuals to produce an incompatible physical response (i.e., isometric contraction of tic-opposing muscles) contingent upon the urge to perform a tic. Although behavioral therapy has significant evidence of efficacy and does not have the side effect burden of the medications used to treat tics, dissemination remains a significant barrier to the widespread implementation of this treatment for tics. Furthermore, the efficacy of behavioral therapy has not been demonstrated in special populations of children with tic disorders such as those with comorbid ADHD, pervasive developmental disorders, or intellectual disability.
+
+See Also
+▶Attention Deficit/Hyperactivity Disorder
+▶Obsessive-Compulsive Disorder (OCD)
+
+References and Reading
+Bloch, M. H., & Leckman, J. F. (2009). Clinical course of Tourette syndrome. Journal of Psychosomatic Research, 67(6), 497–501.
+Bloch, M. H., Peterson, B. S., Scahill, L., Otka, J., Katsovich, L., Zhang, H., et al. (2006). Adulthood outcome of tic and obsessive-compulsive symptom severity in children with Tourette syndrome. Archives of Pediatrics & Adolescent Medicine, 160(1), 65–69.
+Bloch, M. H., Panza, K. E., Landeros-Weisenberger, A., & Leckman, J. F. (2009). Meta-analysis: Treatment of attention-deficit/hyperactivity disorder in children with comorbid tic disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 48(9), 884–893.
+Canitano, R., & Vivanti, G. (2007). Tics and Tourette syndrome in autism spectrum disorders. Autism, 11(1), 19–28.
+Deckersbach, T., Rauch, S., Buhlmann, U., & Wilhelm, S. (2005). Habit reversal versus supportive psychotherapy in Tourette’s disorder: A randomized controlled trial and predictors of treatment response. Behaviour Research and Therapy, 44(8), 1079–1090.
+Khalifa, N., & von Knorring, A. L. (2003). Prevalence of tic disorders and Tourette syndrome in a Swedish school population. Developmental Medicine and Child Neurology, 45(5), 315–319.
+Kurlan, R., McDermott, M. P., Deeley, C., Como, P. G., Brower, C., Eapen, S., et al. (2001). Prevalence of tics in schoolchildren and association with placement in special education. Neurology, 57(8), 1383–1388.
+Leckman, J. F., Walker, D. E., & Cohen, D. J. (1993). Premonitory urges in Tourette’s syndrome. The American Journal of Psychiatry, 150(1), 98–102.
+Leckman, J. F., Zhang, H., Vitale, A., Lahnin, F., Lynch, K., Bondi, C., et al. (1998). Course of tic severity in Tourette syndrome: The first two decades. Pediatrics, 102(1 Pt 1), 14–19.
+Peterson, B. S., & Leckman, J. F. (1998). The temporal dynamics of tics in Gilles de la Tourette syndrome. Biological Psychiatry, 44(12), 1337–1348.
+Piacentini, J., Woods, D. W., Scahill, L., Wilhelm, S., Peterson, A. L., Chang, S., et al. (2010). Behavior therapy for children with Tourette disorder: A randomized controlled trial. Journal of the American Medical Association, 303(19), 1929–1937.
+Scahill, L., Sukhodolsky, D. G., Williams, S. K., & Leckman, J. F. (2005). Public health significance of tic disorders in children and adolescents. Advances in Neurology, 96, 240–248.
+Scahill, L., Erenberg, G., Berlin, C. M., Jr., Budman, C., Coffey, B. J., Jankovic, J., et al. (2006). Contemporary assessment and pharmacotherapy of Tourette syndrome. NeuroRx, 3(2), 192–206.
+Singer, H. S. (2010). Treatment of tics and Tourette syndrome. Current Treatment Options in Neurology, 12(6), 539–561.
+Wilhelm, S., Deckersbach, T., Coffey, B. J., Bohne, A., Peterson, A. L., & Baer, L. (2003). Habit reversal versus supportive psychotherapy for Tourette’s disorder: A randomized controlled trial. The American Journal of Psychiatry, 160(6), 1175–1177.
+Woods, D. W. (2001). Habit reversal treatment manual for tic disorders. In D. W. Woods & R. M. Miltenberger (Eds.), Tic disorders, Trichotillomania, and other repetitive behavior disorders: Behavioral approaches to analysis and treatment (pp. 97–132). Boston: Kluwer Academic Publishers.
+
+Tourette’s Syndrome
+▶Tourette Syndrome
+
+Toxicology
+Susan Hyman
+Developmental and Behavioral Pediatrics, Division Chief Neurodevelopmental and Behavioral Pediatrics, University of Rochester Golisano Children’s Hospital, Rochester, NY, USA
+
+Synonyms
+Teratology
+
+Definition
+Toxicology is the branch of science that studies the adverse effects of chemicals and environmental exposures on living organisms. It includes aspects of biology, chemistry, and medicine in the scientific approaches used. It studies the symptoms, mechanisms, measurement, and treatment of poisonous substances that people and other living things are exposed to. The science of toxicology identifies chemical compounds and environmental events that negatively impact health. It was initially considered as the study of poisons and has been expanded with the technological impact on the environment. The study of dose and effect is central to toxicology. Many compounds, such as lead, are toxic or harmful above certain levels of exposure. The risk for injury increases with the level of exposure. Toxicity refers to the biologic effect of a substrate or compound. The Society of Toxicology defines toxicology as “the study of the adverse effects of chemical, physical, or biologic agents on living organisms and the ecosystem, including the prevention and amelioration of such adverse effects.” Toxicologists are important to the study of the potential role of exposures to environmental agents such as air pollution and maternal use of medication such as selective serotonin reuptake inhibiters during brain development. The scientists who study toxicology approach question regarding the safety of compounds and events living things are exposed to from the fields of biology, chemistry, medicine, and environmen-tal science, among others. Neurotoxicologists study the effects of chemicals and environmental events on the brain and brain development.
+
+See Also
+▶Metallothionein
+▶Thalidomide
+
+References and Reading
+Halladay, A. K., Amaral, D., Ascher, M., Bolvar, V. J., Bowman, A., DiCicco, B. E., et al. (2009). Animal models of autism spectrum disorder: Information for neurotoxicologists. Neurotoxicology, 30(5), 811–821.
+Kaplan, Y. C., Keskin-Arslan, E., Acar, S., & Sozmen, K. (2017). Maternal SSRI discontinuation, use, psychiatric disorder and the risk of autism in children: a meta-analysis of cohort studies. British Journal of Clinical Pharmacology, 83(12), 2798. PMID:28734011.
+Lam, J., Sutton, P., Kalkbrenner, A., Windham, G., Halladay, A., Koustas, E., Lawler, C., Davidson, L., Daniels, N., Newschaffer, C., & Woodruff, T. (2016). A systematic review and meta-analysis of multiple airborne pollutants and autism Spectrum disorder. PLoS One, 11(9), e0161851. https://doi.org/10.1371/ journal.pone.0161851. PMID:27653281.
+Society for Toxicology. www.toxicology.org. Accessed 24 Sept 2017.
+Winneke, G. (2011). Developmental aspects of environ-mental neurotoxicology: Lessons from lead and polychlorinated biphenyls. Journal of the Neurological Sciences, 308(1–2), 9–15.
+
+Train-and-Hope Strategy
+Geralyn Timler
+Speech Pathology and Audiology, Miami University, Oxford, OH, USA
+
+Definition
+Intervention goals for individuals with autism spectrum disorders (ASD) often include increasing the use of one or more target skills within the treatment sessions. Moreover, comprehensive interventions include strategies to support the generalization of the new target skills. Generalization is the ability to use newly acquired skills with other materials, people (e.g., family members, teachers, peers), and settings (e.g., home, work, school, recess), that differ from the treat-ment sessions. The phrase “train and hope” was identified in Stokes and Baer’s (1977) classic review of treatment generalization strategies. “Train and hope” refers to teaching individuals a desired skill within a treatment session and hoping that the individual will generalize the use of that skill without implementation of a predetermined plan or strategy to facilitate generalization. Instead of merely “hoping” for generalization, Stokes and Baer (1977) recommended implemen-tation of explicit and systematic strategies to pro-mote generalization. These strategies include using sequential modification, training sufficient exemplars, programming common stimuli, using natural maintaining contingencies, training loosely, using indiscriminable contingencies, mediating generalization by teaching individuals to monitor their own performance, and training to generalize. Please see generalization and maintenance for an explanation of these generalization strategies.
+
+See Also
+▶Generalization and Maintenance
+
+References and Reading
+Kashinath, S., Woods, J., & Goldstein, H. (2006). Enhancing generalized teaching strategy use in daily routines by parents of children with autism. Journal of Speech, Language & Hearing Research, 49(3), 466–485. https://doi.org/10.1044/1092-4388(2006/036.
+Stokes, T., & Baer, D. (1977). An implicit technology of generalization. Journal of Applied Behavior Analysis, 10, 349–367.
+Strain, P., Schwartz, I., & Bovey, E. (2007). Social skills intervention for young children with autism: Programmatic research findings and implementation issues. In W. H. Brown, S. L. Odom, & S. R. McConnell (Eds.), Social competence in young children (pp. 253–272). Baltimore: Paul H. Brooks Publishing.
+Whalen, C. (Ed.). (2009). Real life, real progress for children with autism spectrum disorders: Strategies for successful generalization in natural environments. Baltimore: Paul H. Brooks Publishing.
+
+Trainer Implementation
+▶Treatment Fidelity
+
+Training Parents of Youth with Autism to Advocate for Adult Disability Services
+Carol Rabideau1 and Meghan M. Burke2
+1Vanderbilt Kennedy Center, Nashville, TN, USA
+2Department of Special Education, University of Illinois at Urbana-Champaign, Champaign, IL, USA
+
+Definition
+Parents of individuals with autism spectrum disorder (ASD) often advocate throughout their lifespans on behalf of their offspring. An advocate is “someone who acts on someone’s behalf or for their best interest” (Alper et al. 1995). With respect to individuals with ASD, parent advocacy can take on many forms ranging from advocating to access and obtain individual services to advocating for systemic changes to a service delivery system. With respect to adult services for individ-uals with ASD, parents’ knowledge about the adult service delivery system, feelings of empow-erment, and skills to identify, access, and receive needed services can result in effective parent advocacy.
+
+Historical Background
+Upon aging out of special education services, individuals with ASD shift from an entitlement driven system (i.e., special educations services) to an eligibility driven system (i.e., adult services). Although adult services exist, after exiting special education, many adults with ASD lack needed services (Shattuck et al. 2011). Without services, individuals with developmental disabilities, including ASD, may make little progress (Taylor and Hodapp 2012). Indeed, youth with ASD may go for years without needed services and rely on family to provide support.
+
+Barriers to Receiving Adult Services for Youth with ASD
+Although there are many barriers to accessing adult services, for brevity, only three barriers will be listed here. First, the adult service delivery system is fragmented. Comprised of a variety of agencies, services, and funding sources, the adult service delivery system stretches across multiple domains including but not limited to employment, health, leisure and recreation, housing or commu-nity living, financial assistance, and education. Different policies dictate regulations for certain adult services. Health services, for example, are (in part) outlined by the Patient Protection and Affordable Care Act while community living and housing services are (in part) regulated by Section 8 of the Housing Act of 1937. Further, in addition to having a variety of policies relate to different services, some policies overlap. For example, agencies that receive any federal funding must comply with Section 504 of the American Rehabilitation Act, which provides accommodations and services to individuals with disabilities, including ASD. Such agencies could include post-secondary education institutions, places of employment, and leisure and recrea-tional facilities as long as they receive some federal funding. Due to the fragmented nature of the service delivery system including the different policies and regulations, parents struggle to access and obtain services for their offspring with ASD.
+
+Second, in addition to being fragmented, adult services are also complicated because, in some instances, certain policies and services impact one’s eligibility and reception of other services. For example, in a given state, the Division of Vocational Rehabilitative Services generally helps individuals with disabilities, including ASD, find, secure, and maintain employment. Unfortunately, many states have waiting lists for Vocational Rehabilitative Services. Often, in these waiting lists, individuals with disabilities are priori-tized by their level of needs. In some states, the level of need is, in part, determined by whether the individual is receiving supplemental security income (SSI). If the individual is receiving SSI, then the individual has a higher priority on the waiting list. Parents would have to learn how to navigate the SSI regulations to access Vocational Rehabilitative services. Thus, the onus is often put on parents to understand the rules of different service delivery systems as well as how the systems relate to one another. Third and finally, adult services are difficult to navigate and receive because they are often eligi-bility driven. Parents of youth with ASD may have grown accustomed to their offspring being entitled to receive special education services, which are federally required regardless of the cost to the school. Adult services, however, are eligibility driven, meaning youth with ASD must qualify to receive services. For example, youth with ASD may be eligible to receive a Home and Community Based Services (HCBS) Medicaid Waiver – this waiver reimburses states for providing supports including employment, transportation, and therapies. One issue with the HCBS Medicaid Waiver is that 43 out of 50 states have waiting lists for this waiver (Research and Training Center on Community Living 2013). Depending on the state, it could take years before a youth with ASD is eligible to receive a Medicaid Waiver. Further, eligibility for an HCBS waiver varies by state. In some states, an individual must have an intellectual disability to be eligible for the waiver, whereas other states require individuals to have a developmental disability (i.e., eligibility is not limited to an intellectual disability) to be eligible for the waiver. Thus, depending on the state, individuals with ASD may not even be eligible for the waiver. This issue of varying eligibility can lead to inter- and intrastate variation. Specifically, indi-viduals with ASD may be “locked in” to residing in states where they are eligible for Medicaid waiver services feeling they are unable to move to a state wherein they would not be eligible.
+
+Need for Parent Advocacy
+Given that the onus is on parents to identify, access, and receive adult services for their off-spring with ASD, parents need advocacy skills. When parents are knowledgeable and advocate for their children, parents report that transition planning and the subsequent shift to adult services is more seamless (Timmons et al. 2004). Yet, many parents of individuals with developmental disabilities, including ASD, report struggling to advocate and to understand the adult service delivery system (Hetherington et al. 2010). As such, there is a strong need for parent advocacy training interventions about adult services. Although the research about parent advocacy is scant, the limited extant research indicates that parent advocacy interventions lead to increases in knowledge and perceived advocacy skills to navigate service delivery systems (Burke et al. 2016).
+
+Current Knowledge
+Because the systems of services and supports for individuals with ASD who are moving from high school to adult life are very complex, it is impor-tant to understand what choices are available and how enrollment in some services may affect access to other services. To help in the decision-making process for parents of young adults with ASD in Tennessee, a comprehensive 12-week parent advocacy curriculum, entitled the Volunteer Advocacy Program – Transition (VAP-T), is used to train parents. Funding for this work was received from the National Institute of Mental Health. The training provides information on var-ious services and post-secondary, advocacy tips for parents of young adults with ASD, as well as a Letter of Intent form. The Letter of Intent is not a legal document; it is a record of information about the person with a disability. This includes family information and a record of the other topics found in the VAP-T curriculum. Included in this document would be: the services and programs that have been applied for in the past, the outcome of those applications, and the programs and services that are currently in place. The information found in a Letter of Intent is important for anyone supporting a family mem-ber throughout his or her life-course. The docu-ment helps families plan and assess life-goals from year to year. Parents in the VAP-T are encouraged to complete a couple of pages of the Letter of Intent each week that coordinates with the topic covered. Notably, the Letter of Intent should be updated as changes occur and, other-wise, at least annually. The 12 sessions in the VAP-T curriculum are 2.5 h each in consecutive weeks and are held in the evening to accommodate parents who often have active careers in this stage of family life. Because the sessions are offered during the dinner hour, a light meal or snack is provided. Each week’s agenda includes PowerPoint presentations by subject matter experts, activities for partici-pants, time for discussion among participants across three sites, and resources specific to the topic covered and the physical location of partic-ipants. Participants and facilitators in all locations received a notebook containing all materials, activity sheets, and resources.
+
+The group facilitator and subject matter experts are present with a group at the host site. In collaboration with distance sites, the training is provided using video conferencing, so that two other groups of parents can join remotely. Addi-tionally, if participants cannot attend at the host or remote sites then, on occasion, they can join from a home computer. All sessions are video recorded for later viewing if a participant misses a session. Support to the remote sites includes a meeting before the start of the training to go over the

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+change. Charting progress on targeted behaviors is also possible through curriculum-based measurements if team members are examining change in the context of the classroom as compared to the performance expected of other students in the classroom. Parents can have a critical role in the measurement of outcomes, particularly if the desired behaviors are primarily observed in the home, although documenting generalization of behaviors across home and school settings is ideal.
+
+### Qualifications of Treatment Providers
+
+There is no specific training required to implement video modeling other than being familiar with the equipment and software used to create and edit the video models or video self-models. Models require the basic skills to act out a pre-determined script that is targeting the desired behavior. Parents, teachers, speech-language pathologists, and other related service providers may be required to take on primary responsibility for planning and editing the videos or particular aspects of the video depending on the targeted behaviors and the requirements for language, engagement, physical positioning, behavior, etc.
+
+### See Also
+
+*   Modeling
+*   Self-Recognition
+*   Social Skill Interventions
+*   Video Instruction
+
+## Video Prompting
+Kyle D. Bennett and Mashal Salman Aljehany
+Department of Teaching and Learning, Florida International University, Miami, FL, USA
+University of Jeddah, Jeddah, Makkah, Kingdom of Saudi Arabia
+
+### Definition
+
+Video prompting (VP) is one of the several video-based instruction strategies, and it involves a student observing an actor performing individual task steps on a video before imitating those task steps. The process is comprised of the following sequence: The student observes a single step, and then the student performs that step. The student repeats this process until all task steps are completed. Additional prompts and performance feedback are required for some individuals, and these should be faded as soon as possible to promote the independent demonstration of the skills or the independent use of the VP system.
+
+### Historical Background
+
+Video-based instruction (VBI) is based, in part, on Albert Bandura’s Social Learning Theory (Bellini and Akullian 2007). In that seminal work, Bandura (1977) posited that in addition to learning behaviors through direct experience, humans could also acquire behaviors by observing others emit behavior. Bandura reasoned that the ability to imitate others makes for efficient learning that results in the large behavior repertoires that humans develop. Others have supported the idea that imitation is a critical feature of learning that is essential for developing a variety of important skills (Cooper et al. 2020). VBI is also based on other behavioral procedures such as task analysis, behavior chaining, and performance feedback (Banda et al. 2011). VBI has been implemented for decades with individuals with developmental disabilities (DD), including people who experience autism spectrum disorder (ASD; Banda et al. 2011; Bellini and Akullian 2007). VBI incorporates two main strategies that include video modeling (VM) and VP; there are additional variations based on these overall VBI techniques. The main difference between VM and VP is related to the video presentation. When using VM, an interventionist plays the video of an actor performing an entire task before instructing the student to imitate what they observed on the recording. When using VP, the interventionist plays a video of one step of a task being completed by an actor prior to instructing the student to model what they viewed, and this continues until all steps have been presented using the video recordings, as well as all steps having been completed or attempted by the student (Aljehany and Bennett 2019).
+
+VP is a VBI strategy that is used with behavior chains (i.e., behaviors comprised of multiple steps, such as making a sandwich). Earlier research suggested that people performed better when completing a behavior chain while viewing smaller portions of video compared to viewing longer video sections (Margolius & Sheffield, as cited in LeGrice 1989). LeGrice (19989), applying the aforementioned research finding, was among the first researchers to explore the effects of VP on the skill acquisition of chained behaviors among individuals with DD. In that study, LeGrice (1989) successfully used VP as part of a time delay strategy to teach individuals with intellectual disability (ID) to use a video recorder and a personal computer. Participants were given an opportunity to perform the target behaviors. If the participants did not respond, or if they responded in error, a video prompt was delivered up to three times. The researcher showed the participants a video clip of an actor completing the corresponding task step and then asked the participants to imitate what they observed. In this sense, VP was used as a consequence strategy. Soon after the LeGrice (1989) study, Tiong et al. (1992) examined the effects of VP when teaching individuals with ID fire safety skills (e.g., exiting a building). Similar to the LeGrice (1989) study, Tiong et al. (1992) used VP as part of a time delay strategy whereby participants were given an opportunity to respond. However, contingent upon no response or an error, the researchers showed participants a video prompt of the correct behavior, and this video was shown up to three times. Similar to the previously discussed study, VP was used as a consequence strategy. Results indicated that participants acquired the correct responses, there was evidence of generalization, and the responses maintained following the removal of the intervention.
+
+In a later study, Norman et al. (2001) investigated VM combined with VP to teach children with DD three self-help skills including cleaning sunglasses, putting on a watch, and using a zipper to close a jacket. Norman et al. (2001) started sessions by showing a complete video model of the entire task. This video model was followed by a zero-second time delay whereby the researchers then showed participants a video prompt of the first task step to be completed. The time delay for showing participants the video prompt eventually increased to 5 sec. Results indicated that VM combined with VP was successful in teaching the students the targeted skills. In addition to these positive results, there is an important distinction between the study by Norman et al. and those by LeGrice (1989) and Tiong et al. (1992): Norman and colleagues used the VM and the VP sequence, at least initially, as an antecedent strategy rather than a consequence strategy. Although VP eventually became a consequence strategy in the Norman et al. study, this variation is notable in that the researchers implemented VP as a system to match errorless learning parameters. Graves et al. (2005) also used VM combined with VP to teach three secondary students with ID to complete cooking tasks. Similar to Norman et al. (2001), Graves et al. (2005) initially used VM and VP as antecedent strategies to reduce the likelihood of student errors while learning the targeted tasks. VP eventually became a consequence strategy as the study progressed, and the results indicated that the participants acquired the cooking skills with anecdotal reports of generalization and maintenance.
+
+An additional, albeit subtle, variation of this research line was conducted by Sigafoos et al. (2005). In this study, the researchers examined VP (without the use of VM) on the acquisition and maintenance of adults with DD making a snack. Intervention sessions consisted of researchers playing a video prompt before requesting participants to complete a given step. Sigafoos et al. continued to use VP as an antecedent intervention throughout the study, and this represents an approach to VP that potentially reduces errors committed by participants. Results suggested that two of the three participants acquired and maintained the skill; however, VP was not helpful for the remaining participant. In a follow-up study, Sigafoos et al. (2007) examined the effects of VP as an antecedent strategy to improve a daily living skill (DLS) – in this case, dishwashing – among adults with DD. The VP procedure was implemented identically to the Sigafoos et al. (2005) study. Results suggested that participants acquired the skills; however, unlike previous research, they did not maintain the skill when VP was removed. Attempts to fade VP by progressively combining steps until the procedure resembled VM were successful with two of the three participants. The remaining participant’s responding was variable. The results related to maintenance led to Sigafoos et al. (2007) questioning whether VP might lead to a level of prompt dependency for some participants. That is, some participants would continue to require video prompts to complete tasks. For some researchers, practitioners, caregivers, and students, this dependency might be of concern since the use of devices to prompt behavior might not be suitable in all settings (Sigafoos et al. 2007).
+
+The studies described represent a sampling of the development of the VP strategy; the presentation of these studies does not imply a specific timeline nor does it represent an exhaustive sample of the work during the timeframe of the reviewed studies. The critical point is that the tactic emerged as a means of helping individuals learn tasks comprised of multiple steps given that early research demonstrated that shorter video clips, focused on fewer behaviors, were effective when teaching people skills (LeGrice 1989). The application of VP with individuals with DD proved successful, and researchers applied variations (e.g., as an antecedent strategy and as a consequence strategy) with success. The development of VP continued during the ensuing years, and there is ample research available today to suggest that the practice is evidence-based under certain circumstances.
+
+### Current Knowledge
+
+Over the last several decades, researchers have explored the effects of VP on the acquisition, maintenance, and generalization of skills among individuals with DD, including those people experiencing ASD with or without ID. Many of these studies have been organized into systematic reviews of the literature and meta-analyses, and our understanding of the effects of VP has improved as a result of this work. The effectiveness of VP on the acquisition of chained behaviors has been well-documented. One of the first research teams to synthesize the research on VP was conducted by Banda et al. (2011). That research demonstrated that VP was effective for teaching a variety of chained behaviors to individuals with ASD with or without ID. In their review of the literature, Gardner and Wolfe (2013) reported that VP was effective when teaching chained behaviors to individuals with ASD. A subsequent review by Domire and Wolfe (2014) reported similar findings. In a recent meta-analysis, Aljehany and Bennett (2019) reported that VP was an effective instructional strategy for teaching DLS; their results showed the effect size of VP was in the high-moderate range. Among these review studies, the chained behaviors frequently explored were DLSs (e.g., cooking, setting tables, laundry skills, tying shoes, and washing dishes), community skills (e.g., using debit cards), vocational skills (e.g., cleaning, making photocopies), and safety skills (e.g., exiting a building).
+
+Maintenance and generalization of skills were noted among some of the studies in the aforementioned systematic reviews. Indeed, Banda et al. (2011) and Domire and Wolf (2014) reported that some researchers demonstrated positive results related to maintenance and generalization of skills. However, issues with maintenance were also noted in some studies. Overall, these features of learning – that is, maintenance and generalization – seem to be understudied, and additional work is needed.
+
+In addition to the overall effects of VP, researchers have explored parameters of VP, including the perspective of the videos, the actors serving as models in the videos, differing screen sizes, and the use of other procedures as part of a VP intervention package, to name a few (Bennett et al. 2017). The perspective from which the video is recorded, and subsequently played for those receiving VP, can be from (a) a first-person, or point-of-view, perspective; (b) a third-person, or spectator, perspective; or (c) a combination of both of these. A first-person perspective shows learners a video of an actor performing a task; however, only the actor’s arms and hands are in view. A third-person perspective shows students a video of the actor completing tasks from a distance such that most or all of the actor’s body is in view while completing the task. Researchers have used all three perspectives across VBI studies with success. When it comes to VP, in particular, researchers have reported that the strategy has been effective using either perspective, as well as the combined perspective (Domire and Wolfe 2014). Moreover, Bennett et al. (2017) reported that there were no differences in two comparison studies whereby researchers examined differences related to the video perspectives. Notwithstanding these findings, a first-person perspective has the advantage of reducing additional stimuli that might be less relevant to task completion, and this feature could benefit individuals with ASD who are known to have issues with over-selectivity and attending to tasks (Gardner and Wolfe 2013).
+
+Another parameter of VBI that received attention in the literature is related to the actors performing tasks in the videos. Bandura (1977) suggested that imitation of others’ behaviors could be affected by the degree to which the actor, or model, is similar to the individual learning skills. In the overall VBI literature, there are numerous examples of various models used as actors in the videos, including self as the model, children as the models, and adolescent and adults as the models. In the Bennett et al. (2017) review on parameters of VBI, comparison of models was examined by researchers with inconclusive results. In some cases, self as the model (when compared to peer or adult models) was more effective for a limited number of participants. In other instances, self as the model, adult as the model, and peer as the model were equally effective (Bennett et al. 2017). In demonstration studies where the model type was not compared, there have been successful reports of VM and VP with children modeling adult actors, adolescents modeling adult actors, and adults modeling adult actors (Gardner and Wolfe 2013). Thus, specific guidance is difficult to offer at the present moment, and additional research seems to be warranted when examining the effects of model type when implementing VM or VP (Bennett et al. 2017).
+
+In recent years, and as a result of improvements in mobile technology, researchers have investigated the effects of different screen sizes when using VBI. Among the advances in technology are the screen sizes of video playback devices. Devices used in past VBI research included televisions, desktop computer monitors, laptop computers, tablet computers, and smartphones, to name a few. To date, there are inconclusive results related to this parameter of VBI (Bennett et al. 2017). Some researchers found little differences in student performance when comparing large to small screen sizes using VM (Miltenberger and Charlop 2015), while other researchers found larger screen sizes to be more effective for participants when using VM (Mechling and Ayres 2012). Recently, Bennett et al. (2016) found that results between two different screen sizes were negligible for two participants, with the remaining participant performing better with a larger screen size when using VP. Considering the corpus of work examining screen sizes with VBI, Bennett et al. (2016) posited that differences in student performance could be unique to the characteristics of the students or the tasks they are learning to complete.
+
+Other intervention components have been included with VP (Aljehany and Bennett 2019; Banda et al. 2011). Some of these additional strategies include the use of voice-over instructions that accompany each video step, the use of response prompts and fading systems (e.g., least-to-most prompting, graduated guidance), and error correction (Aljehany and Bennett 2019). Voice-over instruction provides students with an auditory cue as part of the video, and the inclusion of this extra cue can help some individuals perform the demonstrated behavior (Bennett et al. 2017). However, as pointed out by Bennett et al. (2017), a few individuals seemed to perform better without the use of voice-over narration. Thus, caregivers and practitioners should record the video with narration and then test if the narration is useful to the student. Adjusting this parameter, if initially recorded, is as simple as lowering the volume (Bennett et al. 2017).
+
+In addition to voice-over narration, researchers have examined the effects of additional response prompting and fading systems to help learners follow video prompts. In a recent meta-analysis of VP, Aljehany and Bennett (2019) reported that the use of response prompting and fading systems, as well as error correction, was needed for some participants with ASD and ID. If these extra strategies are needed, it is critical that implementers fade these extra prompts so that the videos evoke student behavior absent of additional assistance by others. Ideally, the students learn to model the video demonstrations on their own so that they can prompt their behavior, especially as they approach new tasks (Smith et al. 2015).
+
+The promise of any VBI system, including VP, is that individuals with ASD and similar developmental disabilities learn a system that can prompt their behaviors independent of caregivers or professionals. The improvements in mobile technology (e.g., tablets, smartphones, and smartwatches) offer the potential for greater independence as students learn to become VBI users (Smith et al. 2016). To date, however, there have been a limited number of studies whereby researchers examined the effects of VBI as a self-instruction system. In the existing research on self-instruction, implementers completed some tasks for the students while setting up the self-instruction sessions, and this limits the results of the extant data (Smith et al. 2015, 2016). Smith et al. (2016) completed one of the few studies whereby researchers demonstrated that individuals with ASD and ID could learn to self-instruct using VM on a smartphone. The results of this study are promising, but additional work is needed in this area.
+
+### Future Directions
+
+Although the use of VP has been well-studied over recent decades, there are still areas that need to be addressed. First, many studies on the use of VP have focused on teaching DLSs. Additional work is needed to learn if VP is useful in developing other skills among individuals with ASD, including academic skills and social skills. Second, Aljehany and Bennett (2019) reported that VP was most frequently studied with adolescent and adult participants. There were too few studies and participants where researchers examined VP with elementary-aged students. Moreover, there were even fewer studies and participants that included preschool-aged children. Given these findings, additional research is needed to inform caregivers and practitioners the extent to which VP is effective with younger populations. Third, as illustrated by Banda et al. (2011), Domire and Wolfe (2014), and Smith et al. (2016), additional studies are needed to assess the generalization potential of VBI strategies, including VP. This issue is more pronounced considering that VBI strategies, paired with mobile technology, have the potential to serve as a self-instruction methodology or a self-remediation technique (Smith et al. 2016). Fourth, additional work is needed to explore methods to fade VP so that individuals perform tasks in complete independence. As Sigafoos et al. (2007) argued, there could be instances in which it is not feasible or appropriate for individuals with disabilities to use VP. For instance, some tasks, or task steps, require careful attention (e.g., construction), and the use of mobile technology as a self-instruction or self-prompting device could compromise the safety of the person performing the task or others nearby. Finally, as technology continues to advance, becomes more portable, and begins to simulate reality (e.g., portable devices used in virtual reality simulations), the integration of VP tactics with this technology becomes possible. However, research into how such technology affects individuals with ASD, the practicality of using such technology in home and community settings, and the feasibility of integrating the instructional strategies with advance technology is needed (Pham et al. 2019).
+
+Although additional work is required to refine some of the parameters surrounding the use of VP, as well as research on integrating VP with more advanced technological developments, the practice is well-suited for helping people with ASD. Years of research have demonstrated that the strategy is useful for developing skills among this population of learners. Caregivers and teachers should consider using this strategy when circumstances permit.
+
+### See Also
+
+*   Video Modeling/Video Self-Modeling
+
+## Video Tape Analysis Studies
+Gail Fox Adams
+Department of Applied Linguistics, University of California, Los Angeles, CA, USA
+
+### Definition
+
+This entry on videotape analysis studies considers how researchers use video to study autism, especially in terms of diagnosis, skill assessment, behavioral expression, and lived experience. The studies primarily represent research from the United States.
+
+### Historical Background
+
+Live observation of human behaviors and interactions is a basic investigative tool used in the humanities and social sciences

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+psychometric properties were improved by updating the norms, by adding additional evidence of reliability and validity, by extending the floor and ceiling, and by reexamining bias. Finally, user-friendliness was increased by reducing the testing time, reorganizing the manual and the record form, and simplifying administration procedures. The WPPSI-III addresses the developmental period that involves some of the most profound changes in children’s cognitive abilities.
+
+Psychometric Data
+
+Development and Standardization Procedures
+
+The WPPSI-III development was a 4-year iterative process where each phase of development leads to further refinements of the scale. The AERA 1999 Standards for Educational and Psychological Testing served as primary resources. Test development occurred in five general stages: conceptual development, pilot, national tryout, standardization, and final assembly and evaluation.
+
+Conceptual Development. An advisory panel with nationally recognized experts in school psychology, clinical psychology, neuropsychology, and cognitive development was assembled to work with the team throughout the process. Early in the development, a group of 106 professionals participated in a focus group to refine goals and assist in the formulation of the initial scales. Further, two telephone surveys were conducted with users of the WPPSI-R and professionals in childhood assessment. Finally, semi-structured surveys of other experts and examiners were conducted at all stages of test development to rate the research versions of the scale on such qualities as developmental appropriateness, user-friendliness, and clinical utility. In all, results were summarized and discussed, and at all stages of development, modifications to the working blueprint and research versions of the scale were integrated.
+
+Pilot Stage. The goal of the pilot stage was to produce a version of the scale for use in the next stage (the national tryout stage). The focus was to examine any content issues, relevance of items, adequacy of floors and ceilings, the clarity of instruction, identification of responses processes, administration, scoring, and item bias.
+
+National Tryout Stage. The version used included 17 subtests, and data was obtained from a stratified sample of 424 children representatives of key demographic variables in the national population. Stratification occurred along age, sex, race, parent education level, and geographic region. Further, special group data were collected (e.g., groups of participants with mental retardation, intellectual giftedness, and language disorder as well as oversample of African American and Hispanic children) to provide additional understanding of the adequacy of the subtests and the clinical utility of the scale.
+
+Standardization Sample. After collecting data from the national tryout stage, a standardization edition of the WPPSI-II was created. All preceding research questions were reexamined using similar research methods, but at this stage, the focus was on the derivation of norms and the provision of reliability, validity, and clinical utility. The WPPSI-III was standardized on a sample of 1700 children aged 2:6–7:3 who were chosen to match closely with the 2000 US census data on the variables of gender, geographic region (Northeast, South, Midwest, and West), parent education (0–8 years, 9–11 years, 12 years which was high school degree or equivalent, 13–15 years which was some college or associate’s degree, and 16 or more), and ethnicity (Whites, African Americans, Hispanics, Asians, and other). The sample was divided into nine age-groups of 200 each (2:6–2:11, 3:0–3:5, 3:6–3:11, 4:0–4:5, 4:6–4:11, 5:0–5:5, 5:6–5:11, 6:0–6:11) and 7:0–7:3 which included 100 participants. The group was evenly split in gender. Trained recruiters and independent examiners identified the children meeting the specified inclusion criteria for the standardization sample. Both the child and parents were paid an incentiv_e_ fee for participation. Subjects were excluded if they were tested on any intelligence measure in the previous 6 months, had uncorrected visual and/or hearing loss, were not English speaking, had an upper extremity disability that would affect motor performance, were admitted to a hospital or psychiatric facility, were taking medications that might depress test performance, or had a previously diagnosed physical condition or illness that might depress test performance.
+
+Final Assembly. The consistency of the item sets, instructions, and stimulus material was evaluated throughout the development process and retained, modified, or deleted. Further, a special study was conducted to examine the possible effects of a new testing order using a within-subjects design. Sixty children aged 4:0–7:3 were administered 14 subtests in the proposed order for the final scale and matched to 60 children from the standardization sample, and no significant differences were found. To establish a quality assurance procedure, qualified examiners were found, a computer program automatically checked the values entered by scorers, and scoring studies were conducted to determine the underlying response process.
+
+Statistical Properties
+
+Reliability. Reliability refers to the accuracy, consistency, and stability of test scores across situations. True scores are hypothetical and defined as what an examinee would score if the test were perfectly reliable. The difference between a true score and an examinee’s obtained score is measurement error. A reliable test would thus have very small measurement error as it would be consistent within one administration and on different occasions.
+
+Internal Consistency. The evidence for internal consistency was obtained using the normative sample and the split-half method which was used in the WPPSI-R and divides subtest items with an odd-even split to form two half-tests. The reliability coefficient of the subtest is the correlation between the total scores of the two half-tests corrected by the Spearman-Brown formula for the full subtest. Coding and Symbol Search are timed subtests, and thus test-retest stability coefficients were used as reliability estimates. For the overall standardization sample, the average reliability coefficients range from .84 to .95 which are excellent ( .90) to good. The average internal consistency coefficients for Verbal IQ = .95, Performance IQ = .93, Processing Speed Quotient = .89, General Language Composite = .93, and Full Scale IQ = .96.
+
+Test-Retest Stability. The test-retest stability for the WPPSI-III was obtained on a sample of 157 children (13–27 children from each of the nine age-groups). The WPPSI III was administered a second time to establish stabiity to a group of children at an interval of a mean of 26 days (range 14–50 days). The sample was stratified (39.5% female, 66.2% White, 12.1% African American, 17.2% Hispanic, and 4.5% other). Average test-retest coefficients for the following were: Verbal IQ = .91, Performance IQ = .86, Processing Speed Quotient = .86, General Language Composite = .91, and Full Scale IQ = .92. The largest practice effects (scores increasing from first testing to second testing) across age bands were 5–6 points for Performance IQ and Processing Speech Quotient, while the average for all ages across the other domains were just under three points.
+
+Validity. There are three major types of validity: content (does the assessment adequately sample relevant aspects of the construct being measured), criterion-related (scores are related to specified external criteria), and construct (when the construct measured by the test was actually measured). The WPPSI-III research team established validity by utilizing evidence from the test content, the response process, the internal structure, and the relationship to other variables. First, using intercorrelation studies, all intersubtest correlations on the WPPSI-III were statistically significant. For both age bands, the presence of moderate to high correlations supports the premise that a general intelligence factor is present. Second, factor analytic studies were used to evaluate the internal construct structure of the WPPSI-III. For both age bands, results were consistent with the predicted factor model proposed. In short, the WPPSI-III is a two-factor test, Verbal and Performance. For ages 4:0–7:3, a third factor includes Processing Speed. When only the core subtests were analyzed, the WPPSI-III subtests each loaded on its predicted factor, with the exception of Picture Concepts at ages 6:0–7:3. It loaded onto a Verbal subtest rather than on its intended factor (Performance). Finally, the relationship to other measures by locating constructions within a nomological network of known variables was conducted. The WPPSI-III was compared to the WPPSI-R, WISC-III, Bayley Scales of Infant Development-II, and Differential Abilities Scale. The global scales of these instruments correlated strongly with the WPPSI-III (.80–.89). The WPPSI-III Verbal Scale correlated substantially higher with the verbal scales of the WPPSI-R, WISC-III, and DAS than with the nonverbal scales of these instruments. The WPPSI IV and Wechsler Individual Achievement Tests II were both administered to 208 children to examine the relationship between the cognitive measure and academic achievment. The WIAT-II composites were variably correlated with the domain IQ scores of the WPPSI-III ranging from .31 (Processing Speed Index and WIAT-II Reading) to .78 (Full Scale IQ and Total Achievement WIAT-II).
+
+Clinical Uses
+
+Special Subgroups
+
+Because the WPPSI-III is often used as part of a diagnostic assessment, its clinical utility and specificity was examined through a number of special group studies conducted concurrently with the scale’s standardization. These samples were not randomly selected; instead, they were from a variety of educational and clinical settings. The sample sizes were small, and only group performance is reported. The following describes the special interest groups and their performance in the WPPSI-III:
+*   Intellectually gifted – this group obtained significantly higher Verbal, Performance, and Full Scale IQ than children in the normal population.
+*   Children with mild or moderate mental retardation. For the mild severity subgroup, composite score for the group for Full Scale IQ was 62.1. For the moderate severity subgroup, average Full Scale IQ for the group was 53.1.
+*   Children with developmental delays. Children in this group performed significantly worse than a matched control group with average Full Scale IQ score at 81.8.
+*   Children with developmental risk factors. On all domains, IQ ranged from 85.7 to 88.6 with the exception of Processing Speed where the average IQ for the group was 90.1.
+*   Children with autistic disorder. Consistent with research, the Performance IQ (mean 88.2) was significantly higher than the Verbal IQ (mean 70.6).
+*   Children with expressive language disorder. Both groups’ mean composite score differences were greatest for Verbal IQ (mean 90.6) and Full Scale IQ (mean 90.1).
+*   Children with mixed receptive-expressive language disorder. Average Verbal IQ was 83.1, Performance IQ was 85.2, Processing Speed IQ was 82.7, Full Scale IQ was 81.9, and General Language Composite was 86.7 representing significantly lower mean scores than a matched control group.
+*   Children with Limited English Proficiency. No differences were found for Processing Speed and Performance IQ, but Verbal IQ averaged 80.2 and General Language Composite averaged 79.2.
+*   Children with attention-deficit hyperactivity disorder. In the research and clinical literature, traditional IQ scores have not been found to be useful in discriminating children and adults with ADHD. Children with the disorder typically achieve scores near the normative range. In this subsample, all scores (subtests and domain scores) were between 93.8 (Verbal IQ) and 97.4 (Performance IQ).
+*   Children with motor impairment. As expected, children with motor impairments scored significantly lower than the normative population on the Performance IQ (average 87.7) and marginally significantly lower on the Processing Speed IQ (average 89.9).
+
+Results from the special group studies indicate that the WPPSI-III is valid and clinically useful (with the exception of the ADHD category, but this is not specific to the WPPSI-III but general to all intelligence tests).
+
+Interpretation
+*   WPPSI-III scores should never be interpreted in isolation.
+*   Standard scores enable practitioners to compare scores within the WPPSI-III and between the WPPSI-III and other related measures.
+*   Age-corrected standard scores also allow for a comparison of children’s cognitive functioning across other children of similar age.
+*   Other information (percentile ranks, descriptive classification, test-age equivalents) can also be used to benefit interpretation.
+
+Profile analyses can be identified from both intraindividual and interindividual perspectives by comparing the child’s score patterns across subtests or to the appropriate normative reference group. This can aid in identifying meaningful patterns of strengths and weaknesses.
+
+Wechsler Test of Adult Reading
+
+Description
+
+The Wechsler Test of Adult Reading (WTAR; Pearson 2001) was developed for estimating pre-morbid (pre-injury) intellectual functioning and pre-injury IQ and memory abilities. The WTAR takes less than 10 min to administer and is appropriate for individuals between the ages of 16–89 years of age. It is composed of 50 words that have atypical grapheme to phoneme translations. A grapheme is a letter or set of letters that correspond to a sound, or phoneme, such as the f in the word fan, the ph in the word phone, or the gh in the word tough. The reason for relying on words that have unusual or irregular pronunciations is to test prior word knowledge by minimizing the individual’s ability to apply standard pronunciation rules. Thus, the test attempts to rely on the individual’s prior, or pre-injury, learning or knowledge of the word list that is presented. The rationale for this approach is that reading recognition is relatively stable, even when there is evidence of cognitive decline due to aging, illness, or brain injury. The WTAR record form includes the WTAR stimulus words with correct pronunciations and item scoring with an area for recording demographic data, the individual’s raw score, standard score, percentile rank, and a space for predicting pre-morbid IQ and memory functioning from the current WTAR performance.
+
+An advantage of the WTAR is that it was co-normed with the United States (US) and United Kingdom (UK) versions of the Wechsler Adult Intelligence Scale®-Third Edition (WAIS®-III) and Wechsler Memory Scale®-Third Edition (WMS®-III). Estimating pre-morbid or pre-injury level of intellectual functioning may be useful in treatment planning. This co-norming allowed for the development of equations for predicting both the WAIS-III and WMS index scores from the WTAR. A large representative norming sample that was matched to both the US and UK populations was employed to provide demographic data that improves accurate prediction of pre-morbid IQ in neurological cases. There is extensive between group validity for individuals with Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, Korsakoff’s syndrome, and Traumatic Brain Injury. However, there is limited information about individuals with autism spectrum disorders. A key part of the rationale of the WTAR is the assumption of a normal development of reading skills prior to injury or cognitive decline. Individuals with ASD tend to perform well (and consistent with their level of cognitive ability) on mechanical reading and spelling (Minschew et al. 1994). Indeed, decoding or basic word identification, which is the skill that is assessed on the WTAR, is generally an area of strength amid poor reading comprehension (e.g., O’Connor and Hermelin 1994; Patti and Lupinetti 1993). Therefore, the WTAR may overestimate pre-morbid functioning. Just as assessment of single-word comprehension may overestimate general (sentence and paragraph) reading level (Newman et al. 2007), the WTAR may overestimate prior cognitive capacities. Moreover, given that a majority of individuals with ASD are routinely assessed throughout childhood and adolescence to determine their need for academic accommodations, assessment of pre-morbid functioning may be best accomplished through a review or earlier testing reports.
+
+Wechsler, David
+
+David Wechsler was born on January 12, 1896, the youngest of seven children, to Moses Wechsler, a merchant, and Leah (Pascal) Wechsler, a shopkeeper, in Lespedi, Romania. The family immigrated to the United States in 1902 when famine and poor economic conditions in Romania led to “worsening the scapegoating of Jews and resulting in severe applications of existing anti-Jewish decrees” (Wasserman 2012, pp. 30–31). He died at the age of 85 in New York City on May 2, 1981.
+
+His experiences just before, during, and after America’s entry into the World War I paved the way for him to become the world’s leading expert in intelligence testing and clinical assessment. In 1917, working with testing pioneer Robert S. Woodworth at Columbia University, Wechsler earned his M.A. in experimental psychopathology on retention in Korsakoff’s psychosis; that same year, he also worked under E. G. Boring, scoring army intelligence tests, as a civilian volunteer. After induction, while serving in the army’s psychology division in Fort Logan, Texas, Corporal Wechsler administered individual intelligence tests, including the Stanford-Binet, to recruits who could not be validly assessed by the army group tests (e.g., illiterates, suspected malingerers). In 1919, as an army student at the University of London, he worked closely with Karl Pearson (who developed the coefficient of correlation) and Charles Spearman (who promoted the theory of general intelligence or “g”). He then studied at the Sorbonne for 2 years, specializing in experimental and physiological psychology (Edwards 1994). By the time he earned his Ph.D. in 1925, under Woodworth at Columbia (on the measurement of emotional reactions via the galvanic skin response), his meteoric career was already on the rise. As Wasserman (2012) notes, “Columbia was one of the few major universities that provided graduate experimental psychology training with a willingness to address applied problems, termed experimental abnormal psychology by Woodworth” (p. 31) (and later called clinical psychology).
+
+“In many ways, David Wechsler was an unexpected success – coming to the United States as a child amid a flood of Eastern European immigrants, losing both parents by the age of 10, compiling a relatively ordinary academic record in high school and college (while graduating early),"... "and not having become a naturalized citizen prior to the war. Even so, these risk factors may have been somewhat ameliorated by the following: (1) the guidance of his accomplished older brother, pioneering neurologist Israel S. Wechsler, who became his caretaker and role model; (2) the opportunity to provide military service as an army mental test examiner, which allowed him to thereby quickly learn about assessment and make key professional contacts; and (3) receiving his graduate education and professional psychology training at an opportune time and place in the development of what eventually would become clinical psychology.” (Wasserman 2012, p. 30)
+
+In the mid-1920s, Wechsler was in the first wave of clinical psychologists, a breed of scientist-practitioners that represented a notable departure from the purely academic and experimental tradition of the American Psychological Association (APA). He was among the first to set up a private clinical practice and, in 1932, became chief psychologist at Bellevue Psychiatric Hospital, a post he held until 1967. Concurrently (1933–1967), he was a faculty member at New York University College of Medicine. Wechsler’s test development techniques (combining verbal and nonverbal skills to produce a truly global measure of intelligence, a notion first sparked by scoring the group-administered Army Alpha and Army Beta, verbal and nonverbal tests, respectively); (Yoakum and Yerkes 1920) and his clinical approach to the assessment of intelligence are among the greatest innovations in applied psychology during the twentieth century. “Probably the work of no other psychologists, including Freud or Pavlov, has so directly impinged upon the lives of so many people” (Matarazzo 1981, p. 1542).
+
+French pioneer Alfred Binet developed the first intelligence test in 1905 (the Binet-Simon) and Stanford psychologist Lewis Terman translated and adapted Binet’s work within the United States to produce the Stanford-Binet Intelligence Scale in 1916. However, it was American psychologist David Wechsler who dramatically, and permanently, changed the face of intelligence testing when he published the Wechsler-Bellevue (W-B), for ages 7–69 years, in 1939 (known in the literature as the W-B Form I because Form II was published in 1946). Wechsler’s intelligence (or “IQ”) tests departed from Binet’s and Terman’s approach to measuring mental ability. Binet and Terman used mostly verbal tasks to measure intelligence, whereas Wechsler added a performance (nonverbal) scale to a verbal scale, with both contributing equally to a person’s full-scale IQ. The Binet-Simon and Stanford-Binet offered a single score, global IQ, plus mental age (MA); the W-B offered verbal, performance, and full-scale IQs, along with a profile of scaled scores on 11 separate subtests (one of which, vocabulary, was an alternate and did not contribute to the IQs). The 1916 Stanford-Binet and its subsequent editions in 1937, 1960, and 1972 (coauthored by Maud Merrill) were routinely administered to children, adolescents, and adults; however, they were never standardized (normed) on adult populations. Consequently, when Wechsler published the W-B, standardized on children and adults (including older adults), he effectively developed the first real test of adult intelligence. And, in 1939, he replaced the ratio IQ that had become a staple in the Binet scales with the “deviation IQ.” Ratio IQs were based on an old formula that compared mental age to chronological age and represented a “rubber yardstick” because 1 year’s growth is quite different at age 5, 9, or 15; the outmoded formula also was not applicable to adults. By contrast, deviation IQs are standard scores with a mean of 100 and a standard deviation (SD) of 15 for all three IQs at all ages. They are derived from the concepts of the normal curve and standard deviations, which avoid problems associated with ratio IQs. Wasserman (2012) points out that “Wechsler deserves credit for popularizing the deviation IQ, although the Otis Self-Administering Tests and the Otis Group Intelligence Scale had already used similar deviation-based composite scores in the 1920s” (p. 35).
+
+Wechsler’s (1939) definition of intelligence has been widely quoted: Intelligence is the aggregate or global capacity of the individual to act purposefully, to think rationally and to deal effectively with his environment. It is global because it characterizes the individual’s behavior as a whole; it is an aggregate because it is composed of elements or abilities which, though not entirely independent, are qualitatively differentiable. By measuringement of these abilities, we ultimately evaluate intelligence. But intelligence is not identical with the mere sum of these abilities, however inclusive. In this definition, he shows the influence of Spearman’s g on his theory but also makes it clear that there is more to intelligence than g.
+
+But Wechsler’s lasting imprint was as a test developer, not as a theorist. And, ultimately, his greatest contribution may have been his clinical approach to the measurement of intelligence. When the Stanford-Binet reigned supreme prior to the publication of the W-B, the main approach to test interpretation was psychometric, with the focus on the precise IQ, its percentile rank, the band of error surrounding the IQ, and group differences in mean IQ (e.g., urban versus rural children); the main book on Stanford-Binet interpretation was written by McNemar (1942), a statistician. Wechsler changed all that. He believed that intelligence was part of personality and that personality variables affected how a person performed on an IQ test. He encouraged examiners to interpret the IQs and subtest scaled scores within the context of the clinical behaviors observed during the evaluation and the reasons for referral (emotional disturbance, dementia) and to interpret responses to specific items (e.g., verbal answers on social comprehension questions) in terms of their clinical content. Thanks in large part to his experiences as an examiner during wartime, his clinical acumen, and his responsibilities at Bellevue Hospital, which brought him into one-on-one contact with individual patients from diverse backgrounds with an array of diagnoses, Wechsler (in conjunction with innovators such as Rapaport et al. 1945) changed IQ measurement from psychometric testing to clinical assessment. That distinction still characterizes the training of clinical psychologists and neuropsychologists worldwide.
+
+An immediate benefit of the Wechsler approach to IQ testing was the large number of reliable and valid scores yielded by his tests – three IQs and an array of a dozen or so scaled scores on separate subtests. The full-scale IQ provides a global measure of intelligence (as was provided by the Stanford-Binet). However, the addition of separate verbal and performance IQs allowed examiners to determine whether a person was better able to express his or her intelligence via verbal comprehension and expression or nonverbally via the manipulation of concrete materials. And the subtest scaled scores – standard scores with a mean of 10 and SD of 3 for all subtests at all ages – permitted clinicians to examine a person’s profile of strengths and weaknesses on cognitive tasks such as information, similarities, block design, and picture arrangement. The separate IQs and the profile of scaled scores provided a breakthrough for researchers and clinicians who needed to go beyond a single, global IQ to better understand a person’s abilities and disabilities. The global IQ masked areas of strength and weakness. The W-B, and later the 1949 Wechsler Intelligence Scale for Children (WISC) and 1955 Wechsler Adult intelligence Scale (WAIS), spawned state-of-the art research with patients diagnosed with neurological impairment in the left versus right hemisphere (Reitan 1955; Meyer and Jones 1957), with children and adolescents referred for reading and learning disabilities (Bannatyne 1971), and with individuals diagnosed with autism (Murata et al. 1974; Wassing 1965). The comparison of verbal IQ with performance IQ (V-P discrepancy) was espe-cially crucial for understanding the strengths and weaknesses of individuals with left-hemisphere damage (P > V), right-hemisphere damage (V > P), and autism (P > V). The separate IQs also contributed mightily to understanding the differential effects of aging on intelligence (Feingold 1950; Jarvik et al. 1962) and to the theory of fluid and crystallized intelligence (Horn and Cattell 1966). The profile of scaled scores was found to be particularly useful for identifying children with reading disabilities and for understanding the language problems of individuals with autism or other language disorders (e.g., comparing their performance on verbal subtests that required much verbal expression, such as vocabulary, with their performance on tasks like information that required little verbal expression). For reviews of studies on Wechsler profiles for children and adults diagnosed with autism spectrum disorders, including Asperger’s disorder, consult Barnhill et al. (2000), Klin et al. (2000), and Lichtenberger and Greenberg (2009).
+
+The Stanford-Binet remained the most popular IQ test into the early 1960s, even after the WISC and WAIS were published. However, as the fields of learning disabilities, neuropsychological assessment, and autism began to grow exponentially during the 1960s and started to dominate clinical practice in schools and clinics, Wechsler’s scales surpassed the Stanford-Binet. By the time the WISC revision (WISC-R) was published in 1974, Wechsler’s scales for children and adults were by far number one in the United States, and his scales have remained number one, worldwide, into the second decade of the twenty-first century. The WISC-IV and WAIS-IV are dominant, and the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III) is a popular choice for assessing young children. Not shown in the figure is Wechsler’s widely used memory test for adolescents and adults, the Wechsler Memory Scale (WMS), now in its fourth edition (WMS-IV).
+
+“For his master’s thesis completed in 1917, Wechsler patched together a clinical memory battery from existing published and unpublished tests [,] . . . a pattern he was later to follow with intelligence tests” (Wasserman 2012, p. 31). But his approach to test construction does not diminish his amazing innovations, including his insistence (in a Binet-dominated world) that nonverbal assessment must accompany verbal assessment to yield a truly global measure of a person’s intelligence or his assertions that IQs and scaled-score profiles are only meaningful when interpreted within a rich clinical context, points that he introduced to the world with eloquence in his landmark articles and books (e.g., Wechsler 1928, 1930, 1935, 1939, 1944, 1950, 1958, 1971, 1975). And his genius did not diminish with age. When I worked with Wechsler on the revision of the WISC in the early 1970s, “he was in his mid 1970s and as active and involved in his tests as ever. He showed me notebooks filled with new items, including comic strips he had cut out from newspapers to adapt for nonverbal test items. With his own tool kit, he had constructed a variety of wooden dolls and formboards, always in search of new ways of measuring mental ability.” (Kaufman 2009b, p. 34)
+
+He even analyzed himself: “After completing his fellowship at the Sorbonne, Wechsler traveled through France, Switzerland, and Italy, before reluctantly returning to the United States.... His ambivalence about returning, as disclosed to Edwards (1974), was reflected in his 1922 paper on the psychopathology of indecision” (Wasserman 2012, p. 33). And his ideas were consistently decades, even generations, ahead of his time. Wechsler (1935) was an early advocate of measuring adaptive behavior skills, urging that daily behaviors, social demands, and functional living skills be considered alongside IQ test results before assigning a mental deficiency diagnosis. As J. D. Wasserman (personal communication, July 27, 2011) pointed out, Wechsler’s approach is “entirely congruent with even contemporary standards for diagnosing intellectual disability (Schalock and The AAIDD Ad Hoc Committee on Terminology and Classification 2010).” He also reflected current philosophies by urging caution and sensitivity about the consequences of applying labels such as mental deficiency and genius based solely on IQ, because “Too much is at stake” (Wechsler 1971, p. 54).
+
+In Wechsler’s lifetime, his scales were organized around the distinction between V and P IQs. Subsequent versions have emphasized four indexes, two of which replaced V IQ and P IQ (verbal comprehension index, or VCI, and perceptual reasoning index, or PRI), and two of which were new: working memory index (WMI) and processing speed index (PSI). L. G. Weiss (personal communication, July 26, 2011) noted that Wechsler’s “early notions on the importance of mental manipulation (Arithmetic/Digit Span) and mental speed (Digit Symbol/Coding) were borne out by modern research and subsequently fleshed out into WMI and PSI, which increased his test’s sensitivity to clinical disorders and extended his influence well into the twenty-first century.” In a 1976 interview, Wechsler credited Woodworth and E. L. Thorndike as contributing most to his intellectual development, and he also praised Augusta Bronner and William Healy for refining his clinical skills: “they were both wonderful clinicians and they were the first, as I recall, who had discussions of every individual case at which first the social worker would present her history, then the psychiatrist, then the psychologist, and they either praised the individual and so forth on the basis of these conferences” (Hargus and Wasserman 1993).
+
+Wellbutrin
+Bupropion
+
+Wernicke, Karl (1848–1905)
+
+*   Carl Wernicke (May 15, 1848–June 15, 1905)
+*   Doctor of Medicine (Psychiatry)
+*   University of Breslau
+
+Major Appointments (Institution, Location, Dates)
+
+*   Professor of Psychiatry and Nervous Diseases, University of Halle, Halle, Germany, 1904
+*   Associate Professor of Neurology and Psychiatry, University of Breslau, Wroclaw, Poland, 1885
+*   Scientist, Department of Psychiatry and Nervous Diseases, Berlin Charité Hospital, Berlin, Germany, 1878
+
+Major Honors and Awards
+
+*   Member Royal College of Psychiatry (1957)
+*   Fellow Royal College of Psychiatry (1972)
+*   Honorary Fellow of Psychiatry, University of Edinburgh
+
+Landmark Clinical, Scientific, and Professional Contributions
+
+Wernicke is best known for his theory of aphasia, publishing a manuscript entitled “The Aphasia Symptom Complex” in 1874 when he was 26 years old. This publication catapulted Wernicke as the leader in aphasia research. Several years later, Wernicke published a textbook on brain diseases. In this book, he described a condition called “pseudoencephalitic haemorrhagica superior,” later to be known as “Wernicke’s encephalopathy.” In addition to his work on aphasia, Wernicke pioneered a technique for drainage of cerebral spinal fluid in patients with hydrocephalus.
+
+Short Biography
+
+Karl Wernicke was born in Wroclaw, Poland, in 1848. He studied medicine at the University of Breslau. Upon completion of his medical degree, Wernicke completed training in psychiatry. Wernicke worked under and collaborated with a variety of neuroscientists and neuropsychiatrists prior to designing his theory of aphasia. Wernicke discovered that damage to the left posterior, superior temporal gyrus resulted in deficits in language comprehension. This region of the brain is commonly referred to as “Wernicke’s Area,” and the syndrome in which damage to this region of the brain occurs often results in what is now known as “Wernicke’s aphasia.” Following Wernicke’s publication of his aphasia manuscript, he was appointed to the Department of Psychiatry and Nervous Disease of the Berlin Charité Hospital in 1878. During his time at the hospital, there was a significant discord between Wernicke and the administration. This conflict forced Wernicke into private practice. Despite this conflict, Wernicke remained active in both clinical care and research activities. He continued to publish and pioneer new techniques in the field of neuroscience. In 1885, Wernicke became Professor of Psychiatry at Breslau Psychiatric Hospital at the university where he opened an outpatient clinic for individuals with neurological pathology. Unfortunately, there was tension between Wernicke and the hospital. Wernicke was ultimately forced to leave his position. In 1904, Wernicke was offered the Chair of Psychiatry and Nervous Diseases at University of Halle. Wernicke continued his work in neurological disease at the university until his untimely death due to a bicycle accident in 1905 at the age of 58. Although Wernicke died relatively early, his work continues to have significant influence in the fields of neurology and psychiatry.
+
+Wernicke’s Aphasia
+
+Synonyms
+Fluent aphasia; Receptive aphasia; Sensory aphasia
+
+Short Description or Definition
+
+Wernicke’s aphasia is a condition that is associated with damage to Brodmann area 22 in the left posterior superior temporal gyrus. The disorder is named for Karl Wernicke, the individual who is credited with first recognizing it. Wernicke’s aphasia is also known as fluent aphasia or receptive aphasia as deficits tend to be in the content of the spoken message rather than speech output. As such, individuals with Wernicke’s aphasia use sentences that are intact in terms of syntax, grammar, rate of speaking, and prosody but demonstrate difficulties with content such as vocabulary. Error patterns include word substitutions, additions, and difficulties with word order. In addition, patients demonstrate extreme difficulty in comprehending both what they themselves are saying and what is being said to them.
+
+Categorization
+
+Wernicke’s aphasia is considered a fluent aphasia within larger aphasia classification systems.
+
+Epidemiology
+
+Estimates of the prevalence of Wernicke’s aphasia in the larger population are largely unknown, though it has been estimated that 80,000 people develop aphasia in the United States each year.
+
+Clinical Expression and Pathophysiology
+
+Wernicke’s aphasia often occurs as the result of damage to left posterior superior temporal gyrus. The condition manifests itself as a fluent aphasia in which an individual demonstrates relatively fluent language output. However, this output is characterized by the use of extra words, “made-up” words, or words that are out of order. In addition, language comprehension skills are largely impaired in individuals with Wernicke’s aphasia.
+
+Treatment
+
+Treatment for Wernicke’s aphasia is often multi-faceted and is typically individualized based on the patient’s profile of strengths and needs. Individuals with aphasia often enroll in formal speech-language therapy to address functional communication in a variety of settings in which they are expected to communicate. Therapy goals are focused on maximizing the individual’s ability to communicate effectively with peers and family members, given residual strengths. For individuals with Wernicke’s aphasia, it is critical to support both comprehension and production; although speech/language output is fluent, patients often do not comprehend what they are saying or what is being said to them. Although some individuals recover completely, individuals with aphasia often experience lifelong deficits. In these cases, family member and patient support groups are often a critical piece of the therapeutic process as the patient and family learn to manage their new situation.
+
+West Syndrome
+Infantile Spasms/West Syndrome
+
+Whole-Genome Sequencing
+Next-Generation Sequencing
+
+Wide Range Assessment of Memory and Learning (WRAML)
+
+Synonyms
+WRAML-2; WRAML2
+
+Description
+
+The WRAML2 (Sheslow and Adams 2003) is a clinical assessment instrument designed to evaluate memory and learning in children and adults (ages 5–90 years). The test is administered individually with the examiner presenting items and recording responses. Three index scores are derived from six core subtests assessing verbal memory, visual memory, and attention-concentration. In addition, a general memory standard score can be calculated. Supplementary subtests provide the opportunity to assess working memory, recognition memory, and delayed recall. A more brief “memory screening” option is also available.
+
+Content
+
+In all, the WRAML2 is comprised of 6 core subtests and 11 optional subtests, generating 9 possible global index scores. Several subtests from the original WRAML (Sheslow and Adams 1990) are now optional (e.g., Sentence Memory) or limited to a specific age group (e.g., Sound Symbol for ages 5–8 years; Verbal Working Memory and Symbolic Working Memory for individuals 9 years of age and older). New additions include two subtests to assess working memory and four recognition memory tasks. In addition, items have been updated on existing subtests, such as contemporary full-color scenes on the Picture Memory subtest, revised stories in story memory (lengthened to accommodate adults), and more contemporary language on the Sentence Memory subtest. One subtest from the WRAML, Visual Learning, has been eliminated. The core battery can be individually administered in under an hour, and a memory screening form, composed of four subtests, requires 10–15 min administration time and correlates highly (r =.91) with the full test. The WRAML2 yields standard scores, scaled scores, and percentiles. The test also provides several qualitative analyses scores. Age equivalents are available for the child and preadolescent age groups. Subtest raw scores are converted into scaled scores based on a mean of 10 and standard deviation of 3. Index scores are derived from the sum of the relevant subtest scores and reported with a mean of 100 and standard deviation of 15 for age-based performance comparisons. A profile grid is provided allowing the examiner to plot scores across subtests and indexes for interpretation. A computerized scoring program is also available. The global domains (underlined) and subtests (italicized) of the WRAML2 are described briefly below:
+
+*   Verbal Memory Index. An index of how well the individual is able to learn and recall both meaningful verbal information and relatively rote verbal information.
+*   Story Memory Subtest. A measure of memory for contextualized or meaningful verbal information. Two short stories are read to the examinee; following each story, the examinee is asked to orally recall as much of the story as he/she can remember.
+*   Verbal Learning Subtest. A measure of how well the individual actively learns, with practice opportunities, and is able to recall meaningful verbal information that is without context. The evaluator reads a list of common, single-syllable words followed by an immediate free recall trial; this list is repeated across four trials.
+*   Visual Memory Index. An index of how well the individual can learn and recall both meaningful (i.e., pictorial) and minimally related, rote (i.e., design) visual information.
+*   Design Memory Subtest. A measure of memory for visual material that is minimally meaningful. Five cards, each with an array of geometric shapes, are exposed, and then after a brief delay, the examinee is asked to draw what is remembered.
+*   Picture Memory Subtest. A measure of nonverbal immediate memory for contextualized information or meaningful information. The examinee is shown a colorful, everyday scene that he/she can scan for 10 s before it is removed. Then a similar alternate scene is immediately presented, and the participant must determine the elements that have been moved, changed, or added.
+*   Attention/Concentration Index. An index of how well the individual can learn and recall relatively nonmeaningful rote, sequential information. The tasks comprising this index require brief attentional demands and/or immediate rote recall abilities.
+*   Finger Windows Subtest. A measure of nonverbal, rote sequential recall; the examinee is asked to repeat gradually more difficult visual sequential patterns demonstrated by the examiner.
+*   Number Letter Subtest. A measure of auditory, rote sequential recall; the examinee is asked to repeat a series of items consisting of progressively longer number and letter sequences that are dictated by the examiner.
+*   General Memory Index. An index of overall memory functioning derived from performance on the six core subtests above.
+*   Working Memory Index. An index of how well the individual can operate on (e.g., add to, reorganize, or manipulate) and retain information that is held in the short-term memory buffer (i.e., in an active, online state).
+*   Verbal Working Memory (for ages 9 years and older). A measure of how well an individual retains information that is manipulated while it is in short-term memory. The examinee listens to a list of nouns that are both animals and nonanimals and, immediately thereafter, is required to recall the words in a specified reorganized order.
+*   Symbolic Working Memory (for ages 9 years and older). A measure of how well a person actively operates on and retains symbolic information (e.g., numbers, letters) prior to recall. Memory and attention are important for this task, and executive skills are also involved in finding an appropriate learning strategy.
+*   Verbal Recognition. An index of how well specific verbal information that was presented previously during this testing session is recognized following a short delay (15–20 min).
+*   Story Memory Recognition. A measure of recognition memory using a multiple-choice format with questions from each previously presented story. A comparison can be made between free recall (in the immediate recall and the delayed recall story memory formats) and recognition.
+*   Verbal Learning Recognition. Provides a measure of recognition recall of verbal information presented previously as part of the verbal learning subtest.
+*   Visual Recognition. An index of how well the individual can recognize specific nonverbal or visual information that was presented previously in the session (i.e., approximately 15–20 min earlier).
+*   Design Memory Recognition. A measure of recognition recall of visual information that was viewed approximately 20 min earlier. The examinee is asked to look at and distinguish between designs that were and were not presented earlier in the testing session. It involves attention to visual detail and executive skills.
+*   Picture Memory Recognition. A measure of recognition of previously presented meaningful visual information or contextualized visual information, such as that found in pictures, in scenes, or in the environment.
+*   General Recognition. In contrast to memory retrieval as represented in the verbal memory index and the visual memory index scores, the general recognition index provides an estimate of how well the individual can recognize specific verbal and visual information that was presented previously (i.e., approximately 15–20 min earlier). This information is relevant to initial encoding strategies.
+*   Delay Recall. No global index score is provided for delayed recall; however, for each individual subtest, retention scores are generated providing a measure of memory decay over time.
+*   Story Memory Delay Recall. A measure of memory for a meaningful verbal narrative over a 10–15-min delayed interval.
+*   Verbal Learning Delay Recall. A measure of memory retrieval after the initial learning of new verbal information (approximately 15 min after initial learning).
+*   Sound Symbol Delay Recall (for ages 8 and younger). A measure of memory retrieval for paired associates (visual-auditory verbal) over a short delay.
+*   Additional Subtests
+*   Sentence Memory. A measure of immediate verbal memory skills similar to those needed to follow novel verbal directions or to relay a brief phone message accurately. On this subtest, the examinee is required to repeat dictated single sentences of increasing length.
+*   Sound Symbol (for ages 8 and younger). A measure of paired-associate memory and learning. The examiner shows a symbol appearing on a page of the easel booklet and then identifies its sound that the child then repeats. The child is asked to try to recall that same sound when he/she sees the same symbol.
+
+Historical Background
+
+The first edition of the WRAML was published in 1990, filling a need for a comprehensive well-normed battery assessing children’s memory functioning. The original WRAML was normed for children ages 5–17 years. The second edition, published in 2003, provides updated and extended norms (ages 5–90 years) as well as new test material. Focus groups were used to obtain first-hand information about improving the test; an item “tryout” was followed by a standardization program. Only one subtest (Visual Learning) was eliminated in the revision.
+
+Psychometric Data
+
+Norms. The standardization edition of the WRAML2 was administered to 1200 individuals across 22 states, representing different geographical regions. A national stratified sampling technique was used, controlling for age, sex, race, region, and education, with efforts to make the normative sample mirror the 2001 US Census. Slight variations in the normative sample from census data were corrected with a statistical weighting procedure.
+
+Reliability. Scores from the WRAML2 are highly reliable. Reliability coefficients for the core battery Verbal Memory Index, Visual Memory Index, and attention/Concentration Index are .92, .89, and .86A, respectively. The alpha reliability for the general index is .93. Internal consistency for the six core subtests is also shown to be very good, with Cronbach’s alpha coefficients ranging from .82 to .96. The range is wider for the optional subtests (.63–.96). Test-retest reliability indicates a substantial learning effect from one test time to another (median administration lag of 49 days) with an overall gain of approximately +1 scaled score on subtests. Therefore, reliability correlations are lower with correlations between .53 and .85 for core subtests and indexes and .47 to .80 for optional subtests. Inter-rater reliability was quite high (.98).
+
+Validity. Internal validity was assessed via examination of item content, subtest intercorrelations, exploratory factor analyses, confirmatory factor analysis, and differential item functioning. Item separation reliabilities, indicating how well the items define the variable being measured, were high (.90–1.00 for all subtests). Intercorrelations of the WRAML2 indexes and subtests were high for the older group (9 years to adult) but more variable in younger children. Results from factor analysis studies supported the internal validity of the WRAML2. External validity was examined by comparing scores on the WRAML2 with related memory and learning measures, such as the Wechsler Memory Scale-III (WMS-III), Children’s Memory Scale (CMS), and the California Verbal Learning Test-II (CVLT-II). Correlations were moderate (e.g., r = .60 for the WRAML2 General Memory Index score and the WMS-III General Memory Index score and r = .49 for the CMS). Moderate correlations were also found with cognitive measures (i.e., Wechsler Adult Intelligence Scale [WAIS-III] and the Wechsler Intelligence Scale for Children, Third Edition [WISC-II]) and with achievement tests (i.e., Wide Range Achievement Test 3 [WRAT3], and the Woodcock-Johnson, Third Edition [WJ-III] Tests of Achievement).
+
+Clinical Uses
+
+Learning and memory are fundamental to academic success. Therefore, an instrument such as the WRAML2 may be highly useful when evaluating individuals with a learning disability and/or school-related problems. For children with attentional difficulties, administration of the WRAML2 may help to clarify the contributions of attention, learning, and/or memory problems. This measure may also be used to assess memory impairment following head injury. The manual contains information about small studies involving learning disabled children and adults with traumatic brain injury, Alzheimer’s disease, and alcohol abuse, suggesting WRAML2 sensitivity to these influences.
+
+Wide-Range Assessment of Visual-Motor Abilities
+Visual-Motor Integration, Developmental (VMI) Test
+
+Wilbarger Protocol
+Deep Pressure Proprioception Touch Technique
+
+Williams-Beuren Region Duplication
+7q11.23 Duplications
+
+Wing Subgroups Questionnaire (WSQ)
+Behavior Development Questionnaire
+Behavioral Development Questionnaire
+
+Wing, Lorna
+
+Landmark Clinical, Scientific, and Professional Contributions
+
+In 1979, Lorna Wing and her Maudsley colleague, Judith Gould, published a landmark study of 173 children in Camberwell, South London, who had autistic features or had an IQ of under 50, or both. As Wing told Adam Feinstein in 2009, there were certainly some children who beautifully fitted Leo Kanner’s 1943 criteria for autism, “but there was a huge collection in the middle who could not be put into either category. Very few fitted Asperger’s syndrome, because they virtually all had an IQ of under 60 and none were mainstreamed.” From their findings, Wing and Gould concluded that there was clearly a broader autism phenotype. This inspired them to introduce the concept of the triad of impairments in autism: deficits in social relations, communication, and imagination, a concept still used by diagnosticians today. Some researchers, like Francesca Happé at London’s Institute of Psychiatry, maintain that, while the Wing-Gould version does indeed form a true unitary triad, the DSM-IV and ID-10 notion of repetitive behavior – rather than imagination – as the third element has meant that the triad becomes “fractionable” (in the sense that the three elements may have separate causes at the genetic, neurological, and cognitive levels).
+
+Short Biography
+
+Dr. Lorna Wing, FRCPsych, born on October 7, 1928, is an English psychiatrist and physician and one of the world’s leading autism authorities. As a result of having an autistic daughter, Suzie, she became involved in researching developmental disorders, particularly autism spectrum disorders. She joined with other parents of autistic children to found the National Autistic Society (NAS) in the United Kingdom in 1962. She currently works part time as a consultant psychiatrist at the NAS Centre for Social and Communication Disorders at Elliot House. Wing is the author of many books and academic papers, including Asperger’s Syndrome: a Clinical Account, a 1981 academic paper that popularized the research of Hans Asperger and introduced the term “Asperger’s syndrome.” Although groundbreaking and influential, Wing herself cautioned in her 1981 paper that “it must be pointed out that the people described by the present author all had problems of adjustment or superimposed psychiatric illnesses severe enough to necessitate referral to a psychiatric clinic . . . (and) the series described here is probably biased towards those with more severe handicaps.”
+
+Books
+
+*   (1964). Autistic children.
+*   (1966). Physiological measures, sedative drugs and morbid anxiety, with M.H. Lader.
+*   (1969). Children apart: Autistic children and their families.
+*   (1969). Teaching Autistic children: Guidelines for teachers.
+*   (1971). Autistic children: A guide for parents.
+*   (1975). Early childhood autism: Clinical, educational and social aspects (editor).
+*   (1975). What is operant conditioning?
+*   (1988). Aspects of autism: Biological research (editor).
+*   (1989). Hospital closure and the resettlement of residents: Case of Darenth park mental Handicap hospital.
+*   (1995). Autistic spectrum disorders: An aid to diagnosis.
+*   (1996). The autistic spectrum: A guide for parents and professionals.
+*   Waites, J., & Swinbourne, H. (2002). Smiling at shadows: A mother’s journey raising an autistic child.
+
+Winkleman v. Parma City School District (2007 Rights Under IDEA)
+
+Definition
+
+In General
+
+In this case, the U.S. Supreme Court held that the Individuals with Disabilities Education Act (IDEA) grants parents independent, enforceable rights. Parents are not limited to bringing claims on behalf of their child or claims based on procedural and reimbursement-related matters, but may sue to enforce their own rights to a “free appropriate public education” (FAPE) for their children. The Supreme Court based its decision on statutory analysis, legislative intent, and policy considerations (Steiner 2008, p. 1172). IDEA entitles parents to voice their concerns as part of the individualized education program and (IEP) team as well as to submit a complaint concerning the adequacy of the education, the IEP’s construction, or other related matters. “Any party aggrieved,” including a parent, also has the right to bring a civil action based on that complaint. Additionally, IDEA’s express purpose is to protect “the rights of children with disabilities and parents of such children.” The court held that IDEA was intended to confer separate rights to parents of children with disabilities.
+
+Implications for ASD Students
+
+Since parents have their own independent rights under IDEA, a parent of a student with an autism spectrum disorder (added to IDEA’s list of disabilities in 1991), whether or not he or she can afford an attorney, now has the right to enforce his or her rights as a parent to obtain a quality education for their child. This makes IDEA a more substantive and robust reform of special education law (McNeal).
+
+Litigation Strategies
+
+While the decision appears clear-cut, it has led to significant confusion and problems in the lower courts (Zirkel 2009, p. 1). It triggers many larger issues, which are not resolved by the court. For example, there is no clear distinction between the child’s rights under IDEA and the rights of the parents and no sense of what remedy there is for a violation of parental rights. In addition, there is a concern that this decision allows parents to represent their children’s substantive interests pro se which violates common law principles and public policy considerations (Steiner 2008, p. 1181). These considerations seek to ensure that children are represented by those that can best represent them. Still, the decision does clearly allow parents to lower the potential costs of litigation by suing without representation.
+
+WISC
+WISC – V
+
+WISC – V
+
+Synonyms
+WISC
+
+Description
+
+The Wechsler Intelligence Scale for Children–Fifth Edition (WISC–V) is a standardized clinical assessment instrument. The clinical instrument assesses general cognitive abilities in children ages 6–16 years 11 months; specifically, it assesses general problem-solving and reasoning skills in verbal, nonverbal, and visual domains, as well as working memory and processing speed in children and adolescents. This test is individually administered, scored, and interpreted by a trained clinician. Administration typically can be completed within 60–90 min. The WISC–V results in standard scores according to age-referenced norms from a national sample; standard scores can be calculated for specific cognitive indices as well as one or more global indicators of cognitive ability including a Full-Scale Intelligence Quotient (Full-Scale IQ). The WISC–V framework consists of a Full-Scale Index (or Full-Scale IQ), in addition to primary, ancillary, and complementary indices that are made up of various combinations of primary, secondary, and complementary subtests that are the actual tasks administered by the clinician to the individual. The primary indices that form the core of the cognitive measure are Verbal Comprehension (VCI), Visual Spatial (VSI), Fluid Reasoning (FRI), Working Memory (WMI), and Processing Speed (PSI). These indices are the result of a five-factor model of cognitive abilities (Wechsler 2014). The five primary indices are derived from ten primary subtests: Vocabulary, Similarities, Block Design, Visual Puzzles, Matrix Reasoning, Figure Weights, Digit Span, Picture Span, Coding, and Symbol Search. Two primary subtests make up each index score, with complementary subtests rounding out a fuller measurement of the cognitive abilities of each index area.
+
+The Verbal Comprehension Index (VCI) is comprised of the primary subtests, Similarities and Vocabulary, and the secondary subtests, Information and Comprehension. The VCI subtests assess an individual’s ability to access and apply acquired word knowledge. The application of this knowledge involves verbal concept formation, reasoning, and expression. The Visual Spatial Index (VSI), which is comprised of two primary subtests, Block Design and Visual Puzzles, assesses an individual’s ability to evaluate visual details and to understand visual spatial relationships to construct geometric designs from a model. The ability to construct designs requires visual spatial reasoning, integration and synthesis of part–whole relationships, attentiveness to visual detail, and visual-motor integration. The Fluid Reasoning Index (FRI) measures a person’s ability to detect the underlying conceptual relationships among visual objects and to use reasoning to identify and apply rules. Identification and application of conceptual relationships require inductive and quantitative reasoning, broad visual intelligence, simultaneous processing, and abstract thinking. The FRI is comprised of two primary subtests, Matrix Reasoning and Figure Weights, and two secondary subtests, Picture Concepts and Arithmetic. The Working Memory Index (WMI) assesses the ability to register, maintain, and manipulate visual information through the Picture Span subtest and auditory information through the Digit Span subtest. Letter–Number sequencing is a secondary subtest within the WMI that further assesses working memory through an auditory information task. The last primary index, Processing Speed, measures an individual’s speed and accuracy of visual identification, decision-making, and decision implementation. Performance on the two primary PSI subtests, Coding and Symbol Search, and one secondary subtest, Cancellation, is related to visual scanning, visual discrimination, short-term visual memory, visuomotor coordination, and concentration.
+
+In total, the WISC–V consists of 21 subtests, only ten of which are considered primary subtests and essential to calculating primary index scores. Seven of the ten primary subtests combine to arrive at the Full-Scale IQ (Similarities, Vocabulary, Block Design, Matrix Reasoning, Figure Weights, Digit Span, and Coding). The remaining 11 subtests are considered secondary or complementary. Secondary subtests (Information, Picture Concepts, Letter–Number Sequencing, Cancellation, Comprehension, and Arithmetic) can be used as a substitute for a related primary subtest to calculate the Full-Scale IQ. Additionally, secondary subtests are recommended for administration in order to provide more in-depth information about a child’s intellectual functioning and for clinical decision-making, as they make up ancillary index scales. Complementary subtests (Naming Speed Literacy, Naming Speed Quantity, Immediate Symbol Translation, Delayed Symbol Translation, and Recognition Symbol Translation) are not designed as measures of intelligence but rather to aid in assessment of cognitive processing associated with academic learning. The Complementary subtests comprise the complementary index scales of Naming Speed, Symbol Translation, and Storage and Retrieval. The WISC–V provides global indicators of cognitive functioning via the Full-Scale IQ, as well as ancillary indices, specifically, the Nonverbal Index, General Ability Index, and Cognitive Proficiency Index. The ancillary indices may be interpreted as a better estimate of a given child’s overall cognitive abilities than the Full-Scale IQ based on the individual’s performance profile. More specifically, when there is an unusually high amount of variability between subtest scores that comprise the Full-Scale IQ, this Index score is not considered to be a good measure of the child’s global intellectual ability, and it is recommended that other global estimates of cognitive functioning be reported. The Nonverbal Index is a measure of general cognitive ability that minimizes expressive language demands. The General Ability Index is an estimate of cognitive ability that reduces emphasis on working memory and processing speed, and it is thought to be a more pure representation of crystallized intelligence, which is heavily based on language and previously learned information. Lastly, the Cognitive Proficiency Index is the summary index made up of the four working memory and processing speed subtests, thus providing a global estimate of a child’s cognitive efficiency for manipulating and rapidly processing information.
+
+Historical Background
+
+Intelligence tests were first developed in the early 1900s to more objectively assess and quantify aspects of disability and mental illness, in addition to qualifications or capabilities of an individual. There are various theories and definitions of intelligence and thus various theories on how to measure cognitive functioning (Sattler 2008). David Wechsler (see Wechsler, David), the creator of the original Wechsler scales that have evolved into the current WISC–V, considered intelligence to be a global factor. He designed his original intelligence scale to consider factors contributing to the effective intelligence of the individual with the overall IQ score representing an index of general mental ability. Thus, from its outset, the Wechsler intelligence scales are rooted in a factor theory of intelligence – espousing to the theory of a general intelligence that is comprised of multidimensional cognitive abilities. Wechsler developed the Wechsler–Bellevue Intelligence Scale in 1939 for use with adults, and in 1949, the first edition of the Wechsler Intelligence Scale for Children (WISC; Wechsler 1949) was created as a downward extension of the Wechsler–Bellevue scale. The WISC was the first intelligence test created for use in children, specifically children ages 5 to 15 years old (Sattler 2008). The original scales consisted of 12 subtests, many of which remain on the current version in an updated form. The subtests were organized into Verbal and Performance scales resulting in a Verbal IQ (VIQ), Performance IQ (PIQ), and Full-Scale IQ. In 1974, the first revision was produced, the Wechsler Intelligence Scale for Children���Revised (WISC–R; Wechsler 1974). The revision changed the targeted age range for the instrument to 6- to 16-year-old children. After Wechsler’s death in 1982, Psych Corp produced subsequent editions of the WISC and continues to list David Wechsler as the author through the current version.
+
+The third edition (WISC–III; Wechsler 1991) was the first revision to introduce significant content and structure changes to the measure. A new subtest for processing speed was developed for the WISC–III, and the measure was organized into more specific indices of cognitive functioning beyond the overarching composites of VIQ, PIQ, and FSIQ: Verbal Comprehension (VCI), Perceptual Organization (POI), Freedom from Distractibility (FDI), and Processing Speed (PSI). The next revision, WISC–IV, was developed in 2003 (Wechsler 2003). The development of the WISC–IV again ushered in major changes to subtest content and structure including updated item content and scoring procedures. The original VIQ, PIQ, and FSIQ were dropped and formally replaced with the specific indices developed on the WISC–III that were revised to reflect the cognitive abilities assessed by the WISC: the Verbal Comprehension Index (VCI); the Perceptual Reasoning Index (PRI), formally named Perceptual Organization; the Working Memory Index (WMI), formerly named the Freedom from Distractibility; and the Processing Speed Index (PSI). The WISC–V is the most recent revision of the WISC. One of the major changes in the new edition is the update to the theoretical foundation of cognitive functioning and intellectual assessment through the use of a five-factor model of test structure. The transition from a four- to five-factor model was born from factor analytic research of the WISC–IV and the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV; Wechsler 

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+Writing Disorders
+
+Short Description or Definition
+Writing is considered to have two basic components: the process and product of writing. Writing processes include “planning (prewriting), organizing, drafting, reflecting, revising, and editing. . .as well as forming letters and sequences of letters into words” (American Speech Language Hearing Association [ASHA] 2001), while the products of writing are the final form of the composition produced as either handwritten or word-processed text. Writing involves not only the physical process of forming letters and sequences of letters into words and using appropriate writing mechanics, such as punctuation and capitalization, then, but also consists of the planning, organizing, drafting, reflecting, revising, and editing that leads to the final product (Paul and Norbury 2012; Scott et al. 2009). Children who find writing difficult early in their education often perform poorly on academic measures as they progress through the grades (Nancollis et al. 2005). Children with speech-language disabilities, including those with ASD, are at risk for writing difficulties (Puranik et al. 2008).
+
+Assessment
+Writing can be assessed informally, using rubrics to rate the quality of various components of the writing process and product (e.g., Paul and Norbury (2012); Westby and Clauser 2005). In addition, standardized tests are available for the identification of writing disorders. These may be administered by a speech-language pathologist, learning disability specialist, or special educator. They include, for example, the Oral and Written Language Scales (Carrow-Woolfolk 1996), the Test of Adolescent and Adult Language-4 (Hammill et al. 2007), the Test of Written Expression (McGhee et al. 1995), the Test of Written English (Anderson and Thompson 1988), the Test of Written Language-4 (Hammill and Larsen 2009), the Woodcock Language Proficiency Battery-Revised (Woodcock 1991), the Writing Process Test (Warden and Hutchinson 1992), and the Written Language Assessment (Grill and Kerwin 1990).
+
+Categorization
+The characteristics of writing disorders, according to Scott et al. (2009), include:
+
+*   Poor planning, monitoring, evaluating, and revising work. Children with writing difficulties have been described as having a “stream of consciousness” writing style (Graham and Harris 1999). Because of this, they demonstrate text-structure problems (with paragraph organization, transitions between paragraphs, and story plots). They often do not recognize and correct errors as they write (Roth 2000) or go back over their writing to correct errors later (Scott 2005).
+
+*   Difficulty with writing mechanics. Graham and Harris (1999) and MacArthur (2000) observe that children with writing difficulties struggle with spelling, capitalization, punctuation, and handwriting. In addition, they often write sentences that are shorter and simpler than those of their peers. They have fewer clauses per sentence, they overuse and to begin sentences, and often omit grammatical word endings, such as -ed (to express past tense).
+
+*   Sparse writing. Children who struggle with writing write less and get less practice writing (Wong 2000). Because of their language deficits, these children often have limited vocabularies and may not know the right word to express their ideas. They may forget the main point, plan, or the structure of their text because they got stuck on the mechanical process of writing. These findings were confirmed by Scott (2005), who found that problems with thinking through and controlling the steps of writing create difficulty in both excluding unimportant material as well as including appropriate information.
+
+*   Ineffective revision. Children with writing difficulties tend to view revising as proofreading. Revisions are typically made at the sentence level, without considering changing content (Roth 2000).
+
+Epidemiology
+There are no precise estimates of prevalence for writing disorders, although the National Center for Learning Disabilities estimates that 5% of school-aged children have learning disorders; most who have disabilities in the area of reading will have writing disorders, as well.
+
+Natural History, Prognostic Factors, and Outcomes
+Natural History: Some children may have difficulty with the physical act of writing, the ability to form letters, as early as the preschool years. This specific deficit in handwriting is often referred to as dysgraphia. For most children, writing problems will be identified in 2–4th grade, when school-sponsored writing and spelling are targeted within the curriculum. These difficulties usually accompany reading problems and executive function difficulties and can persist throughout development, into adulthood. However, many adolescents and adults can develop compensatory strategies to that help them cope with their weaknesses and can find careers that allow them to use their strengths for successful adjustment.
+Prognostic Factors: Children with reading disorders are at risk for writing disorders, as well.
+Outcomes: Although deficits in spelling, handwriting, and composition often persist into adulthood, many adults find ways to be successful in work and life.
+
+Clinical Expression and Pathophysiology
+No pathophysiological signs have been identified that are specific to developmental writing disorders, although adults with acquired dysgraphia are generally found to have lesions in the parieto-occipital region.
+
+Evaluation and Differential Diagnosis
+Evaluation and Differential Diagnoses: Writing disorders that are specific to letter formation and handwriting (dysgraphia) need to be distinguished for general motor weakness, spasticity, and incoordination that would affect processes other than writing, such as self-feeding and other hand skills. Since writing disorders almost always occur in conjunction with reading disorders, the two do not require differential diagnosis, but writing ability should always be evaluated in students with reading disorder.
+
+Treatment
+Intervention
+Several aspects of writing may be addressed in an intervention program, including:
+
+*   Writing mechanics. Children who have specific difficulties with writing mechanics may demonstrate poor handwriting skills, may fatigue or experience hand cramps quickly when writing, and may be “poor spellers.” If this is the case, the use of assistive technology, such as word processing, can be utilized effectively to reduce fine-motor demands of handwriting (MacArthur 2000). In addition, the spell check function in a word processing program can be helpful for a child who experiences difficulty with writing mechanics and spelling (MacArthur 2000). Working with word prediction software in intervention is also an option for children with problems in writing mechanics. This software predicts a word to type, based on what has been written so far, reducing the stress of both word finding and spelling.
+
+*   Planning and composing. One effective strategy for improving writing process is Self-Regulated Strategy Development (SRSD; Graham et al. 2000). There are six “stages” in the SRSD: (1) develop background knowledge; (2) identify strategy, goals, and significance; (3) modeling of the strategy; (4) memorization of the strategy; (5) collaborative practice; and (6) independent practice. A variety of other strategies are described in Paul and Norbury (2012).
+
+*   Revising. Children with writing disorders may benefit from the use of assistive technology, such as speech synthesis software that allows the computer to read the composition aloud, while highlighting the words being spoken. The student can then use oral language skills to make revisions. Collaborating with classmates or teachers during revision may also be helpful (MacArthur 2000; Wong 2000). Students can be divided into small groups for peer revisions. Each child is instructed to provide, for example, two positive comments and two suggestions for improvement for their classmates’ compositions. The students might also ask questions about unclear or underdeveloped aspects of compositions.
+
+*   Visual aids and graphic organizers have also been shown to improve writing. Visual outlines in the form of flow charts and maps (“webs”) can help students organize the writing process (Hyerle 2004). These can be used as a basis for creating a more mature composition. Additional examples of intervention approaches for writing difficulties can be found in Paul and Norbury (2012).
+
+See Also
+Dysgraphia
+Learning Disabilities
+
+Writing Interventions for Individuals with Autism Spectrum Disorder
+
+Definition
+Writing is a means to communicate and express ideas, thoughts, feelings, and information. Writing interventions refer to effective and evidence-based instructional practices used to support individuals in developing varied elements of writing including but not limited to letter formation, handwriting, length of writing, sentence structure, and quality of writing. Writing interventions often target specific genres of writing including but not limited to descriptive writing, expository writing, narrative writing, and persuasive writing.
+
+Historical Background
+The topic of writing interventions and students with autism spectrum disorder (ASD) was not widely considered prior to the twenty-first century. Preceding the enactment of legislation in the 1970s, students with disabilities were denied access to general education. In 1973, Section 504 of the Rehabilitation Act was signed, mandating the inclusion of individuals with disabilities in all programs that receive federal funding, including public schools. In 1975 the Education for All Handicapped Children Act (P.L. 94-142) was signed mandating the rights of students with disabilities to a free and appropriate education in the least restrictive environment. Despite these mandates including students in public education, there was a prevailing assumption that most individuals with ASD would not benefit from instructional time spent on academic skills including writing, and instead the primary focus of education was to address behavioral and life skills. In 1990, P.L. 94-142 was renamed as the Individuals with Disabilities Education Act (IDEA). IDEA was reauthorized in 1997 with an impactful amendment that required students with disabilities to participate in local and state assessments. IDEA was further reauthorized as the Individuals with Disabilities Education Improvement Act (IDEIA) in 2004, mandating students with disabilities receive academic instruction founded in scientifically based research. These mandates have brought about increased expectations for the academic performance of students with disabilities and the increasing inclusion of students with disabilities in general education classrooms. This includes access to the general education curriculum and participation in high-stakes testing for students with ASD in our public schools. Students with ASD, however, have been found to struggle with academic performance, including in the domain of written expression, yet skill in writing is paramount to success in local and state assessments. Moreover, developing skill in functional writing as a communication tool is especially important for the subset of individuals with autism spectrum disorder (ASD) with limited or unreliable speech. Faced with the mandates of IDEIA (2004), many teachers have found them-selves unprepared to provide instruction that meets the individualized needs of students with ASD as well as prepares them to meet state standards. This established a need for high-quality writing interventions for students with ASD. Fortunately, there has been increased attention on how to enhance the academic performance of students with ASD in the area of writing.
+
+Current Knowledge
+Individuals with ASD often experience reduced success in writing, placing them at a disadvantage in communicating their thoughts, fully participat-ing in school, or gaining employment (Pennington and Delano 2012). Specific writing difficulties include using fewer words and making more spelling errors than their neurotypical peers and having weaker overall structure in their writing (Asaro-Saddler 2014, 2016). In a study of the learning profiles of children with high-functioning autism, Mayes and Calhoun (2008) report that despite strength in visual and verbal reasoning, students with ASD have writing weaknesses, including difficulty with handwriting and with expressing thoughts on paper. Additionally, difficulty with perceptual and visual motor tasks has been found to impact functional handwriting including length, speed, and legibility of writing (Ashburner et al. 2012). Differences in executive functioning and theory of mind (ToM) have frequently been reported to impact the writing of individuals with ASD. Executive functioning differences may result in difficulty generating, planning, and integrating ideas in writing for individuals with ASD, and as a result instructional practices aimed at supporting the planning, organization, and self-monitoring of writing emerge as essential (Delano 2007). Differences in ToM, the ability to consider the thoughts or feelings of others (Baron-Cohen et al. 1985), have been reported as impacting the ability of individuals with ASD to write persua-sively (Asaro-Saddler and Bak 2014) and the quality of both narrative and expository texts (Brown and Klein 2011). More recently, an emphasis on ToM differences as opposed to deficits has led to consideration of how ToM variations may also emerge as strengths for individuals with autism (see Atherton et al. 2019). For example, strength in imagination and attention to detail may enrich the narrative story writing of individuals with ASD.
+
+Writing Interventions
+Numerous interventions have been identified to improve the writing of individuals with ASD. Many of these writing interventions stem from practices identified as evidence-based (EBPs) for instructing students with ASD in a variety of skill areas (see Reichow et al. 2008). Such EBPs include reinforcement, task analysis, prompting, time delay, video modeling, and use of visual supports (National Professional Development Center on ASD 2014). In fact, combining multiple instructional practices to individualize writing intervention for individuals with ASD has emerged as a common practice with interventions frequently combining visual supports, such as a graphic organizer, along with positive reinforce-ment (Accardo et al. 2019). Reviews of the liter-ature detail writing interventions to support individuals with ASD (see Pennington and Delano 2012; Asaro-Saddler 2014; Accardo et al. 2019). Recommended writing interventions along with examples of implementation follow.
+
+Self-regulated strategy development. Self-regulated strategy development (SRSD) is an instructional package that includes the teaching of academic strategies for writing combined with instruction in self-regulation. Through SRSD, teachers individualize instruction and guide students to use writing strategies while also providing support for student goal setting, self-regulation, and motivation. Instruction includes six stages: (1) developing background informa-tion needed for writing; (2) discussing the purpose and benefit of the strategy to be used; (3) modeling of the strategy; (4) memorization of the strategy; (5) supporting students in use of the strategy; and (6) independence, evidenced by student generalization of the strategy across a variety of tasks (Santangelo et al. 2008). SRSD is individu-alized with instruction at each stage continuing until student mastery through a criterion-based approach.
+
+SRSD has emerged as a highly effective writing intervention for individuals with learning disabilities as well as individuals with ASD. In a study of the use of SRSD with three elementary-age students with ASD, students were taught to use the POW-+ Tree strategy and the WWW, What-2, and How-2 writing strategy, given a choice of writing tasks, a motivational “rocket” chart, and visual supports. All of the students increased the number of story elements used, the holistic quality, and the number of words when writing stories (Asaro-Saddler 2014). Similarly, in a study of SRSD which included choice of writing tasks, self-regulation charts, cue cards, and a com-puter graphic organizer program, all of the stu-dents aged 18–20 increased the quality of argument-driven writing and writing duration (using the strategy DATE + SCORES) and quality appeared to generalize to prompts in a college writing course (Jackson et al. 2018). In a review of the research literature on the effectiveness of SRSD with individual with ASD, Asaro-Saddler (2016) concluded that most students with ASD participating in SRSD instruction increased both the quality and quantity of their writing. In addi-tion, students increased their planning time and their use of self-regulatory strategies. Since many students with ASD are reported to have difficulty with executive functioning tasks such as planning, organizing, and self-monitoring, the SRSD method may help these students to organize their thoughts and to check the quality of their writing.
+
+Graphic organizers. The use of visual information during instruction, such as through graphic organizers and pictorial representations, allows students to process information faster and easier. Graphic organizers provide a visual repre-sentation of information to enhance learning. As a component of the SRSD method, graphic orga-nizers have been shown to be part of an effective instructional package for enhancing the writing skills of individuals with ASD. For example, Bishop et al. (2015) implemented a graphic orga-nizer package to improve the persuasive writing of three middle school students with ASD. The graphic organizer had spaces for planning – a brainstorming box, three boxes for reasons, a counter-argument box, an introduction box, and a conclusion box. After training with the graphic organizer, all three students improved on all three of the outcome measures – correct writing sequences (defined as “two adjacent writing units that are correct within the context of what is written”), total words written, and a rubric score.
+
+Behavioral Principles
+Prompting. Instructional methods derived from applied behavior analysis (ABA) are also recommended to enhance the writing of individ-uals with ASD (see “Progressive Applied Behavior Analysis”), often in combination with other instructional methods. One such method is prompting. For example, response prompting was combined with sentence frames to improve the sentence writing of 7–12-year-old students with moderate intellectual disability and ASD (Pennington et al. 2018). A sentence frame is a scripted portion of a sentence – for example, “I want ______” – that can be completed by using a targeted written response such as “I want the candy.” During instruction, the teacher presented the student with a preferred item and the sentence frame and prompted the student to respond by saying “Write a sentence to tell me what you want.” If the student failed to respond within 10 s, the teacher prompted the student using a predetermined prompt hierarchy. If the student responded correcting the teacher provided either p-raise or a tangible reinforcer. Using these pro-cedures, each of the students produced three types of sentences and maintained their responding fol-lowing the intervention.
+
+Task Analysis. Similarly, a task analysis approach can be used to enhance the writing of individuals with ASD. For example, Lee et al. (2016) used a task analysis approach to identify the steps needed to teach three skills: identification of key ideas in a paragraph, identifying supporting details and recording information on a graphic organizer, and composing informational text. Two students with significant intellectual disabilities, one of whom was identified as autis-tic, were instructed on writing skills using the task analyses as the framework for the instruction. In addition, during instruction teachers used a graphic organizer that consisted of written pro-mpts for each of the three target skills and utilized a least intrusive prompt approach if the student did not respond during the lesson. Following instruc-tion, both of the students made significant improvements on all three skills.
+
+Reinforcement. Reinforcement of behavior is another ABA method that has frequently been used to teach writing skills. In their review of the research on writing interventions for students with ASD, Pennington and Delano (2012) identified 11 studies in which reinforcement was used. A number of different methods were identified, including the delivery of tokens, a computer-delivered auditory fanfare for correct responding, and a reinforcing video clip. Since student prefer-ences can vary widely, it is important to assess individual student response to reinforcers before choosing a method to use to reinforce student responses to writing tasks.
+
+Assistive Technologies
+Computer-aided instruction. Assistive technolo-gies, including computer-aided instruction, voice output communication aids (VOCAs), multimedia presentations, and video modeling, have been successfully used to improve the writ-ing of individuals with ASD. Using computer-aided instruction (CAI), Stromer et al. (1996) developed software to teach a student with intel-lectual disability and ASD to improve spelling. The student was presented with a sample word on the computer screen which, when touched, disappeared and was replaced by a set of letters. The student could then select the correct letters to spell the target word. When the student responded correctly, the computer lit up with a flashing display, and the student received a token that could be exchanged for a preferred item. The student with ASD was able to suc-cessfully spell words using this method, and spelling improvement was found to generalize to other tasks. Additional studies provide example of how CAI, used in combination with other instructional practices, can be used to teach writing skills to individuals with ASD. Pennington et al. (2014) combined simultaneous prompting, an errorless teaching procedure involving the delivery of a controlling prompt immediately following a dis-criminative stimulus, with CAI to teach story writing to five students with autism (see Pennington et al. 2010, 2014). Story templates were created using commercially available soft-ware, and the participants were prompted to create a story using words and pictures on the story template. In both studies the researchers found that the combination of simultaneous prompting with computer-aided instruction improved the ability of the students to construct stories. Additionally, students showed evidence of gener-alizing their story construction skills through increased verbal responses.
+
+Voice output communication aids. Assistive technology includes the use of voice output com-munication aids to support the written expression of individuals with ASD. Two studies by Schlosser and colleagues combined the use of a voice output communication aid (Lightwriter 35) with a copy-cover-compare method to teach spell-ing to students with ASD (see Schlosser et al. 1998; Schlosser and Blischak 2004). In the first study, a student with ASD significantly increased correct spelling of words and maintained the improvement after the intervention was com-pleted. In the follow-up study in 2004, the inves-tigators used a similar copy-cover-compare method to teach spelling combined with the use of three different forms of feedback – speech, print, and speech + print – with four students with ASD. Results were similar to that found in the earlier study in that all of the participants increased their correct spelling. However, only two of the four students were able to generalize their skills to new words. There was no single type of feedback that was superior, but the results sug-gest that some individuals with ASD may respond better to print feedback, while others may prefer feedback in the form of speech.
+
+Video modeling. Video modeling has been found to be an evidence-based practice for improving the learning and behavior of individ-uals with ASD (Wong et al. 2015). When applied to writing instruction, video modeling has been found to enhance essay writing (Delano 2007) and spelling (Kinney et al. 2003; Kagohara et al. 2012). For example, SRSD was delivered via video self-modeling to three young adults with Asperger syndrome (Delano 2007). The young adults participated in making two videos of them-selves modeling the use of a strategy – one to increase the number of words written and the other to enhance essay writing. The students watched the video at the beginning of each inter-vention session. Following the interventions, all of the students increased the number of words used in their essay writing and increased the num-ber of functional elements (e.g., premises, rea-sons, conclusions, and elaborations) in their writing. In other studies video modeling has been used to improve spelling. Kinney et al. (2003) developed video models that showed one of the researchers writing a word in response to a prompt from an off-camera investigator. After writing the word, the first investigator said the word. These videos were subsequently used by the classroom teacher to teach new words to the student. The researchers reported that the student readily participated in the instructional sessions, learned a large number of new words, and maintained her knowledge of most of the new words over time. Kagohara et al. (2012) sought to increase the use of the spell-check function of two students with ASD through the use of video modeling. Both of the students successfully learned to use the spell-checker and continued to use the method after the video modeling interven-tion was discontinued.
+
+Future Directions
+Studies of writing interventions have clearly established that individuals with ASD can benefit from such instruction. This is true for both those individuals with lower measured cognitive func-tioning and those with IQ scores in the average to higher range. A number of different instructional practices have been found to improve specific writing skills although the most effective approaches often use a combination of methods including self-regulation, strategy instruction, graphic organizers, reinforcement, prompting, and technology. Future research should focus on which of these methods is most useful for the improvement of specific skills (e.g., spelling, written expression, planning) and for which sub-set of individuals with ASD (e.g., those with lower measured cognitive ability or those higher cognitive functioning). Additionally, the effectiveness of technology to enhance writing skills has only begun to be explored. The proliferation of technology, in particular speech recognition software and text-to-speech software, has yet to be examined as poten-tial methods for enhancing the written language skills of individuals with ASD. When combined with some of the other evidence-based practices for improving writing skills, the impact of both tech-nology and these practices can potentially be mul-tiplied. The future of research and practice in enhancing the written language skills of individ-uals with ASD is very promising. The research demonstrates that writing skills can be successfully taught and that individuals with ASD can poten-tially be more successful both in school and in post-secondary education as well as in employment.
+
+See Also
+Academic Skills
+Computer-Assisted Instruction to Teach Academic Skills
+Education
+Inclusion
+Progressive Applied Behavior Analysis
+Video Modeling/Video Self-Modeling
+
+Written Language
+
+Synonyms
+Written communication
+
+Definition
+Written language is the written form of communication which includes both reading and writing. Although written language may at first be considered to simply be oral language in its written form, the two are quite different in that oral language rules are innate whereas written language is acquired through explicit education. Written language, whether reading or writing, requires basic language abilities. These include phonological processing (understanding that words are made of discrete sounds, then associat-ing letters with these sounds, i.e., decoding), vocabulary, and syntax (grammar). Skilled read-ing and writing further require an awareness of what is being read or written in order to construct meaning. Given characteristic and varying difficulties in language in individuals with autism spectrum disorders (ASD), the bidirectional rela-tionship between oral and written language poses challenges to written language development. Further, although reading and writing are based upon the same basic language skills, they are differing forms of written communication with different levels of cognitive demand; these demands present additional challenges to those with ASD learning to read and write. Specifically, writing is more complex than reading because successful written expression requires adequate executive processes (Hooper 2009), that is, pro-cesses for planning, organizing, translating (thoughts into words), revising, and editing. Fur-thermore, writing puts greater demands on internal working memory than does reading does (Berninger and Winn 2006). Additionally, written expression requires handwriting skills, “pen to paper.”
+
+See Also
+Dysgraphia
+Reading
+Writing Disorders
+
+Xanax (Alprazolam)
+
+Synonyms
+Alprazolam; Benzodiazepine
+
+Definition
+Xanax, the brand name of Alprazolam, is a member of the benzodiazepine (BZD) family, a cate-gory of drugs commonly prescribed for conditions such as insomnia, anxiety, agitation, muscle spas-ticity, and epilepsy. Xanax is classified as a high-potency benzodiazepine and is known to have a short-lasting anxiolytic effect, meaning the drug reduces anxiety and has a half-life of approximately 6–27 h half life; it is consequently often prescribed for panic disorder and anxiety-related disorders, such as generalized anxiety dis-order (GAD) and obsessive compulsive disorder (OCD). To treat anxiety, Xanax is most commonly taken by mouth in 0.25–0.5 mg tablets up to three times per day. For panic disorder, it is recommended the drug be taken at a maximum of 6–10 mg per day.
+
+Concerning the mechanism of action, Xanax binds to GABA-A receptors, ion channels that have an affinity towards chloride. When the com-pound binds at two transected subunits of the complex, it in turn alters the shape of the complex’s chloride channels and allows GABA to bind. GABA, a common inhibitory neurotrans-mitter, is known to interfere with brain activity and the connectivity of brain structures, therapeu-tically resulting in sedation and relaxation of the body. Furthermore, when Xanax binds to subunits of the GABA-A receptor complex, specific iso-forms of those subunits regulate the effects of sedation, antianxiety, hypnosis, and amnesia. Because of its fast-acting properties, Xanax can provide more immediate relief for individuals with anxiety, compared the use of SSRIs or buspirone. Yet the administration of Xanax may also result in impairments in executive function-ing, affecting the patient’s alertness, diligence, and decision-making. Because of the drug’s high potency and high lipid solubility, the possibility of memory deficits, such as anterograde amnesia and impairments in delayed recall, are risk factors.
+
+Rebound anxiety is also a possible side effect when withdrawing from Xanax, which results in a worse return of the original symptoms. Rebound anxiety is more often to occur with a benzodiaze-pine like Xanax, because of its shorter elimination half-life compared to other BZDs, such as diaze-pam. In addition to rebound, the fast-acting, short-lived properties of Xanax also expose the patient to risks of withdrawal symptoms and dependence. In terms of Xanax and its relation to autism, anxiety-related disorders are a common comor-bidity of autism spectrum disorders (ASD). Multiple classes of drugs, such as antidepressants, stimulants, anticonvulsants, and antipsychotics are used by youth with ASD to treat comorbid or related diagnoses. Compared to the above classes, benzodiazepines appear to be less commonly used within the community. It is important to note that while benzodiazepines are sometimes taken to directly treat a related diagnosis such as anxiety disorder or epilepsy, they are not prescribed to treat the core symptoms of ASD.
+
+Additionally, there is some recent evidence that suggests that the GABA system may be involved with the pathophysiology of ASD. Several mouse models have shown a reduction in GABAergic interneurons, thus maintaining an imbalance of excitation and inhibition in the brain. This imbalance is thought to be associated with the characteristics of the autism phenotype.
+
+See Also
+Anxiolytic Drugs
+Lorazepam
+
+X-Linked Traits
+
+Definition
+The traits of an organism are its characteristics. These can be normal characteristics such as stature and eye color or abnormal or disease charac-teristics, such as blindness or the symptoms of autism. Traits are influenced by two main factors – the environment and an organism’s genetic mate-rial, the biological instructions for the develop-ment and functioning of a living organism. These instructions are contained in a molecule called deoxyribonucleic acid (DNA). The instruc-tions are spelled out in a sequence or code of four chemical units called nucleobases (or bases). This molecule is contained within nearly every cell of an organism, cells being the building blocks of organisms. Certain segments of the DNA molecule called genes contain the code for creat-ing the components of cells, most importantly, molecules called proteins.
+
+DNA is passed from one generation to the next. In humans, the DNA is not comprised of a single long molecule but rather is divided up into a set of smaller pieces that correspond to structures called chromosomes, which contain both a single long DNA molecule and proteins. By definition, cells of males contain a chromosome X and a chromo-some Y while cells of females contain two of chro-mosome X. The X and Y chromosomes are referred to as the sex chromosomes. Each parent passes one of his or her sex chromosomes to each offspring. By definition, male offspring can inherit their X chromosome only from their mothers (as the father’s Y chromosome is necessary for the creation of male offspring), while female offspring inherit one X chromosome from each parent. Sex chromosomes stand in contrast to the other human chromosomes that are called autosomes. Autosomes are paired in an individual so that each carries two of chromosome 1, for example. One member of the pair is inherited from the individ-ual’s father and the other from the mother.
+
+Traits that result from the sequence of DNA on the X chromosome are called X-linked traits. Rare variations in the DNA sequence are sometimes called mutations. When mutations in the genes on the X chromosome lead to disease, the disease itself is referred to as an X-linked disease. Color blindness is an example of such a disease. Fragile X syndrome, an intellectual disability syndrome that is associated with an increased risk for autism, is another example of an X-linked disease. In autosomes, mutations in a gene on a single chromosome of a pair can lead to a disease or contribute to disease risk. In the most dramatic case in which a single variation or mutation pre-sent on only one of the two chromosomes in a pair leads to a phenotype, this is called a dominant mutation. However, in some cases, a mutation in a gene on one chromosome of a pair will not be sufficient to result in disease because a non-mutated copy of the gene that exists on the other chromosome compensates. Only when mutations in the gene occur on both chromosomes in a pair will the disease occur. These are called recessive mutations. In autosomes, these rules by and large do not vary on the basis of the sex of an individual.
+
+X-linked mutations can be dominant and reces-sive as well. However, since not all the genes on chromosome X are represented on chromosome Y, the inheritance pattern of X-linked dominant and recessive mutations differs from that of anal-ogous mutations on autosomes. In males, an X-linked recessive disease muta-tion will result in disease because there is not an additional nonmutated X chromosome to com-pensate. In females, however, such a mutation would only result in disease if the other chromo-some X also had a disease mutation in the gene in question. If the gene on only one of a female’s X chromosomes was mutated, then the non-mutated X chromosome would compensate, and the individual female would not be affected by the disease. Such females would be called “carriers” of the disease since they carry a disease gene but do not manifest the disease. In X-linked recessive disorders, a common pattern of inheritance within families, therefore, is as follows. The disease is seen to be passed from apparently healthy mothers who are carriers to their sons but not to their daughters.
+
+In X-linked dominant diseases, a mutation on only one copy of chromosome X is sufficient to result in disease. Thus, even females with one mutated X chromosome and one nonmutated one will have the disease. The inheritance pattern commonly seen in X-linked dominant disorders depends on which par-ent carries the mutated chromosome X. If of the parents, only the father has the mutated chromo-some, he will pass the disease to all of his daughters and none of his sons since he passes only chromo-some Y to his sons. If of the parents, only the mother has the mutated chromosome, she will pass the disease in equal proportion to daughters and sons.
+
+It is important to note that this nomenclature evolved in reference to so-called Mendelian inher-itance. These are cases in which there is a highly reliable relationship between the presence of a dominant, recessive, or X-linked mutation and the presence of a disorder or disease. Autism is thought, in contrast, to be a complex genetic dis-order in which many different variations do not cause but rather contribute to the risk for the emergence of the phenotype. These variations, while not as predictive as those found in Mende-lian disorders, may also show dominant, reces-sive, or X-linked effects. As noted, there are also examples of genetic syndromes, such as Fragile X, that are associated with an increased rate of ASD. Finally, there have also been rare cases of genes contributing to “nonsyndromic” autism showing Mendelian inheritance, including X-linked inheritance.
+
+See Also
+DNA
+Genetics
+Recessive Genes
+Variable Expressivity of Genes
+
+Yale Global Tic Severity Scale
+
+Synonyms
+YGTSS
+
+Definition
+Yale Global Tic Severity Scale: The YGTSS is a commonly used measure to document the severity of motor and phonic tics. It is performed by a clinician interview. The clinician reviews a list of possible tics that may have occurred over the past week, first motor and then vocal tics. Once the tics that have occurred over the past week are established, the interviewer goes on to ask about the frequency of the tics, the intensity or forceful-ness of the tics, the complexity of the tics, and the extent to which the tics are directly interfering in the person’s daily life. These dimensions are rated for both motor and phonic tics. The combined total is often used as a measure for overall tic severity. The YGTSS also includes an overall impairment scale rated from 0 to 50. A score of 0 would indicate that the presence of Tourette syndrome has no negative impact on a person’s life. By contrast, a score of 50 would indicate marked interference and disability associated with the presence of Tourette syndrome. The over-all impairment caused by Tourette syndrome may not be completely driven by the tic severity. Some individuals with mild tics may be deeply troubled and embarrassed by the tics. By contrast, some individuals with marked tics seem to proceed with life without too much difficulty.
+
+See Also
+Tics
+Tourette’s Syndrome
+
+Yale In Vivo Pragmatic Protocol
+
+Synonyms
+YiPP
+
+Description
+The Yale in vivo Pragmatic Protocol is an assess-ment tool that measures pragmatic language through a semi-structured conversational task in verbal children aged 9–17. It contains a series of predetermined probes to collect information on a variety of conversational speech acts. Within this 30–40-min conversation, the examiner inserts 23 pragmatic probes within five conversational domains (discourse management, communicative function, conversational repair, presupposition, register variation). If the child produces a prag-matic language behavior in response to the probe, the conversation continues and the next probe is administered. If the child does not respond to the probe, the examiner systematically provides a series of prompts to determine whether or not these prompts are helpful in the production of the pragmatic behavior.
+
+Historical Background
+The YiPP was developed by Dr. Rhea Paul and piloted at the Yale Child Study Center.
+
+Psychometric Data
+This measure is not standardized but yields qual-itative information on pragmatic language skills. Norm-reference to a group of typically develop-ing age-mates is provided.
+
+Clinical Uses
+This assessment provides a naturalistic means to assess pragmatic language difficulties commonly seen in children with autism spectrum disorders. Due to the probes used in this assessment, it re-quires the child to speak in full sentences. Thus, this evaluation is best suited for higher functioning children on the autism spectrum. It yields qualita-tive information in five domains of pragmatic lan-guage. The information derived from this tool can be useful for intervention programming.
+
+See Also
+Conversational Manner
+Discourse Management
+Pragmatic Communication
+
+Yeast Infection
+
+Definition
+Various types of yeast can cause infection in dif-ferent parts of the body. The most common cause of yeast infection is Candida albicans. It can be oral thrush or with vaginitis. Males can also exhibit genital infection. At times, yeast infection can be systemic and potentially very serious (usually this occurs when the individual has some other condition that compromises the immune system, e.g., AIDS). On the skin surface or oral mucosa, yeast infection causes inflation inflammation. Yeast is diagnosed based on micro-scopic examination and search for characteristic yeast organisms. A range of antifungal drugs is used to treat yeast infection. These can be topical or systemic. In addition to the medically accepted yeast infections (noted above), others have proposed that “subclinical” yeast infection can cause a wide range of problems and can be treated by a special diet. The notion behind this diet is that yeast infection, sometimes acquired at the time of vaginal birth, causes autism. The diet consists of having children avoid food that contains yeast or fermented foods, perhaps combined with medications used in treatment of yeast infec-tions. Although dramatic claims have been made, the treatment is unproven. (Smith et al. 2014). Clearly systematic data on this intervention cannot be regarded as having a solid evidence base. Despite this many parents wish to pursue this (or other complementary/alternative) treat-ment options. Typically dietary interventions focused on yeast advise parents to avoid foods for the child that contain yeast (baked goods) or which are fermented (soy sauce) or aged (cheeses). Sugar is also to be avoided. Occasionally parents will wish to pursue long-term anti-fungal treatments – again with no clear evidence of yeast infection. As noted above, although dramatic claims have been made for these treatments, substantive scientific data are lacking, and the hypothesis of “yeast overgrowth” in children with autism has no scientific basis. Helping parents make informed treatment deci-sions is an important aspect of medical care in autism.
+
+Z Scores
+
+Synonyms
+Standard score
+
+Definition
+The Z score (or standard score) is a standardized way to convert raw scores into values expressed in terms of standard deviation units. It is calculated by taking a raw score and subtracting the mean score, then dividing the result by the standard deviation. (The mean score and standard deviation used in the calculation are specific to the popula-tion or test from which the raw score is obtained). Z ¼ rawscore   meanscore ð Þ=standarddeviation A Z score of 1.0 indicates that the raw score was 1 standard deviation above the mean. A Z score of -1.0 indicates that the raw score was 1 standard deviation below the mean.
+
+See Also
+Standard Deviation (SD)
+
+Zebrafish Models
+
+Definition
+In recent years, large-scale human genetics studies have led to considerable advances in our understanding of the biology of autism spectrum disorders (ASD). In particular, these studies have resulted in the identification of a growing list of ASD risk genes that are beginning to converge on common biological mechanisms (De Rubeis et al. 2014; Iossifov et al. 2014; Sanders et al. 2015). At the same time, scientists now face the challenge of leveraging these genetic findings to elucidate the neural circuit mecha-nisms underlying ASD and to identify novel phar-macotherapies that selectively target these mechanisms. Here, scientists have utilized model systems to advance from risk gene discovery to the elucidation of basic neurobiological mecha-nisms. These systems include mouse “knockout” models, in which the function of a particular risk gene is disrupted, as well as human induced plu-ripotent stem cells (iPSCs), which are generated from the cells of an affected individual carrying a mutation in a specific risk gene. More recently, there is growing interest in the use of zebrafish as a system for the functional analysis of risk genes in ASD. There are several unique features of the zebrafish that make it an optimal system for this purpose. First, zebrafish embryos undergo rapid external development and are optically transpar-ent, allowing for direct visualization of basic pro-cesses of vertebrate brain development at early stages. Second, zebrafish have large progenies and their larvae are small and highly tractable in the laboratory. These features facilitate their use in high-throughput pharmacological screens, which are not possible in rodents, given their greater size and complexity. Third, zebrafish provide an in vivo system for investigating the effect of risk gene disruption on the neural circuitry underlying simple, quantifiable behaviors, which is a notable limitation of in vitro methods, including human iPSCs. Finally, recent advances in technologies that allow scientists to target genes of interest in zebrafish, together with the inherent ease of genetic manipulation in this system, have contrib-uted to its growing popularity as a tool for the functional analysis of ASD risk genes. As the list of genes that are strongly associated with ASD continues to grow, zebrafish are likely to emerge as a key player in the identification of convergent biological pathways involving multiple genes and the discovery of novel pharmacological candi-dates for further investigation in ASD.
+
+Historical Background
+Until recently, the major limitation of the use of zebrafish for the functional analysis of risk genes in ASD and other neurodevelopmental disorders was the lack of efficient methods for selectively disrupting a gene of interest. While mouse “knockouts” were generated by isolating stem cells, such an approach was not feasible in zebrafish due to their rapid embryonic develop-ment. Indeed, it was only within the past 10 years that the technology for generating zebrafish lacking the function of a specific risk gene became widely available. For this reason, early studies of zebrafish primarily relied on forward genetics approaches (Granato and Nusslein-Volhard 1996), in which scientists search for an unidentified gene that when disrupted leads to an observed structural or behavioral phenotype. In particular, a large-scale screen in the mid-1990s was a tour de force that established zebrafish as a valuable system for studying basic mechanisms in genetics and developmental biology. This series of studies, in which zebrafish carrying randomly induced mutations were assessed for a range of morphological and behavioral phenotypes, led to the discovery of hundreds of genes involved in fundamental processes of vertebrate development, including axon pathfinding and locomotion (Baier et al. 1996; Granato et al. 1996; Karlstrom et al. 1996). However, the ability to target a gene associated with a human disorder in zebrafish remained limited. For example, early reverse genetics approaches in zebrafish, such as TILLING (Targeted Induced Local Lesions in Genomes), required screening thousands of zebrafish carrying chemically induced mutations to identify a deleterious mutation in a gene of interest (Moens et al. 2008). Another approach to study gene function in zebrafish that is relatively simple to perform in the laboratory is the use of morpholinos, which are sequences of nucleotides that are introduced into zebrafish embryos and transiently reduce the expression of a gene of interest during early developmental stages. While this “knockdown” approach has contributed insights into the role of genes in basic develop-mental processes, drawbacks include its potential for inducing off-target effects and non-specific phenotypes (Kok et al. 2015).
+
+For this reason, scientists have argued that it is important to exam-ine the developmental effects of gene disruption in zebrafish carrying deleterious, heritable mutations in a gene of interest in conjunction with pheno-types resulting from morpholino-induced gene “knockdown” (Kok et al. 2015). In the late 2000s, the introduction of targeted nuclease technology transformed reverse genetics studies in zebrafish, enabling researchers to selec-tively induce heritable mutations in a gene of interest (Doyon et al. 2008; Meng et al. 2008). This technology expanded the range of experi-mental possibilities in zebrafish, allowing researchers to capitalize fully on the advantages of this system for the functional analysis of genes associated with human disorders, including ASD and other neurodevelopmental disorders. Zinc fin-ger nucleases, which are chimeric fusion proteins designed to bind to a gene of interest and disrupt the gene at the target site, were the first available tools. However, this approach was cost-prohibitive and, in some cases, characterized by low efficiencies. More recently, the advent of CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 technology, which hijacks an adaptive immune mechanism used by bacteria for protection against viruses to allow scientists to target genes of interest (Jinek et al. 2012), has revolutionized gene editing in zebrafish as well as other model systems. Com-pared to earlier gene targeting methods in zebrafish, CRISPR/Cas9 offers superior flexibility and efficiency (Hwang et al. 2013). Given these advantages, along with their reasonable cost, this method has allowed scientists to harness the full potential of zebrafish for the functional analysis of ASD risk genes, which has the potential in the next several years to lead to important insights into the biology of ASD.
+
+Current Knowledge
+Despite the obvious evolutionary distance between zebrafish and humans, it is important to observe that there is a reasonable degree of con-servation between the two systems at the genetic, molecular, and pharmacological levels. First, zebrafish are vertebrates and share the same major brain subdivisions as humans, including the forebrain, midbrain, hindbrain, and spinal cord (Guo 2009). Neurotransmitter systems and neural cell types are also largely conserved. Sec-ond, there is evidence for conservation of phar-macological pathways in zebrafish and other vertebrates, based on similarities in their behav-ioral responses to psychoactive agents (Guo 2009; Rihel et al. 2010). Third, there is conservation at the genomic level, such that over 80% of genes associated with human disorders have an ortho-logous gene in zebrafish (McCammon and Sive 2015). Given the advantages of the zebrafish sys-tem, as discussed above, zebrafish represent a reasonable “balance” between experimental manipulability on the one hand and conservation on the other (McCammon and Sive 2015). Taken together, the relative conservation of zebrafish at the level of central nervous system structure, genetics, and pharmacological pathways provides support for its use as a translational tool for inves-tigating the function of genes that are strongly associated with ASD in basic neurodevelopmental processes.
+
+At the same time, there are limits to translating findings from zebrafish or any animal system to human disorders. First, there are notable anatom-ical differences between zebrafish and human brains, including the lack of a neocortex, which is present in mice and clearly limits the translation of findings from zebrafish to humans (Guo 2009; McCammon and Sive 2015). Second, there is less conservation between genes in zebrafish com-pared to mammalian systems, which may compli-cate the ability to identify the orthologs of human genes in zebrafish (McCammon and Sive 2015). Third, it is impossible for any animal system to recapitulate the complex range of clinical features in ASD or any neuropsychiatric disorder. For this reason, it is important to emphasize that the goal of studying zebrafish is to leverage the unique advantages of this relatively simple system to analyze the function of ASD risk genes in basic neurodevelopmental processes.
+
+A number of studies have capitalized on the advantages of zebrafish to investigate the role of ASD risk genes in nervous system development, with many of these studies employing a morpholino-based approach, as described above, to reduce the expression of genes of interest. For example, Kozol et al. (2015) knocked down two ASD-associated genes, SYNGAP1 (Synaptic Ras GTPase Activating Protein 1) and SHANK3 (SH3 and Multiple Ankyrin Repeat Domains 3), in zebrafish, and found that reduced expression was associated with abnormal swimming and escape behaviors, as well as developmental delay and microcephaly. In addition, Blaker-Lee et al. (2012) studied the effect in zebrafish embryos of reducing the expression of 22 genes found in the region of human 16p11.2. Copy number variants in this region are strongly asso-ciated with ASD and other neuropsychiatric dis-orders, including schizophrenia and intellectual disability, yet the contribution of individual genes in the region to ASD is poorly understood. By performing a series of morphological and behavioral assays, the authors found that the reduced expression of many of these genes led to brain and behavioral abnormalities, such as decreased brain ventricle size, abnormal midbrain structure, or alterations in spontaneous movement and touch response (Blaker-Lee et al. 2012). Another zebrafish study found that reduced expression and overexpression of one of the genes in this region led to macrocephaly and microcephaly, respectively, suggesting dose-dependent effects (Golzio et al. 2012). At the same time, it is important to observe that morpholinos are limited by their potential for inducing off-target effects, such that there is grow-ing emphasis on confirming phenotypes resulting from gene knockdown in mutants, where gene function is permanently disrupted (Kok et al. 2015). With the advent of CRISPR/Cas9 technology, this goal is becoming increasingly feasible.
+
+Recent studies have also capitalized on the large progenies of zebrafish and the small size of their embryos and larvae to develop quantitative simple behavioral assays for conducting high-throughput pharmacological screens. For exam-ple, Rihel et al. (2010) tracked the locomotor activity of zebrafish larvae automatically on a light-dark cycle in response to hundreds of psy-choactive compounds to analyze the effects of these drugs on their rest-wake cycle behaviors. Intriguingly, this study demonstrated that psycho-active compounds could be correctly classified by their mechanism of action based solely on the readout of their behavioral effects in zebrafish larvae. In addition, Kokel et al. (2010) screened thousands of compounds for their effects on the photomotor response, a characteristic motor response exhibited by zebrafish embryos follow-ing exposure to a light stimulus, and found that their behavioral responses could be used to cor-rectly categorize novel psychotropic compounds. These studies reveal the potential of large-scale screens of simple behavioral responses in zebrafish to uncover pharmacological mecha-nisms. In a recent study, Hoffman et al. (2016) utilized this automated behavioral profiling approach to analyze the locomotor activity of zebrafish carrying deleterious mutations in the ASD risk gene, CNTNAP2 (Contactin Associated Protein-like 2). This study found that these zebrafish mutants displayed deficits in inhibitory neurons, had increased sensitivity to drug-induced seizures, and were hyperactive at night. By com-paring the abnormal behavioral profile of mutants to the known effects of over 500 compounds in control fish (Rihel et al. 2010), this study found that estrogenic compounds, including the plant-derived estrogen, biochanin A, were able to selec-tively rescue the abnormal behavioral phenotype in mutants, identifying a novel pharmacological path-way not previously associated with CNTNAP2 (Hoffman et al. 2016). In another study, Baraban et al. (2013) conducted a large-scale drug screen to identify compounds that suppress spontaneous seizure-like behaviors in zebrafish carrying a loss-of-function mutation in a gene that is closely related to SCN1A (Sodium Voltage-Gated Channel Alpha Subunit 1), which is associated with a rare syndrome of intractable epilepsy called Dravet syn-drome. Of the 320 compounds tested, the authors identified a single compound, clemizole, which could reverse abnormal seizure-associated behav-iors and electrophysiological abnormalities in mutants (Baraban et al. 2013). These studies high-light the potential of the zebrafish system as a first-pass screening approach to identify novel pharma-cological candidates with relevance to ASD and other neurodevelopmental disorders for further investigation in mammalian systems.
+
+Future Directions
+There are a number of emerging technologies that are likely in the next several years to expand the repertoire of questions that scientists can address using the zebrafish system. First, genetically encoded fluorescent calcium indicators are allo-wing scientists to image the activity of individual cells throughout the brain of a live larval zebrafish at high resolution, which is possible given their transparent heads. These live imaging assays are increasingly being used to study the neural cir-cuitry underlying simple behavioral tasks, such as the optomotor response and prey capture (Filosa et al. 2016; Muto et al. 2013; Portugues et al. 2015). By visualizing brain activity in zebrafish lacking the function of ASD risk genes, scientists can begin to explore how the disruption of these genes leads to abnormalities in specific neural circuits. Second, the advent of CRISPR/Cas9 gene-editing technologies is facili-tating the targeting of multiple ASD risk genes in zebrafish, providing scientists with the opportu-nity to leverage this approach to investigate the effect of multiple ASD risk genes on brain devel-opment simultaneously. Third, high-throughput, automated pharmacological screens based on sim-ple larval behaviors, as described above, have the potential to serve as an important first-pass screen-ing tool to uncover neurochemical pathways that are disrupted due to loss of ASD risk genes and novel pharmacological candidates for further evaluation in mammalian systems. Therefore, the zebrafish system holds considerable translational promise, in conjunction with mouse and cell culture models, to advance our understanding of neurobiological mechanisms in ASD. Taken together, given the unique features of this system, the zebrafish has the potential to emerge as a key contributor to the functional analysis of ASD risk genes and the elucidation of convergent biological and pharmacological mechanisms in ASD.
+
+See Also
+Animal Models
+Candidate Genes in Autism
+Genetics
+Neuroscience
+Recessive Genes
+
+Zero Reject
+
+Definition
+The term zero reject refers to the requirement that an individual with a disability recognized by the Individuals with Disabilities Education Act (IDEA) cannot be denied access to special educa-tion and necessary related services in the United States. This explicitly implies that a local educa-tion agency (LEA) must make an affirmative effort to determine eligibility for special education services, develop a free and appropriate individu-alized educational plan, implement that plan in the least restrictive environment, and demonstrate that the plan is effectively addressing the educa-tional needs of a student with a disability. The LEA does not have the right to refuse (reject) determination of need, or provision of services, under federal law.
+
+See Also
+Individuals with Disabilities Education Act (IDEA)
+
+Ziprasidone
+
+Synonyms
+Geodon
+
+Definition
+Ziprasidone is atypical antipsychotic medication that has been on the marketplace since early in the 2000s. It is distinguished from others of the class in that it appears to be less likely to cause weight gain. There are a few studies that have examined the benefits of ziprasidone in children or adults with autism, so information on dose and effective-ness is incomplete. Although rare, a drawback of ziprasidone is that it has a potential for affecting electrical signals in the heart. Most clinicians do not consider it as a first-line treatment because of this concern.
+
+See Also
+Antipsychotics: Drugs
+
+Zolpidem
+
+Synonyms
+Ambien; Gamma-aminobutyric agonist
+
+Definition
+Despite its high prevalence, few pharmacological treatments outside antihistamines and melatonin exist for sleep disorders among pediatric populations (Chevreuil et al. 2010). Few clinical trials have examined whether benzodiazepines, such as Zolpidem, may benefit autistic spectrum disorder (ASD) children with sleep disorders (Chevreuil et al. 2010). A recent case report sug-gests Zolpidem was effective in reducing cata-tonic behavior in an adolescent with ASD (Zaw and Bates 1997). However, murine models sug-gest zolpidem exacerbates social and cognitive deficits observed in ASD children (Banerjee et al. 2012; Han et al. 2014). Further clinical trials are required to elucidate the mechanism, thera-peutic use, and side effects of Zolpidem among ASD children with sleep disorders.
+
+See Also
+Gabapentin
+Midazolam
+Zyprexa
+
+Zone of Proximal Development (ZPD)
+
+Definition
+A concept developed by the Russian psycholo-gist Lev Vygotsky (1896–1934) to describe the point where a child (or adult) is most able to learn, i.e., just past the point at which a skill/ task/activity has been mastered but not so diffi-cult as to be impossible for the child to learn. From the educator’s point of view, this suggests giving the child materials/tasks that are carefully selected to enable the child to advance without overwhelming them. Put another way, this notion refers to tasks that the child is capable of learning and performing (often initially with guidance) before these become fully indepen-dent activities. High interest in the topic was based on his awareness that for most children certain activities (e.g., language learning) were more effortless than work in other areas (e.g., mathematics). His work also reflected an aware-ness of distinguishing between development and teaching and has implications for theories like those of Jean Piaget on cognitive development. In Vygotsky’s view, a middle ground was needed between self-directed learning and explicit educational instruction. For him a good teacher was one who directed teaching at the child’s specific level of ability. His death at a young age meant that he did not fully develop the concept.
+
+The concept of the zone of proximal devel-opment prefigures in important ways certain current approaches to treatment and intervention (e.g., in learning difficulties). Other approaches, e.g., Maria Montessori’s teaching methods, sim-ilarly attempt to match activities with skills that are emerging for the individual child. Other theorists have referred to these concepts in slightly different ways, e.g., J. McV. Hunt (1961) referred to the “problem of the match,” that is of being aware of exactly where the child was developmentally and providing a slightly more challenging task. The notion has also been viewed as an important aspect of scaffold-ing helping the child learn from peers as well as teachers. In this process, the teacher or a knowl-edgeable peer help a student who is just at his or her zone of proximal development to learning a more challenging task. Clearly an awareness of where the child is actually at is critical in this regard. The concept has been extensively used in educational settings. It has implications not only for teacher-directed learning but the assis-tance of peers as well.
+
+The social context of children’s learning is important given the usual indication of the child to learn by imitation, and this imitation and ability to learn socially provides an important tool for learning. Thus by teaching children at a level slightly above current skill levels, parents and teachers help them expand their abilities. The concept applies not only to more straight-forward school-related tasks but other complex activities, e.g., riding a bicycle, learning to drive a car. It is important for parents and teachers to avoid teaching outside the zone of proximal development; conversely, it is also important that parents/teachers are able to sensibly with-draw scaffolding when a child has mastered, or is well on his or her way to mastering, an activ-ity. The use of older peers/mentors can be important in helping the child learning a slightly more challenging task. For individuals with autism spectrum disor-ders, an awareness of the child’s levels of func-tioning in specific areas is critical given that some skills (e.g., nonverbal tasks like puzzles) are learned with greater ease than other (e.g., language-based) tasks. As with typically devel-oping children, teachers and peers can be helpful in facilitating learning in the child with ASD as tasks are “pitched” superficially at an appropri-ate level and within the ability of the child with ASD to learn.
+
+See Also
+Education
+Social Cognition
+
+Zygosity
+
+Synonyms
+Allele similarity
+
+Definition
+It describes DNA sequence similarity or origin at a particular genetic locus or between twins. In dip-loid organisms, where one allele is usually inherited from the mother and one from the father, zygosity describes whether these homologous alleles have identical (homozygous) or different (heterozygous) DNA sequence. A diploid individ-ual may also be missing one (hemizygous) or both (nullizygous) alleles. Alleles are autozygous if they originate from a common ancestor via nonrandom mating or inbreeding; autozygous alleles are homo-zygous. Alternatively, alleles that coincide via ran-dom mating are allozygous and may be homozygous or heterozygous. Twins are described as monozygotic (MZ) when a single egg is fertilized to form one zygote, which later divides into two embryos. Dizygotic (DZ) twins occur when two eggs are fertilized independently, yielding two zygotes; on average, they share 50% of their DNA sequence, similar to nontwin siblings. Com-parisons of concordance (shared diagnosis) rates between MZ and DZ twin pairs are used to estab-lish the overall heritability of conditions or pheno-types in “twin studies.” Multiple twin studies have been conducted in autism spectrum disorders and demonstrate a significant role for genetic factors in the etiology of these syndromes.
+
+See Also
+Dizygotic (DZ) Twins
+Monozygotic (MZ) Twins
+
+Zyprexa
+
+Synonyms
+Olanzapine
+
+Definition
+Despite advancements in cognitive-behavior ther-apies, no single pharmacological therapy has suc-cessfully treated the disruptive-compulsive behaviors of Autism Spectrum Disorders (ASD), which include hand-flapping, tantrums, and self-injurious behaviors (Fido and Al-Saad 2008). Among the pharmacological agents examined to reduce disruptive-compulsive behaviors, atypical antipsychotics, including olanzapine, showed the most promise (Fido and Al-Saad 2008; Malek-Ahmadi and Simonds 1998; Malone et al. 2001). Specifically, ASD children prescribed olanzapine showed significant reductions in irritability, hyperactivity, and lethargy (Fido and Al-Saad 2008; Malek-Ahmadi and Simonds 1998; Malone et al. 2001). Unlike typical antipsychotic medica-tions, atypical antipsychotic medications block postsynaptic dopamine and serotonin receptors and have a lower risk of extrapyramidal symp-toms, such as dyskinesias, among children and adolescents (Malone et al. 2001). However, patients prescribed olanzapine experience several side-effects including: hyperlipidemia, hyper-prolactinemia, weight gain, and tardive dyskine-sia (Fido and Al-Saad 2008). Other case reports have also reported hyponatremia and excessive masturbation from prolonged use of olanzapine among ASD patients (Chiang et al. 2013; Hergüner 2010). In addition, it remains uncertain whether long-term use of olanzapine produces detrimental effect to learning and development of ASD children (Fido and Al-Saad 2008). Fur-ther studies of olanzapine are required to determine its long-term side-effects and potential applications for treating symptoms of ASD.
+
+See Also
+Midazolam
+Neurontin (Gabapentine)

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+of obsessions, experimental conditions may be difficult to design and repeatedly implement. However, future research may examine brief functional analyses and the use of physiological measures such as heart rate monitoring (Chok and Koesler 2014). Although studies report promising results, additional randomized controlled trials are sorely needed to firmly establish the efficacy of adapted CBTalone as well as the additive value of combining CBT with ABA-based procedures including functional behavior assessment for children and youth. Addressing all maintaining variables in a treatment package derived from function-based assessment may quicken treatment response for OCBs. Typically, CBT studies included high-functioning individuals, whereas ABA studies included children and youth with ASD and an ID. Future research is needed with children and youth who present with a wide range of cognitive and adaptive levels to evaluate ideal treatment modalities, including psychosocial and pharmacological treatment. Research is needed to tease out key treatment components that are evidence-based for varying populations. Studies involving component analyses may aid in determining active ingredients for a range of ASD presentations, including children with ASD alone as well as those presenting with comorbid conditions such as attention deficit hyperactivity disorder (ADHD), comorbid anxiety, and varying neurodevelopmental disorders (DSM-5, APA 2013; Postorino et al. 2017). Also, examination of sociocultural variables (e.g., socioeconomic status, cultural values) is crucial in designing treatment protocols that are person- and family-centered to meet the needs of youth and their families. Evaluation of CBT and combined treatments is also needed across varying age ranges. For example, only a handful of case studies and one RCT (Russell et al. 2013) have evaluated CBT to treat OCBs in older adolescent and adult populations with ASD. In this study, 46 adolescents and adults with a mean age of 27 years were randomized to CBT versus an anxiety management condition, with both groups showing significant reductions in OCBs. Extension is also needed to younger populations, with an emphasis on early intervention for children of preschool age presenting with ASD and other related disabilities (Guertin et al. 2019). To date, published research shows promise, but as suggested above, additional research evaluating singular and dual treatment modalities including Fb-CBT, with consideration of variables including level of cognitive functioning, comorbid presentations, cultural factors, and various age ranges, is needed. A focus of future studies should be on the establishment of a strong evidence base for treating obsessive-compulsive behavior with manualized packages such as Fb-CBT that are tailored to the needs of individuals across the ASD spectrum.
+
+See Also
+▶Anxiety
+▶Autism Spectrum Addendum to the Anxiety Disorders Interview Schedule-Parent Interview
+▶Applied Behavior Analysis (ABA)
+▶Cognitive Behavioral Therapy (CBT)
+▶DSM-5 and Autism Spectrum Disorder
+▶Functional Analysis
+▶Functional Behavior Assessment
+▶Habit Reversal
+▶Hoarding in Youth with Autism Spectrum Disorders
+▶Mental Health and ASD
+▶Obsessive-Compulsive Disorder (OCD)
+▶Repetitive Behavior
+▶Restricted Interest
+
+American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.
+Bodfish, J. W., Symons, F. J., & Lewis, M. H. (1999). The repetitive behavior scale: Test manual. Morganton: Western Carolina Center Research Reports.
+Boyd, B. A., McDonough, S. G., & Bodfish, J. W. (2012). Evidence-based behavioral interventions for repetitive behaviors in autism. Journal of Autism and Developmental Disorders, 42(6), 1236–1248. https://doi.org/ 10.1007/s10803-011-1284-z.
+Boyd, B. A., Woodard, C. R., & Bodfish, J. W. (2013). Feasibility of exposure response prevention to treat repetitive behaviors of children with autism and an intellectual disability: A brief report. Autism, 17(2), 196–204. https://doi.org/10.1177/1362361311414066.
+Chambless, D. L., & Hollon, S. D. (1998). Defining empirically supported therapies. Journal of Consulting and Clinical Psychology, 66(1), 7–18. https://doi.org/10.1 037/0022-006X.66.1.7.
+Chok, J. T., & Harper, J. M. (2016). Heart rate assessment and use of a multiple schedule treatment for an individual with obsessive compulsive-like behavior. Journal of Developmental and Physical Disabilities, 28(6), 821–834. https://doi.org/10.1007/s10882-016-9511-3.
+Chok, J. T., & Koesler, B. (2014). Distinguishing obsessive-compulsive behavior from stereotypy. Behavior Modification, 38(3), 344–373. https://doi. org/10.1177/0145445513509475.
+Cooper, J. O., Heron, T. E., & Heward, W. L. (2019). Applied behavior analysis (3rd ed.). Hoboken: Pearson Education.
+Elliott, S. J., & Fitzsimons, L. (2014). Modified CBT for treatment of OCD in a 7-year-old boy with ASD: A case report. Journal of Child and Adolescent Psychiatric Nursing, 27(3), 156–159. https://doi.org/10.1111/ jcap.12081.
+Farrell, L., Waters, A., Milliner, E., & Ollendick, T. (2012). Comorbidity and treatment response in pediatric obsessive-compulsive disorder: A pilot study of group cognitive-behavioral treatment. Psychiatry Research, 199(2), 115–123. https://doi.org/10.1016/j. psychres.2012.04.035.
+Goodman, W. K., Price, L. H., Rasmussen, S. A., Riddle, M. A., & Rapoport, J. L. (1986). Children’s Yale-Brown obsessive compulsive scale (CY-BOCS). Bethesda: National Institute of Mental Health.
+Guertin, E. L., Vause, T., Jaksic, H., Frijters, J. C., & Feldman, M. (2019). Treating obsessive compulsive behavior and enhancing peer engagement in a preschooler with intellectual disability. Behavioral Interventions, 34(1), 19–29. https://doi.org/10.1002/ bin.1646.
+Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003). Functional analysis of problem behavior: A review. Journal of Applied Behavior Analysis, 36(2), 147–185. https://doi.org/10.1901/jaba.2003.36-147.
+Kendall, P. C., & Choudhury, M. S. (2003). Children and adolescents in cognitive-behavioral therapy: Some past efforts and current advances, and the challenges in our future. Cognitive Therapy and Research, 27, 89. https://doi.org/10.1023/A:1022542814822.
+Kose, L. K., Fox, L., & Storch, E. A. (2018). Effectiveness of cognitive behavioral therapy for individuals with autism spectrum disorders and comorbid obsessive-compulsive disorder: A review of the research. Journal of Developmental and Physical Disabilities, 30(1), 69–87. https://doi.org/10.1007/s10882-017-9559-8.
+Kuhn, D. E., Hardesty, S. L., & Sweeney, N. M. (2009). Assessment and treatment of excessive straightening and destructive behavior in an adolescent diagnosed with autism. Journal of Applied Behavior Analysis, 42(2), 355–360. https://doi.org/10.1901/jaba.2009.42- 355.
+Lehmkuhl, H. D., Storch, E. A., Bodfish, J. W., & Geffken, G. R. (2008). Brief report: Exposure and response prevention for obsessive compulsive disorder in a 12-year-old with autism. Journal of Autism and Developmental Disorders, 38(5), 977–9981. https://doi.org/ 10.1007/s10803-007-0457-2.
+March, J. S., & Mulle, K. (1998). OCD in children and adolescents: A cognitive-Behavioral treatment manual. New York: The Guilford Press.
+Martin, G., & Pear, J. J. (2019). Behavior modification: What it is and how to do it (11th ed.). New York: Routledge.
+Matson, J. L., & Vollmer, T. R. (1995). User’s guide: Questions about Behavioral function (QABF). Baton Rouge: Scientific Publishers.
+Matson, J. L., & Williams, L. W. (2014). Functional assessment of challenging behavior. Current Developmental Disorders Reports, 1(2), 58–66. https://doi.org/10.10 07/s40474-013-0006-y.
+Matson, J. L., Tureck, K., & Rieske, R. (2012). The questions about Behavioral function (QABF): Current status as a method of functional assessment. Research in Developmental Disabilities, 33(2), 630–634. https://doi.org/10.1016/j.ridd.2011.11.006.
+Mirenda, P., Smith, I. M., Vaillancourt, T., Georgiades, S., Duku, E., Szatmari, P., . . . Zwaigenbaum, L. (2010). Validating the repetitive behavior scale-revised in young children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 40(12), 1521–1530. https://doi.org/10.1007/s10803- 010-1012-0.
+Mulligan, S., Healy, O., Lydon, S., Moran, L., & Foody, C. (2014). An analysis of treatment efficacy for stereotyped and repetitive behaviors in autism. Review Journal of Autism and Developmental Disorders, 1(2), 143–164. https://doi.org/10.1007/s40489- 014-0015-8.
+Murray, K., Jassi, A., Mataix-Cols, D., Barrow, F., & Krebs, G. (2015). Outcomes of cognitive behaviour therapy for obsessive-compulsive disorder in young people with and without autism spectrum disorders: A case controlled study. Psychiatry Research, 228(1), 8–13. https://doi.org/10.1016/j.psychres.2015.03.012.
+Neil, N., & Sturmey, P. (2014). Assessment and treatment of obsessions and compulsions in individuals with autism spectrum disorders: A systematic review. Review Journal of Autism and Developmental Disorders, 1(1), 62–79. https://doi.org/10.1007/s40489-013- 0006-1.
+Neil, N., Vause, T., Jaksic, H., & Feldman, M. (2017). Effects of group functional behavior-based cognitive-behavioral therapy for obsessive-compulsive behavior in a youth with autism spectrum disorder. Child & Family Behavior Therapy, 39(3), 179–190. https://doi.org/10.1080/07317107.2017.1338448.
+Piacentini, J., Langley, A., & Roblek, T. (2007a). Cognitive-behavioral treatment of childhood OCD: Its only a false alarm, therapist guide. Programs that work. New York: Oxford University Press.
+Piacentini, J., Peris, T. S., Bergman, R. L., Chang, S., & Jaffer, M. (2007b). BRIEF REPORT: Functional impairment in childhood OCD: Development and psychometrics properties of the child obsessive- compulsive impact scale-revised (COIS-R). Journal of Clinical Child & Adolescent Psychology, 36(4), 645–653. https://doi.org/10.1080/15374410701662790.
+Postorino, V., Kerns, C. M., Vivanti, G., Bradshaw, J., Siracusano, M., & Mazzone, L. (2017). Anxiety disorders and obsessive-compulsive disorder in individuals with autism spectrum disorder. Current Psychiatry Reports, 19(12), 92. https://doi.org/10.1007/s11920- 017-0846-y.
+Reaven, J., & Hepburn, S. (2003). Cognitive-behavioral treatment of obsessive- compulsive disorder in a child with Asperger syndrome. Autism, 7(2), 145–164. https://doi.org/10.1177/1362361303007002003.
+Rispoli, M., Camargo, S., Machalicek, W., Lang, R., & Sigafoos,

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+home, schools, and communities will exert preventive effect on bullying not only for children with ASD but for all children receiving such interventions (Olweus 1994; Olweus and Limber 2010; Vreeman and Carroll 2007). Understanding the relationships between ASD and bullying has been limited due to the shortcomings of previous studies, including small sample size, limited sampling methods, and/or inadequate measurement of bullying (Mandell et al. 2005). Future study should focus on incidence/prevalence of bullying, the impact of bullying experiences on the natural course of ASD, associations between bullying experiences and other comorbid psychopathology, and development and assessment of intervention programs in larger population-based samples of children and adolescents with ASD.
+
+Conclusion
+Bullying is common among all children, but the children with ASD are at even greater risk of this harmful experience. And, just as is the case for typically developing children, reduction of bullying enhances developmental prospects for all children, including those with ASD. While ASD may not be preventable at this time, we can reduce or even prevent bullying experiences for children with ASD, as we can and must for all children.
+
+References and Reading
+
+Bupropion
+Lawrence David Scahill
+Nursing and Child Psychiatry, Yale Child Study
+Center, Yale University School of Nursing, New
+Haven, CT, USA
+Marcus Autism Center, Children’s Healthcare of
+Atlanta, Atlanta, GA, USA
+Department of Pediatrics, Emory University,
+Atlanta, GA, USA
+
+Synonyms
+Wellbutrin; Zyban
+
+Definition
+Bupropion is a novel antidepressant that is believed to block reuptake of dopamine. It is indicated for the treatment of depression (Wellbutrin) and for smoking cessation (Zyban). It has also been evaluated as a treatment for attention deficit/hyperactivity disorder in children and adults. It has not been evaluated systematically in children or adults with autism.
+
+See Also
+
+References and Reading
+
+Buspirone
+Lawrence David Scahill
+Nursing and Child Psychiatry, Yale Child Study
+Center, Yale University School of Nursing, New
+Haven, CT, USA
+Marcus Autism Center, Children’s Healthcare of
+Atlanta, Atlanta, GA, USA
+Department of Pediatrics, Emory University,
+Atlanta, GA, USA
+
+Synonyms
+Buspar; Vanspar
+
+Definition
+Buspirone is an antianxiety medication that is an agonist for serotonin 1A receptor. Unlike benzodiazepines, buspirone does not directly affect a GABA system and is not habit-forming. There is limited information on the use of buspirone in children and only one trial in adolescents with pervasive developmental disorders. In that study, buspirone appeared to be only modestly beneficial for disruptive and agitated behavior.
+
+See Also
+
+References and Reading
+
+California Verbal Learning Test, Children’s Version
+(CVLT-C)
+Beau Reilly and Raphael Bernier
+Psychiatry and Behavioral Sciences, University of
+Washington, Seattle, WA, USA
+
+Synonyms
+CVLT – Children’s Version; CVLTC; CVLT-C
+
+Description
+The California Verbal Learning Test-Children’s Version (CVLT-C; Delis et al. 1994) is an examination of auditory and verbal learning for children between the ages of 5 years and 16 years 11 months. The test makes use of familiar visual categories to generate a measure of short- and long-term memory performance. Encoding and recall are examined via the use of single words verbally presented in the context of “a shopping list” over the course of eight total trials spanning 15–20 min with an additional 20-min period to accommodate delayed recall testing. During the first five trials of CVLT-C, a list of 15 items consisting of three semantic categories (fruit, clothing, and toys), labeled “list A” or “the Monday list,” is read aloud to the child and he or she is asked to recall as many items as possible following each presentation. During the sixth trial, a second 15-item list containing new words that belong to one of the categories from the original list (fruits) as well as words from two new categories with semantic similarities (furniture and sweets) from the original list are presented as “list B” or “the Tuesday list” to the child as an interference task. The child is then asked to recall as many words as possible. After completion of the list B trial, the child is then asked to recall words from list A without presentation of the items. In the seventh trial, list categories are used as cues to elicit recall from the original list via prompts from the examiner such as “Tell me all the things to wear in the Monday list.” Following trial 7 is a 20-min break from the task during which time the child can complete nonverbal tasks or participate in other activities that provide moderate distraction. Following this “long-delay” interval, the child is asked to recall as many words as he or she can from list A (long-delay free-recall trial), asked to recall words from list A after being provided with the categorical cues (long-delay cued-recall trial), and finally read a 45-item list aloud and asked to indicate whether or not each word was on list A (yes/no recognition trial). Responses are recorded and documented by the examiner during every trial.
+
+A complete administration of the CVLT-C produces data on eight recall measures, eight learning characteristics, four areas of recall errors, four recognition measures, and five contrast measures. This includes information concerning encoding strategies for success over time as well as the characteristics of errors that occur. In addition to generating information on the quantity of items accurately recalled after each of the eight testing trials, the CVLT-C allows for the detailed examination of characteristics related to acquisition methods utilized during the learning process. Characteristics related to the learning process are examined through the use of learning strategy variables and contrast variables. The learning strategy variables aid in outlining the characteristics of acquisition and encoding that progress throughout the course of the examination. They include semantic clustering (i.e., consecutive words from the same category), serial clustering (i.e., words recalled in the same order in which they were presented), primacy recall (i.e., percentage of words recalled from the first five items of the list), middle recall (i.e., percentage of words recalled from the middle five items of the list), recency recall (i.e., percentage of words recalled from the last five items of the list), learning slope (i.e., the average number of new words recalled per learning trial), consistency (i.e., percentage of words recalled once that were also recalled on the following trial), recognition hits (i.e., number of words correctly identified as belonging to list A during the recognition trial), and discriminability (i.e., accuracy of distinguishing target words in list A from distraction words in list B). Characteristics of errors are also calculated with regard to perseveration (i.e., words repeated in a trial), free intrusions (i.e., extra-list intrusions on all free-recall trials), cued intrusions (i.e., extra-list intrusions on the cued-recall trials), total intrusions (i.e., extra-list intrusions on all trials), false-positives (i.e., words incorrectly identified as list A items during the recognition trials), and response bias (i.e., the tendency to identify words as belonging on the target list during recognition trials). The learning slope variable, in particular, allows for a thorough examination of specific learning characteristics that may be evident across differing presentations of clinical populations. Deficits in areas related to learning (i.e., flat learning slope across trials with low amounts of new words learned), encoding (e.g., poor trial 1 performance followed by a normative learning slope), or sustaining focus (i.e., normative recall on initial trials with poor recall on later trials) can be identified with the learning slope, allowing for the closer inspection of learning characteristics and discrimination of other possible domains of learning that may be affected (Spreen and Strauss 1998). Children with Down syndrome, attention deficit hyperactivity disorder (ADHD), and other disorders have demonstrated distinct and differentiated characteristics of learning slope in clinical populations (Delis et al. 1994). The CVLT-C contrast variables (Donders 1999) aid in the identification of trial discrepancies and learning differences that occur throughout the learning process. These include aspects of encoding related to proactive interference (i.e., the contrast between list B recall and list A trial 1 recall), retroactive interference (i.e., the contrast between list A short-delay free recall and list A trial 5), rapid forgetting (i.e., contrast between list A long-delay free recall and list A short-delay recall), and retrieval problems (i.e., contrast between discrimination trial and list A long-delay free recall).
+
+Historical Background
+Delis et al. (2000) observed that while there are a variety of verbal learning instruments that measure the amount of material that is recalled, far fewer examine the processes by which the information is learned and retrieved. Construction of CVLT-C in 1994 followed the same process-oriented approach of the original California Verbal Learning Test (CVLT) for adults (Delis et al. 1987). For construction of the task, selection of the target words themselves was chosen based on their frequency of occurrence in the English language as well as the frequency of reported words by children in the sample. The three most common words in each semantic category were removed to avoid recall confounds associated with item familiarity (Miller et al. 2003). The context of a shopping list was selected for its consistent familiarity with children across a wide range of cultural and demographic variables and mapped closely with the CVLT with regard to presentation, timing, and scoring.
+
+Psychometric Data
+The normative sample for the CVLT-C consists of stratified data taken from the 1988 US census findings and is comprised of 920 children in 12 age ranges from 5 years to 16 years 11 months. Standardized scores were derived from accumulative raw score performance per age group, distribution normalization, and elimination of outliers and skewing effects. The remaining learning score components of the CVLT-C were developed via regression analyses (Delis et al. 1987). Investigations of test-retest reliability among 106 school-age children ranged from .17 (cued-recall intrusions, for 12-year-olds) to.90 (perseverations, for 8-year-olds) (Delis et al. 1987, 1994; Spreen and Strauss 1998). Alternate forms reliability was reported at .84 (Delis et al. 1987, 1994; Spreen and Strauss 1998), indicating appropriate reliability for multiple administrations with children and tracking results and learning characteristics over time. Gender effects were reported by the authors to be minimal in the initial standardization sample, and significant differences were not found for gender in the 4-year-old sample norms provided by Goodman, Delis, and Mattson (Goodman et al. 1999) for normative populations. However, differences in gender have been reported in followup examinations of the standardization sample (Kramer et al. 1997) and have been evidenced in clinical populations of children with ADHD (Cutting et al. 2003) and significant head injury (Warschausky et al. 2005). Gender effects were also evident in examinations of adolescent populations, with girls outperforming boys (Beebe et al. 2000). Low correlations with measures of executive functioning and moderate associations with intelligence measures such as the Wechsler block design and vocabulary subtests have been reported (Beebe et al. 2000). Donders (1999) also identified a significant link between parental education levels and test performance in the standardized sample, with children of parents with higher education consisting of 22% of the highest performing children and children of parents with lower rates of education accounting for 30% of the children in the below-average range of performance.
+
+Predictably, age effects were observed among the standardization sample, with steeper learning slopes being present in children as age increased and development progressed. Consistency of recalled items and immediate recall scores were also observed to have developmental trends across the sample. The use of semantic clustering strategy as a learning strategy was first emergent among 9–12-year-old participants. Adolescents in the sample exhibited higher degrees of serial clustering strategy use compared to other age groups (Delis et al. 1994). Investigations of executive functioning and CVLT-C process scores further indicate that perseverative errors evidence strong consistency throughout development with minimal improvement, while rates of intrusions and false-positives exhibit considerable improvement as development progresses into adolescence (Beebe et al. 2002; Delis et al. 1994). Donders (1999) provided maximum likelihood confirmatory factor analysis on 13 qualitative and quantitative variables from the original standardized sample to identify the most salient factors of learning and memory tapped by the CVLT-C. A five-factor model consisting of attention span, learning efficiency, free delayed recall, cued delayed recall, and inaccurate recall showed the greatest fit and was proposed to be a valid and clinically useful predictor of performance on the measure. The CVLT-C has been co-normed with the children’s category test (CCT; Boll 1993), allowing examiners to compare a child’s memory and learning performance with other forms of higher order cognitive functioning. Combining the results of both tasks to generate the learning profile of a child can be clinically valuable as the CCT provides explicit feedback on a nonverbal task, while the CVLT-C provides non-explicit feedback on a verbal task through repetition. By taking advantage of the co-normed scores, clinicians are able to tap a wider range of learning areas and skills for characterizing the cognitive capabilities of the child. Donders (1999) examined the psychometric comparisons of the two measures including the magnitude of difference necessary for statistical significance in scores. Standardized sample data from both measures were used to evaluate covariances and statistically significant discrepancies between the T scores of those instruments as well as the base rate of specific discrepancies among 920 children ranging in age from 5 to 16 years. Results suggested that the CCT and CVLT-C share a small degree of common variance. Statistically significant score discrepancies between the two measures (T-score difference greater than 18 among 5–8-year-olds and greater than 16 among 9–16-year-olds) were common, indicating that evaluation of the potential clinical significance of a discrepancy between the obtained results should also include consideration of base rate statistics when evaluating individual children.
+
+While the standardization sample focused on children ages 5 years through 16 years 11 months, Goodman et al. (1999) provided normative data for 4-year-old participants on the CVLT-C for potential administration with younger populations to aid in early identification and intervention. Each month of the 4-year-old range was represented among the stratified sample of 80 (40 males and 40 females). Performance characteristics of the younger population were considerably similar to that of the normative sample data, apart from a few learning characteristics. The 4-year-old participants had a tendency for higher extra-list intrusions relative to their correct responses on cued recall that were not present on free recall as well as a higher endorsement of distracter items during the recognition trial. Semantic and serial clustering characteristics were also consistent with developmental trends, providing evidence for utility in identifying early memory and learning characteristics with the younger population.
+
+Clinical Uses
+The CVLT-C has been used to assess memory and learning in a wide variety of clinical childhood populations and has been used to examine verbal learning in children with ASD. Early studies of memory and list learning among children with ASD highlighted specific deficits in recall compared to control groups. Boucher and Warrington (1976) used memory tests that employed pictures, lists, and spoken words with 29 children with ASD and compared recall scores against age-matched controls. During trials of forced-choice recall, children with autism showed significantly lower rates of recall than controls but demonstrated considerable improvement when provided with semantic descriptive cues of list items and pictures. Initial investigations of verbal recall among children with autism spectrum disorder (ASD) utilizing the CVLT also suggested distinct differences in learning and memory profiles when compared to typically developing peers. Minshew and Goldstein (1993) compared the performance of high-functioning children and adults with ASD ranging in age from 12 to 40 years old to age-matched normal controls using the CVLT. The comparison group significantly outperformed the ASD group. Specific scores indicated that while individuals with ASD showed comparable recall and recognition scores when presented with list A of CVLT, they showed significantly more intrusion errors on both list A and list B items and considerably lower recall scores on list B. The authors concluded that the overall characteristics of the ASD scores were indicative of a “subtle inefficiency of verbal memory” that was more suggestive of deficits in mechanisms for effectively organizing information than a reflection of comprehensive memory impairment. More recent investigations into learning strategies and encoding profiles of children with ASD lend support for this theory and suggest that the CVLT-C may be effective in highlighting specific characteristics of verbal learning in children with ASD that differ from those of typical developing peers. Phelan, Filliter, and Johnson (Phelan et al. 2010) compared performance and verbal learning characteristics on the CVLT-C between 15 high-functioning children with ASD and typical developing controls. Although the learning profiles and performance characteristics of both groups were comparable, children with ASD demonstrated considerable improvement in their cued-recall scores compared to their free-recall scores, suggesting the need for external supports and cueing opportunities to facilitate verbal memory performance among ASD youth.
+
+Key clinical strengths of the CVLT-C include its relative ease of use and excellent internal consistency. Considerable research and psychometric data have been gathered with CVLT-C, and it has proven useful in predicting a variety of difficulties and deficits that can inform decision making concerning placements in groups such as head trauma patients and other neurodevelopmental disorders (Nagel et al. 2006; Nichols et al. 2004). As previously noted, the test provides a considerable amount of information about the verbal learning process and learning strategies across a relatively short period of time in such a way that recall and cueing effects can be examined efficiently and reliably. Scores on the CVLT-C have been shown to account for a considerable amount of the variance in the prediction of special education services and long-term educational outcome among children with severe head injury that could translate to other clinical populations (Miller and Donders 2003). The CVLT-C’s implementation across a wide range of childhood populations illustrates its breadth in utility and efficiency across several domains of care. The provision of normative data for 4-year-olds additionally provides valuable opportunities for early screening, intervention, and tracking among children early in development. While the internal consistency of the test has been thoroughly investigated and validated, stability coefficients of many of the variables examined in the CVLT-C fall below acceptable standards, cautioning against the use of single variables as valid examination of cognitive factors (Spreen and Strauss 1998). Overall, the test has shown to be an efficient and informative instrument of memory and verbal learning among children that serves as a valuable asset to clinicians involved in diagnostic assessment, treatment planning, service enrollment, and needs assessment.
+
+References and Reading
+
+Cambridge Neuropsychological Test
+Automated Battery
+Aditya Sharma
+Academic Child and Adolescent Mental Health,
+Sir James Spence Institute Newcastle University,
+Newcastle upon Tyne, UK
+
+Synonyms
+CANTAB
+
+Description
+The CANTAB® tests are simple, computerised, non-linguistic, and culturally blind. They can be administered by a trained assistant. Importantly, interpretation of a patient’s condition can be easily understood by a clinician. Below is a complete list of all tests, correct at time of publication. The tests are categorised as assessing the following cognitive domains:
+1. Induction
+2. Visual Memory
+3. Executive function
+4. Attention
+5. Verbal/Semantic Memory
+6. Decision Making and Response Control
+7. Social Cognition
+8. Other tests
+
+CANTAB – Induction
+These very short tests can be used to familiarize participants with the general idea of responding in a task by touching the screen. They can also be regarded as warm-up tasks, getting the participant used to the general testing situation. They consist of: Motor Screening Task and Big/Little Circle.
+
+Motor Screening (MOT)
+Overview
+The Motor Screening test is typically administered at the beginning of a test battery, and serves as a simple introduction to the touch screen for the participant. If a participant is unable to comply with the simple requirements of this test, it is unlikely that they will be able to complete other tests successfully. This test therefore screens for visual, movement, and comprehension difficulties.
+Administration Time
+Around 2 min
+Task
+Participants must touch the flashing cross which is shown in different locations on the screen.
+Outcome Measures
+This test has two outcome measures which measure the participant’s speed of response and the accuracy of the participant’s pointing.
+Test Modes
+Two modes are available – clinical and high visibility. In high visibility the crosses are drawn using thicker lines and are easier to see.
+
+Big/Little Circle (BLC)
+Overview
+The Big/Little Circle test assesses comprehension, learning, and reversal. It is also intended to train participants in the general idea of following and reversing a rule, before proceeding to the Intra-Extra dimensional Shift test (IED), so it should ideally precede the IED task in a battery.
+Administration Time
+Around 2 min.
+Task
+Participants must first touch the smaller of the two circles displayed, then, after 20 trials, touch the larger circle for 20 further trials.
+Outcome Measures
+This test has five outcome measures, covering latency (speed of response) and the participant’s ability to touch the correct circle.
+Test Modes
+One mode – clinical
+
+Visual Memory
+These tests allow investigation of visual and spatial aspects of memory and consist of: Delayed Matching to Sample, Paired Associates Learning, Pattern Recognition Memory and Spatial Recognition Memory.
+
+Delayed Matching to Sample
+Overview
+Delayed Matching to Sample (DMS) assesses forced choice recognition memory for novel non-verbalisable patterns, and tests both simultaneous and short-term visual memory. This test is primarily sensitive to damage in the medial temporal lobe area, with some input from the frontal lobes.
+Administration Time
+Around 10 min
+Task
+The participant is shown a complex visual pattern (the sample) and then, after a brief delay, four similar patterns. The participant must touch the pattern which exactly matches the sample.
+Outcome Measures
+This test has 19 outcome measures, assessing latency (the participant’s speed of response), the number of correct patterns selected, and statistical analysis measuring the probability of an error after a correct or incorrect response.
+Test Modes
+Clinical mode (for testing once); five parallel modes (for repeated testing), and child mode (a simplified version for testing children)
+
+Paired Associates Learning (PAL)
+Overview
+This challenging test assesses visual memory and new learning, and is a useful tool for assessing patients with questionable dementia, Mild Cognitive Impairment, Alzheimer’s disease, and age-related memory loss.
+Administration Time
+Around 10 min, depending on level of impairment
+Task
+Boxes are displayed on the screen and are opened in a randomized order. One or more of them will contain a pattern. The patterns are then displayed in the middle of the screen, one at a time, and the participant must touch the box where the pattern was originally located. If the participant makes an error, the patterns are re-presented to remind the participant of their locations. The difficulty level increases through the test. In the clinical mode, the number of patterns increases from one to eight, which challenges even very able participants.
+Outcome Measures
+This test has 21 outcome measures, covering the errors made by the participant, the number of trials required to locate the pattern(s) correctly, memory scores, and stages completed.
+Test Modes
+Clinical mode (for testing once); five parallel modes (for repeated testing)
+
+Pattern Recognition Memory (PRM)
+Overview
+This is a test of visual pattern recognition memory in a two-choice forced discrimination paradigm. This test is often used, in conjunction with Spatial Recognition Memory (SRM), before the Paired Associates Learning (PAL) test, as both these tests help to train the participant for PAL. PRM and SRM contain different elements of PAL and the results considered together help to decide on the exact nature of the cognitive deficit being considered.
+Administration Time
+Around 5 min, depending on level of impairment
+Task
+The participant is presented with a series of 12 visual patterns, 1 at a time, in the center of the screen. These patterns are designed so that they cannot easily be given verbal labels. In the recognition phase, the participant is required to choose between a pattern they have already seen and a novel pattern. In this phase, the test patterns are presented in the reverse order to the original order of presentation. This is then repeated, with 12 new patterns. The second recognition phase can be given either immediately or after a 20 min delay.
+Outcome Measures
+This test has three outcome measures, including the number and percentage of correct trials and latency (speed of participant’s response).
+Test Modes
+Clinical mode (for testing once); four parallel modes (for repeated testing). Each of these modes also has separate immediate and delayed versions available.
+
+Spatial Recognition Memory (SRM)
+Overview
+This is a test of visual spatial recognition memory in a two-choice forced discrimination paradigm. This test is often used, in conjunction with Pattern Recognition Memory (PRM), before the Paired Associates Learning (PAL) test, as both these tests help to train the participant for PAL. PRM and SRM contain different elements of PAL and the results considered together help to decide on the exact nature of the cognitive deficit being considered.
+Administration Time
+Around 5 min, depending on level of impairment
+Task
+The participant is presented with a white square, which appears in sequence at five different locations on the screen. In the recognition phase, the participant sees a series of five pairs of squares, one of which is in a place previously seen in the presentation phase. The other square is in a location not seen in the presentation phase. As with the PRM test, locations are tested in the reverse of the presentation order. This subtest is repeated three more times, each time with five new locations.
+Outcome Measures
+This test has three outcome measures, including the number and percentage of correct trials and latency (speed of subject’s response).
+Test Modes
+Clinical mode (for testing once); four parallel modes (for repeated testing)
+
+CANTAB – Executive Function, Working
+Memory, and Planning Tests
+These tests address executive function, working memory, and planning; all are associated with the frontal area of the brain.
+
+Attention Switching Task (AST)
+Overview
+AST is a test of the participant’s ability to switch attention between the direction or location of an arrow on screen. This test is a sensitive measure of frontal lobe and ‘executive’ dysfunction.
+Administration Time
+Around 8 min, depending on level of impairment
+Task
+The test begins with an arrow in the centre of the screen which points either to the left or to the right. The participant is introduced to two buttons, one on the left and one on the right, and is asked to press a button corresponding to the direction in which the arrow is pointing. After this initial training, the participant is then told that the arrow might appear on the left or the right side of the screen, and depending on the cue given at the top of the screen, the participant must either press the left or right button to indicate on which side of the screen the arrow is displayed, or else press the left or right button to correspond with the direction in which the arrow is pointing.
+Outcome Measures
+AST has 7 outcome measures, each of which can have various options applied to it. The AST measures cover latency, correct and incorrect responses, commission errors, omission errors, switch cost and congruency cost.
+Test Modes
+AST has one mode: Clinical
+
+Intra-Extra Dimensional Set Shift (IED)
+Overview
+Intra-Extra Dimensional Set Shift is a test of rule acquisition and reversal. It features:
+• Visual discrimination and attentional set formation
+• Maintenance, shifting, and flexibility of attention
+This test is primarily sensitive to changes to the fronto-striatal areas of the brain. This test is a computerized analogue of the Wisconsin Card Sorting test, and is sensitive to cognitive changes associated with schizophrenia, Parkinson’s Disease, and dopaminergic-dependent processes.
+Administration Time
+Around 7 min, depending on level of impairment
+Task
+Two artificial dimensions are used in the test:
+• Color-filled shapes
+• White lines
+Simple stimuli are made up of just one of these dimensions, whereas compound stimuli are made up of both, namely white lines overlying color-filled shapes. The participant starts by seeing two simple color-filled shapes, and must learn which one is correct by touching it. Feedback teaches the participant which stimulus is correct, and after six correct responses, the stimuli and/or rules are changed. These shifts are initially intra dimensional (e.g., color-filled shapes remain the only relevant dimension), then later extra dimensional (white lines become the only relevant dimension). Participants progress through the test by satisfying a set criterion of learning at each stage (six consecutive correct responses). If at any stage, the participant fails to reach this criterion after 50 trials, the test terminates.
+Outcome Measures
+This test has 18 outcome measures, assessing errors, and number of trials and stages completed.
+Test Modes
+Clinical mode (for testing once); seven parallel modes (for repeated testing)
+
+One Touch Stockings of Cambridge (OTS)
+Overview
+One Touch Stockings of Cambridge is a spatial planning task which gives a measure of frontal lobe function. OTS is a variant of the Stockings of Cambridge test (see below) and places greater demands on working memory as the participant has to visualize the solution.
+Administration Time
+Around 10 min, depending on level of impairment
+Task
+As for SOC (Stockings of Cambridge), the subject is shown two displays containing three colored balls. The displays are presented in such a way that they can easily be perceived as stacks of colored balls held in stockings or socks suspended from a beam. This arrangement makes the 3-D concepts involved apparent to the participant, and fits with the verbal instructions. There is a row of numbered boxes along the bottom of the screen. The test administrator first demonstrates to the participant how to use the balls in the lower display to copy the pattern in the upper display, and completes one demonstration problem, where the solution requires one move. The participant must then complete three further problems, one each of two moves, three moves, and four moves. Next the participant is shown further problems, and must work out in their head how many moves the solutions to these problems require, then touch the appropriate box at the bottom of the screen to indicate their response.
+Outcome Measures
+OTS has four outcome measures – problems solved on first choice, mean choices to correct, mean latency to first choice, and mean latency to correct. Each of these measures may be calculated for all problems, or for problems with a specified number of moves (one move to five or six moves).
+Test Modes
+OTS has four modes, with varying numbers of problems and boxes.
+
+Spatial Span (SSP)
+Overview
+Spatial Span assesses working memory capacity, and is a visuospatial analogue of the Digit Span test.
+Administration Time
+Around 5 min, depending on level of impairment
+Task
+White squares are shown, some of which briefly change color in a variable sequence. The participant must then touch the boxes which changed color in the same order that they were displayed by the computer (for clinical mode) or in the reverse order (for reverse mode). The number of boxes increases from two at the start of the test to nine at the end, and the sequence and color are varied through the test.
+Outcome Measures
+This test has six outcome measures, covering span length (the longest sequence successfully recalled), errors, number of attempts, and latency.
+Test Modes
+Two modes: clinical mode and reverse mode.
+
+Spatial Working Memory (SWM)
+Overview
+SWM is a test of the participant’s ability to retain spatial information and to manipulate remembered items in working memory. It is a self-ordered task, which also assesses heuristic strategy. This test is a sensitive measure of frontal lobe and “executive” dysfunction. It has been shown in recent studies that impaired performance on SWM emerges as a common factor in prepsychosis.
+Administration Time
+Around 8 min, depending on level of impairment
+Task
+The test begins with a number of colored squares (boxes) being shown on the screen. The aim of this test is that, by touching the boxes and using a process of elimination, the participant should find one blue “token” in each of a number of boxes and use them to fill up an empty column on the right hand side of the screen. The number of boxes is gradually increased, until it is necessary to search a total of eight boxes. The color and position of the boxes used are changed from trial to trial to discourage the use of stereotyped search strategies.
+Outcome Measures
+The 24 outcome measures for SWM include errors (touching boxes that have been found to be empty, and revisiting boxes which have already been found to contain a token), a measure of strategy, and latency measures.
+Test Modes
+Clinical mode
+
+Stockings of Cambridge (SOC)
+Overview
+SOC is a spatial planning test which gives a measure of frontal lobe function
+Administration Time
+Around 10 min, depending on level of impairment.
+Task
+The participant is shown two displays containing three coloured balls. The displays are presented in such a way that they can easily be perceived as stacks of coloured balls held in stockings or socks suspended from a beam. This arrangement makes the 3-D concepts involved apparent to the participant, and fits with the verbal instructions. The participant must use the balls in the lower display to copy the pattern shown in the upper display. The balls may be moved one at a time by touching the required ball, then touching the position to which it should be moved. The time taken to complete the pattern and the number of moves required are taken as measures of the participant’s planning ability.
+Outcome Measures
+This test has three outcome measures, including the number and percentage of correct trials and latency (speed of participant’s response).
+Test Modes
+Clinical mode
+
+CANTAB Attention Tests
+These tests measure different aspects of attention and reaction time. Choice Reaction Time (CRT), Rapid Visual Information Processing (RVP), and Simple Reaction Time (SRT) use the press pad exclusively as an input device; Match to Sample Visual Search (MTS) and Reaction Time (RTI) use both the press pad and the touch screen.
+
+Choice Reaction Time
+Overview
+Choice Reaction Time (CRT) is a two-choice Reaction Time test which is similar to the Simple Reaction Time (SRT) test, except that stimulus and response uncertainty are introduced by having two possible stimuli and two possible responses. It is useful for testing general alertness and motor speed.
+Administration Time
+Around 7 min, depending on level of impairment
+Task
+An arrow-shaped stimulus is displayed on either the left or the right side of the screen. The participant must press the left hand button on the press pad if the stimulus is displayed on the left hand side of the screen, and the right hand button on the press pad if the stimulus is displayed on the right hand side of the screen.
+Outcome Measures
+This test has 13 outcome measures, assessing correct and incorrect responses, errors of commission and omission (late and early responses), and latency (response speed).
+Test Modes
+Clinical mode
+
+Match to Sample Visual Search (MTS)
+Overview
+Match to Sample Visual Search (MTS) is a matching test, with a speed/accuracy trade-off. It is a simultaneous visual search task with response latency dissociated from movement time. Efficient performance on this task requires the ability to search among the targets and ignore the distractor patterns which have elements in common with the target. This test can help to differentiate between Parkinson’s disease and Alzheimer’s disease, and also between Lewy Body dementia and Alzheimer’s disease.
+Administration Time
+Around 9 min, depending on level of impairment
+Task
+The participant is shown a complex visual pattern (the sample) in the middle of the screen, and then, after a brief delay, a varying number of similar patterns are shown in a circle of boxes around the edge of the screen. Only one of these boxes matches the pattern in the center of the screen, and the participant must indicate which it is by touching it. Reaction time is measured on the basis of the release of the press pad, which allows for its more accurate measurement.
+Outcome Measures
+The 12 outcome measures for SOC cover the number of problems solved with minimum moves, the mean number of moves for n-move problems, mean initial thinking time for n-move problems, and mean subsequent thinking time for n-move problems.
+Test Modes
+Clinical mode
+
+Rapid Visual Information Processing (RVP)
+Overview
+Rapid Visual Information Processing (RVP) is a test of sustained attention (similar to the Continuous Performance Task) and has proved useful in many studies in which drugs are used to help develop a disease model. It is sensitive to dysfunction in the parietal and frontal lobe areas of the brain and is also a sensitive measure of general performance.
+Administration Time
+Around 7 min
+Task
+A white box appears in the center of the computer screen, inside which digits, from 2 to 9, appear in a pseudo-random order, at the rate of 100 digits per minute. Participants are requested to detect target sequences of digits (e.g., 2–4–6, 3–5–7, 4–6–8) and to register responses using the press pad.
+Outcome Measures
+The nine RVP outcome measures cover latency, probabilities, and sensitivity (calculated using Signal Detection Theory), and hits, misses, false alarms, and rejections.
+Test Modes
+Clinical mode, plus 123 mode (for children aged 4–8) and 357 mode (for children aged 7–14)
+
+Reaction Time (RTI)
+Overview
+Reaction Time (RTI) is a latency task with a comparative history (the five choice task) and uses a procedure to separate response latency from movement time. It is more useful than CRT or SRT where it is necessary to control for tremor.
+Administration Time
+Around 5 min, depending on level of impairment
+Task
+The task is divided into five stages, which require increasingly complex chains of responses. In each case, the subject must react as soon as a yellow dot appears. In some stages, the dot may appear in one of five locations, and the subject must sometimes respond by using the press pad, sometimes by touching the screen, and sometimes both.
+Outcome Measures
+The four outcome measures in RTI are divided into Reaction Time (simple and five-choice) and movement time (simple and five-choice)
+Test Modes
+Clinical mode, parallel mode, and child mode
+
+Simple Reaction Time (SRT)
+Overview
+Simple Reaction Time (SRT) is a test which measures simple Reaction Time through delivery of a known stimulus to a known location to elicit a known response. The only uncertainty is with regard to when the stimulus will occur, by having a variable interval between the trial response and the onset of the stimulus for the next trial. Like Choice Reaction Time (CRT), it is useful for testing general alertness and motor speed, and is often sensitive to medication effects.
+Administration Time
+Around 6 min, depending on level of impairment
+Task
+As soon as the participant sees the square on the screen, they must press the button on the press pad.
+Outcome Measures
+The 11 outcome measures for SRT cover latency (response speed), correct responses, and errors of commission and omission.
+Test Modes
+Clinical mode
+
+CANTAB – Semantic/Verbal
+Memory Tests
+These tests, which address semantic and/or verbal memory, are relatively new additions to the CANTAB battery consisting of: Graded Naming Test (GNT) and Verbal Recognition Memory (VRM).
+
+Graded Naming Test (GNT)
+Overview
+The Graded Naming Test has been used extensively in cognitive neuropsychology. The Graded Naming Test (GNT) avoids the problem of ceiling effects in previous naming tests by having participants name drawings of objects in ascending difficulty. Reduced efficiency in retrieving the name of an object can be the first and only indication of impaired language functioning. This test assesses object-naming ability, but is in addition graded in difficulty to allow for individual differ-ences. This means that it may be able to detect any word-finding difficulty even in those with an extensive naming vocabulary.
+Administration Time
+Around 10 min, depending on level of impairment
+Task
+Thirty different line drawings are displayed on the screen, 1 at a time. The participant must identify the object depicted in each drawing.
+Outcome Measures
+This test has six outcome measures, which include total correct, total errors, and normative z-score and percentile.
+Notes
+Currently available in UK English only (this test is culturally biased and there are no alternative ver-sions at present). A pencil and paper version of this test is also available.
+Test Modes
+Clinical mode
+
+Verbal Recognition Memory (VRM)
+Overview
+Despite the general desirability of nonverbal tests because of their culture free applicability, researchers and clinical studies sometimes require verbal tests, perhaps because of need to explore questions relating to language or left hemisphere function. Other verbal tests have a long history of use in psychiatric assessment and clinical studies. The Verbal Recognition Memory test, which assesses immediate and delayed memory of verbal information under free recall and forced choice recognition conditions, should provide comparable results.
+Task
+In the VRM test, the participant is shown a list of 12 words, 1 at a time, and then asked to:
+• Produce as many of the words as possible immediately following the presentation
+• Recognize the words they have seen before from a list of 24 words containing the original 12 words and 12 distractors
+• Following a delay of 20 min, recognize the words they have seen before from another list of 24 words containing the original list and 12 new distractors
+Outcome Measures
+The five outcome measures for VRM cover correct and incorrect responses for the recognition and free recall parts of the test.
+Notes
+Currently available in UK English only
+Test Modes
+Clinical mode and four parallel modes for repeated testing. Each mode has immediate and delayed parts.
+
+CANTAB – Decision Making and
+Response Control Tests
+These tests add another dimension to cognitive profiling and investigation of frontal lobe function. Most decisions in life have an emotional or risk-related component, and many clinical conditions are associated with inappropriate risk models/strategies. They consist of Affective Go/No-go (AGN), Information Sampling Task (IST), Cambridge Gambling Task (CGT) and Stop Signal Task (SST).
+
+Affective Go/No-go (AGN)
+Overview
+This test assesses information processing biases for positive and negative stimuli. Affective cognitive functions are thought to be related to the ventral and medial-prefrontal cortex areas of the brain because of the limbic connections with this region. As such, the Affective Go/No-go test represents a powerful research assessment tool for current studies on the neural substrates of depression, bipolar disorder, Post-Traumatic Stress Disorder (PTSD), and many other affective conditions.
+Administration Time
+Around 10 min, depending on level of impairment
+Task
+The test consists of several blocks, each of which presents a series of words from two of three different affective categories: Positive (e.g., joyful), Negative (e.g., hopeless), and Neutral (e.g., element). The participant is given a target category, and is asked to press the press pad when they see a word matching this category.
+Outcome Measures
+Twelve outcome measures covering latency and errors of commission and omission
+Note
+Currently available in English only.
+Test Modes
+Six modes, four using positive and negative stimuli only, and two using positive, negative, and neutral stimuli
+
+Cambridge Gambling Task (CGT)
+Overview
+The Cambridge Gambling Task was developed to assess decision making and risk-taking behavior outside a learning context. Relevant information is presented to the participants “up-front” and there is no need to learn or retrieve information over consecutive trials. Unlike other “Gambling” tasks, CGT dissociates risk taking from impulsivity, because in the ascending bet condition, the participant who wants to make a risky bet has to wait patiently for it to appear. The likely neural substrate for this task is the orbitofrontal prefrontal cortex. Traumatic Brain Injury, Alcoholism, and Drug abuse are all conditions sensitive to this test.
+Administration Time
+Up to 30 min
+Task
+On each trial, the participant is presented with a row of ten boxes across the top of the screen, some of which are red and some of which are blue. At the bottom of the screen are rectangles containing the words “Red” and “Blue.” The participant must guess whether a yellow token is hidden in a red box or a blue box. In the gambling stages, participants start with a number of points, displayed on the screen, and can select a proportion of these points, displayed in either rising or falling order, in a second box on the screen, to gamble on their confidence in this judgment. A stake box on the screen displays the current amount of the bet. The participant must try to accumulate as many points as possible.
+Outcome Measures
+The six CGT outcome measures cover risk taking, quality of decision making, deliberation time, risk adjustment, delay aversion, and overall proportion bet.
+Test Modes
+Ascending first (where stakes are displayed in ascending order for two stages, then in descending order for two stages) and Descending first (where stakes are displayed in descending order for two stages, then in ascending order for two stages).
+
+Information Sampling Task (IST)
+Overview
+The Information Sampling Task (IST) tests impulsivity and decision making.
+Administration Time
+Up to 15 min
+Task
+The subject is presented with a 5 × 5 array of gray boxes on the screen, and two larger colored panels below these boxes. The subject is instructed that they are playing a game for points, which they can win by making a correct decision about which color is in the majority under the gray boxes. They must touch the gray boxes one at a time, which open up to reveal one of the two colors shown at the bottom of the screen. Once a box has been touched, it remains open. When the subject has made their decision about which color is in the majority, they must touch the panel of that color at the bottom of the screen to indicate their choice. After the subject has indicated their choice, all the remaining gray boxes on the screen reveal their colors and a message is displayed to inform the subject whether or not they were correct. The colors change from trial to trial. There are two conditions – the fixed win condition, in which the subject is awarded 100 points for a correct decision regardless of the number of boxes opened, and the decreasing win condition, in which the number of points that can be won for a correct decision starts at 250 and decreases by 10 points for every box touched. In either condi-tion, an incorrect decision costs 100 points.
+Outcome Measures
+The eight IST outcome measures cover errors, latency, total correct trials, mean number of boxes opened per trial, and probability of the subject’s decision being correct based on the available evidence at the time of the decision.
+Test Modes
+IST has two modes:
+• Fixed win-decreasing win (after practice trials, the fixed win stage precedes the decreasing win stage)
+• Decreasing win-fixed win (after practice trials, the decreasing win stage precedes the fixed win stage)
+
+Stop Signal Task (SST)
+Overview
+SST is a classic stop signal response inhibition test, which uses staircase functions to generate an estimate of stop signal reaction time. This test gives a measure of an individual’s ability to inhibit a prepotent response.
+Administration Time
+Up to 20 min
+Task
+This test consists of two parts. In the first part, the participant is introduced to the press pad, and told to press the left hand button when they see a left-pointing arrow, and the right hand button when they see a right-pointing arrow. There is 1 block of 16 trials for the participant to practice this. In the second part, the participant is told to continue pressing the buttons on the press pad when they see the arrows, as before, but, if they hear an auditory signal (a beep), they should withhold their response and not press the button.
+Outcome Measures
+SST has five outcome measures, each of which can have various options applied to it. The SST mea-sures cover direction errors, proportion of success-ful stops, RT on GO trials, SSD (50%), SSRT.
+Test Modes
+SST has one mode: clinical.
+
+Social Cognition
+A range of disorders are known to affect social cognition and there is an expanding research field examining how such conditions may bias cognitive processes involved in social interaction. This domain is assessed by: Emotion Recognition Task (ERT).
+
+Emotion Recognition Task (ERT)
+Overview
+ERT measures the ability to identify emotions in facial expressions. The participant is shown a series of faces which appear on the screen briefly and asked to identify the emotion (happiness, sadness, anger, disgust, surprise and fear).
+Administration Time
+Around 10 min, depending on level of impairment.
+Task
+One hundred and eighty stimuli, which are computer morphed images derived from the facial features of real individuals each showing a specific emotion, are displayed on the screen, one at a time, in two blocks of ninety. Each face is displayed for a short while (200 ms) and then immediately covered up, and then six buttons are displayed, each describing an emotion which could be portrayed in the photograph. The participant must decide which is the appropriate button to describe the emotion and touch the button. There are fifteen different photographs for each of the six emotions, each showing different levels of intensity.
+Outcome Measures
+The outcome measures for ERTcover percentages and numbers correct or incorrect, and overall response latencies. Results can be looked at across individual emotions, or across all emotions at once.
+Test Modes
+ERT is available for clinical trials immediately, and will be available for academic research in CANTABeclipse 5. Please contact Cambridge Cognition for further information. ERT takes around 10 min to administer in healthy individuals.
+
+Other Tests
+
+Visual Analogue Scales (VAS)
+Overview
+Visual Analogue Scales are psychometric response scales which can be used as a measurement instrument for subjective states. The CANTAB VAS assess subjective measurements of drug effect, energy levels, sickness, alertness and mood.
+Administration Time
+Around 5 min, depending on level of impairment.
+Task
+The participant must respond to sixteen questions as they appear on the screen by touching the on-screen slider and moving it to the appropriate position on the scale.
+Outcome Measures
+The outcome measures for this test allow you to look at the data on a question-by-question basis.
+Test Modes
+Please contact Cambridge Cognition for information about availability for academic research.
+
+Historical Background
+Grounded in the neurosciences, the CANTAB® neuropsychological tests were developed more than 21 years ago at the University of Cambridge by Professors Robbins and Sahakian, to enable detailed translational assessment and evaluation of cognitive function. Lesion, neuroimaging, clinical and psychopharmacological studies have enabled a unique understanding of the structural, clinical and biochemical sensitivities of each of the tests. (CANTAB) The CANTAB battery was developed for the assessment of cognitive deficits in humans with neurodegenerative diseases or brain damage (Fray and Robbins 1999). It consists of a series of interrelated computerized tests of memory, attention, and executive function, administered via a touch-sensitive screen. It allows a decomposition of complex tasks commonly used in clinical assessment into their cognitive components and enables the extrapolation of findings from the animal literature. Tests include versions of the Wisconsin Card Sorting Test and the Tower of London and also the Delayed Matching to Sample test, widely used in monkeys for visual recognition memory. The tests are constructed in such a way that they may be given to animals (monkeys) with minimal change. The nonverbal nature of the CANTAB tests makes them largely language independent and culture free. CANTAB has been standardized on a large, predominantly elderly, population and validated in neurosurgical patients as well as in patients with basal ganglia disorders, Alzheimer’s disease, depression, and schizophrenia. In addition, CANTAB has been used to evaluate: (a) the therapeutic effects of dopaminergic and cholinergic medication in neurodegenerative disease; (b) cognition in 5–11-year old normal, learning-disabled, and autistic children; (c) deficits in patients with HIV infection; and (d) early, asymptomatic Huntington’s disease. The latter illustrate its usefulness in early identification of progressive disorders. It is suggested that the battery should have particular utility across a wide range of age and intelligence in longitudinal assessment after exposure to toxicants, and allow meaningful comparison with experimental studies of toxic effects in other species.
+
+There is emerging evidence to support the involvement of frontal cortex in autism. CANTAB is particularly useful in helping study the cognitive profile of children who have autism and related disorders.
+
+Psychometric Data
+CANTAB tests are sensitive to cognitive changes caused by a wide range of CNS disorders and medication effects. Where error scores are a key outcome measure, CANTAB tests are graded in difficulty to avoid ceiling effects. Where accurate measurement of latency is important, responses are made via a press pad. Elsewhere, engaging touch-screen technology maximizes compliance. The majority of CANTAB tests are independent of language and culture.
+
+Clinical Uses
+The following cognitive and other disorders have been investigated using CANTAB®:
+
+| Disorder 1 | Disorder 2 |
+|---|---|
+| AD/HD – Attention deficit hyperactivity disorder | Lesion in orbitofrontal cortex |
+| AIDS dementia complex | Liver failure |
+| Alcoholism | Long-term health effects of diving |
+| Amphetamine addiction | Machado-Joseph disease |
+| Amygdalo- hippocampectomy | Mad Hatter’s disease |
+| Anorexia nervosa | Manic depression |
+| Anterior parietal damage | Melancholia |
+| Antisocial behavior | Mercury poisoning |
+| Antisocial personality disorder | Mild cognitive impairment (MCI) |
+| Anxiety | Motor neuron disease |
+| Attention deficit- hyperkinetic disorder | Multiple sclerosis |
+| Autism | Multiple system atrophy |
+| Basal ganglia lesions | Narcolepsy |
+| Bipolar disorder | Neuronal migration disorders |
+| Borderline personality disorder | Normal pressure hydrocephalus |
+| Camptocormia | Obsessive compulsive disorder |
+| Capgras syndrome | Organophosphate pesticide exposure |
+| Carcinoid syndrome | Panic disorder |
+| Chronic drug misuse | Paraphrenia |
+| Chronic fatigue syndrome | Parkinson’s disease |
+| Chronic occupational solvent encephalopathy | Periventricular brain insult |
+| Critical illness requiring intensive care | Personality disorder |
+| Dementia Alzheimer-type (DAT) | Petrol (gasoline) sniffing |
+| Dementia lewy body type | Phenylketonuria |
+| Dementia of frontal type | Post-concussion syndrome |
+| Developmental dyslexia | Premature birth needing intensive care |
+| Diabetes | Premenstrual dysphoric disorder |
+| Dorsolateral frontal cortical compression | Progressive supranuclear palsy |
+| Down’s syndrome | Psychopathy |
+| Drug abuse | Psychosis |
+| Dysexecutive syndrome | Questionable Dementia |
+| Frontal lobe damage | Renal Cancer |
+| Frontal lobe excision | Roifman syndrome |
+| Frontal variant frontotemporal dementia | Schizoaffective disorder |
+| Gluten ataxia | Schizophrenia |
+| Hallucinosis | Seasonal affective disorder |
+| Head injury | Self harm |
+| Hearing loss | Semantic dementia |
+| Heart disease | Specific language impairment |
+| Heart failure | Social withdrawal in Schizophrenia |
+| Heavy social drinking | Solvent encephalopathy |
+| Hepatic encephalopathy | Spina bifida |
+| Heroin addiction | Steele-Richardson- Olzsewski syndrome |
+| Herpes encephalitis | Stiff Person syndrome |
+| Hippocampal atrophy | Striatocapsular infarct |
+| HIV/AIDS | Subarachnoid hemorrhage |
+| Huntington’s disease | Substance abuse |
+| Hydrocephalus | Tardive dyskinesia |
+| Hypercortisolemia | Temporal lobe excision |
+| Hyperostosis frontalis interna | Temporal lobe lesion |
+| Hypertension | Tinnitus |
+| Insomnia | Tourette’s syndrome |
+| Korsakoff syndrome | Traumatic brain injury |
+| Late paraphrenia | Trichotillomania |
+| Lead exposure | Tuberous sclerosis |
+| Left ventricular systolic dysfunction | White matter lesions |
+
+Drugs
+Pharmacological studies (academic research) have been carried out on the following drugs using CANTAB:
+
+| Drug 1 | Drug 2 |
+|---|---|
+| Alcohol | Flumazenil |
+| Modafinil | Amisulphiride |
+| Fluoxetine | Neuroleptic |
+| Amphetamine | Galantamine |
+| Nicotine | Antipsychotic medication |
+| Ginkgo biloba | Olanzapine |
+| Antiretroviral therapy | Glyburide |
+| Opiates | Atomoxetine |
+| Guanfacine | Paroxetine |
+| Branch chain amino acid drink | Highly active antiretroviral therapy (HAART) |
+| Pergolide | Bromocryptine |
+| Haloperidol | Perindopril |
+| Buspirone | Heroin |
+| Petrol/ Gasoline | Caffeine |
+| Hydrocortisone | Phenserine |
+| Cannabis | Idazoxan |
+| Quetiapine | Chlorpromazine |
+| Idazoxan plus | Clonidine |
+| Risperidone | Clonidine |
+| Interferon | Ritalin |
+| Clozapine | Interleukin-2 |
+| Rivastigmine | Cocaine |
+| Kava | Rosiglitazone |
+| delta-9 tetrahydrocannabinol | Ketamine |
+| RU-486 | Dexamphetamine |
+| L-Dopa | Scopolamine |
+| Diazepam | Lecithin |
+| SGS742 | Donepezil |
+| MDMA | Sulpiride |
+| Dopaminergic medication | Metamphetamine |
+| Tacrine | Ecstasy |
+| Methylphenidate | Tryptophan |
+| Endozepines | Mifepristone |
+| Tyrosine | |
+
+See Also
+
+References and Reading
+
+Camouflaging Autistic Traits Questionnaire (CAT-Q)
+Laura Hull and William Mandy
+Research Department of Clinical, Educational and
+Health Psychology, University College London,
+London, UK
+
+Synonyms
+CAT-Q
+
+Description
+The Camouflaging Autistic Traits Questionnaire (CAT-Q) is a standardized self-report measure of camouflaging behaviors in autistic and non-autistic adults. It comprises 25 items and takes around 5 min to complete, on paper or online. The scale consists of three sub-scales: compensation (strategies used to overcome social difficulties associated with autism), masking (strategies used to hide autistic characteristics or present a less autistic persona), and assimilation (strategies used to avoid standing out during social interac-tions). In addition to sub-scale scores, a total camouflaging score can be calculated as the sum of all scores (ranging from 25 to 175, with higher scores indicating greater camouflaging). The CAT-Q is completed by the individual themselves, reflecting on their own behaviors at the present time.
+
+Historical Background
+Camouflaging describes the use of strategies, whether deliberate or automatic, to minimize the appearance of autistic characteristics during social interactions and to compensate for social difficulties associated with autism (Hull et al. 2017). Camouflaging has also been proposed as a potential explanation for the underdiagnosis of autism in females (Lai et al. 2015); if girls and women use more, or more successful, camouflaging strategies to hide or compensate for their autism, they are less likely to be identified by clinical services. This can lead to lack of support and acceptance, as well as the potential for resulting mental health difficulties (Bargiela et al. 2016; Milner et al., 2019). Recent research has also suggested that camouflaging strategies themselves may be associated with negative mental health outcomes for autistic adults (Cage et al. 2018; Hull et al. 2017) and young people (Tierney et al. 2016). Until recently there has been no way to measure how much someone is camouflaging. Some researchers have quantified camouflaging as the discrepancy between an individual’s internal autistic experience (such as level of autistic traits) and the external behavioral presentation (such as ADOS score; Lai et al. 2017). This approach has generally concluded that females camouflage more than males (Lai et al. 2017, 2018; Parish-Morris et al. 2017; Ratto et al. 2018). However, the discrepancy approach to mea-suring camouflaging requires multiple, often time-consuming measures to be taken for each individual and only measures the effect camouflaging has on behavior rather than the effort put into camouflaging. An alternative measurement is the CAT-Q, which directly measures the extent of camouflaging strategies self-reported by an individual. This makes measurement of camouflaging quick and easy, and the CAT-Q is freely available to download (Hull et al. 2018).
+
+Psychometric Data
+There is limited psychometric data for this measure, particularly across cultures, abilities, and age groups. The CAT-Q has been validated in autistic and non-autistic adult males and females in a large sample (N ¼ 832; Hull et al. 2018) and demonstrated good internal consistency (α ¼ 0.94 for total camouflaging scale) and acceptable test-retest reliability in a smaller sample (ICC [C,1] ¼ 0.77). Measurement invariance has also been demonstrated between autistic and non-autistic males and females, demonstrating that the CAT-Q can be used with individuals regardless of whether they have received a formal diagnosis of autism. This is particularly important as camouflaging is likely to exist along a continuum, similarly to autistic traits (Constantino, 2011), and individuals who camouflage extensively may consequently not meet current diagnostic criteria for autism (Kreiser and White 2014). There is some evidence to support the convergent validity of the CAT-Q, with higher scores associated with higher levels of autistic-like traits (Hull et al. 2018).
+
+Clinical Uses
+Camouflaging has been associated with mental health difficulties including depression (Lai et al. 2017), anxiety (Hull et al. 2018), and suicidal thoughts (Cassidy et al. 2018). The CAT-Q can be used to identify autistic adults who may be a greater risk of these co-occurring conditions and help them access support. However, norms have not yet been established for either autistic or non-autistic adults; therefore clinically meaningful cutoffs have not been identified. There is also potential for the CAT-Q to be used as part of the autism diagnostic assessment pro-cess in adults and adolescents. Adults, particularly women, who have not yet received an autism diagnosis, may camouflage their characteristics during autism assessments, leading to under-recognition of their level of need. Further clinical research is needed to examine exactly how the CAT-Q can be integrated into gold standard assessment processes.
+
+See Also
+
+References and Reading
+
+Can’t Versus Won’t Dilemma
+Elaine Coonrod
+Department of Psychiatry, School of Medicine,
+TEACCH, The University of North Carolina at
+Chapel Hill, Chapel Hill, NC, USA
+
+Definition
+One common issue faced by parents, teachers, and caregivers of individuals with autism is understanding when a behavioral difficulty is due to a skill deficit (“can’t”), rather than due to deliberate noncompliance (“won’t”). Caregivers who attribute behavior problems to deliberate noncompliance often see the behavior as rooted in laziness, stubbornness, or defiance. This attribution has multiple negative consequences, including increased frustration and stress for the caregiver, as well as use of ineffective or confrontational behavior management strategies. Even caregivers who have some understanding of autism may believe that the individual with autism is purposely engaging in misbehavior, and consequently become embroiled in an unproductive power struggle. The confusing behavioral picture presented by individuals with autism contributes to this misunderstanding. For example, individuals with autism often have a very typical physical appearance, so the caregivers’ natural inclination is to expect age-appropriate skills and behavior. In addition, many individuals with autism, including those with language impairments, can repeat back verbal directions even when they have not fully understood the content of what was said, giving a misimpression about their level of understanding. Furthermore, poor social insight and communication deficits may mean that individuals with autism are unable to recognize and communicate their own lack of skill or need for assis-tance, or may cause them to question directions from others in a manner that is perceived as argumentative or disrespectful. Perhaps most confusing for caregivers is the unusual scatter of strengths and weaknesses shown by individuals with autism, as well as their difficulty in generalizing the use of skills from one context to another. For example, parents of a bright 14 year old with autism may simply have difficulty understanding how their son can have extensive working knowledge of his computer, yet not be able to successfully operate the microwave. A teacher of a more impaired 7 year old may be confused as to why the student can independently use the toilet at home but repeatedly soils her clothing at school.
+
+In general, when faced with a “can’t versus won’t” dilemma, it is more productive to begin by assuming that the individual with autism “can’t” and then conduct a behavioral assessment focused on the symptoms of autism that may be impeding his or her behavioral success. The caregiver should consider the ways in which the individual’s unique profile of strengths and weaknesses in communication, socialization, flexibility and interests, sensory responses, and learning style may be contributing to the behavioral difficulty. That information can then be used to generate positive, proactive strategies to help sup-port desired behaviors in the future.
+
+References and Reading
+
+Canada and Autism
+Marc Woodbury-Smith1,2 and Frank Tran3
+1Department of Psychiatry and Behavioural
+Neuroscience, McMaster University, Hamilton,
+ON, Canada
+2Institute of Neuroscience, Newcastle University,
+Newcastle upon Tyne, UK
+3St. Joseph’s Healthcare, Hamilton, ON, Canada
+
+Background
+In Canada, as in many other countries, a growing public awareness of autism spectrum disorder (ASD) has emerged in the context of the evidence for a rising prevalence and the existence of life-long vulnerabilities and complex medical and mental health comorbidities (Anagnostou et al. 2014). Parents and carers have been instrumental in raising this awareness, and the government has responded with the commissioning of new services, principally focused on the needs of children (Motiwala et al. 2006; Auditor General of Ontario 2013). As discussed subsequently, despite increased public funding for ASD-focused services, significant inequalities in service provision exist for specific groups, including adults and higher-functioning individuals (Shattuck et al. 2012), newly arrived immigrants (Khanlou et al. 2017), and individuals with complex health and social care needs (Autism Ontario 2008). Moreover, service inequalities exist between different provinces (Eggleton and Keon 2007). It is now widely recognized that there is an urgent need for uniformly accessible services for all individuals with ASD, irrespective of age or any other characteristic. The provision of a uniform service can only truly be achieved through federal involvement and ultimately a national policy or legislative framework. Such a strategy has widespread support and in 2007 was a suggestion made by the Senate Select Committee (Eggleton and Keon 2007), although at this stage there is no indication of the adoption of a federal initiative. By way of background, in Canada, each province (of which there are ten, along with three territories) is responsible for providing healthcare and social services for all individuals. This of course includes children and adults with developmental disabilities, such as ASD. Funds for services are raised through taxation, and each province implements its own model of service delivery. Importantly, for healthcare federal policy – by way of the Canada Health Act – still provides some oversight and direction, including the directive that universal access to publically funded “medically necessary” services must be ensured for all. However, the significant power devolved by the government to provincial policymakers does result in interprovincial variation in services. It is difficult to articulate the minutiae of province-by-province differences, and so this present entry will provide a simple overview, drawing examples from individual provinces but not attempting to present a detailed and comprehensive picture of ASD services across Canada.
+
+Overview of Current Treatments and
+Centers
+
+Early Diagnosis and Intervention
+One significant development across Canada has been the widespread availability of early intervention services for children with ASD (Anagnostou et al. 2014; Volden et al. 2015). Early intervention, based on the principles of Applied Behavior Analysis (ABA), is funded at the provincial level by those Ministries responsible for child, family, and community care. For example, in Ontario such services are commissioned by the Ministry of Child and Youth Services (separate from the Ministry of Health which is responsible for healthcare), in Alberta by the Ministry of Children’s Services, and in British Columbia (BC) by the Ministry of Children and Family Development. Early intervention programs (Intensive Behavioural Intervention or IBI) involve one-on-one therapy during which the child is engaged in a series of discrete trials involving reward contingencies to facilitate learning and generalization. The trials themselves focus on language and communication, as well as social and adaptive skills. Intensive intervention typically involves 20–40 h of 1:1 therapy per week over a period of 2 years, which has been shown to maximize the chance of improvement (Reitzel et al. 2015 and references therein). Research has consistently shown that early language and cognition are strong predictors of outcome in later childhood and into adulthood (Henninger and Taylor 2013), although outcome in later adult life may be related more closely to early social adjustment (Howlin et al. 2013). The increased availability of early intervention services has therefore been welcomed by all involved in ASD policy. Moreover, research has shown the success of such programs (Warren et al. 2011), although the research is not clear cut (Reichow et al. 2012). There is still much interprovincial variation in service delivery: for example, in Nova Scotia publicly funded IBI services are available to all young children with ASD, and this level of care is echoed in Ontario. As would be expected, with the evidence for a rising prevalence, currently estimated at ~1% (or 67,000 children age between 3 and 20 years) in Canada (Anagnostou et al. 2014), the demand on these services is large, and consequently wait times are often long between receiving a diagnosis and accessing IBI. Some form of triage is often in place to target those children who are more likely to benefit. For example, in Ontario IBI is reserved for younger children who have “severe autism.” Although “severe” is not explicitly defined, clinicians responsible for intake use a variety of screening tools to determine eligibility. However, even targeting services in this way, wait times are still substantial. For example, in 2013, the waitlist for IBI in Southern Ontario, comprising parts of the Greater Toronto Area (GTA) and the surrounding “Golden Triangle,” included 1748 children (Auditor General of Ontario 2013). Slightly different service provision is seen in BC and Alberta, where public funding is provided to partly offset the costs of private intervention sourced by the family itself.
+
+Early intervention demands that diagnosis is made as early as possible, which is dependent on the availability of expert clinical diagnostic services. Although certain groups, such as the American Academy of Pediatrics, have recommended universal screening for ASD between the ages of 18 and 24 months (Johnson and Myers 2007), the Canadian Pediatric Society instead advices developmental surveillance (Anagnostou et al. 2014). In parts of Canada, this approach has been facilitated through the use of brief, validated, and reliable screening questionnaires (Zwaigenbaum 2009). While family physicians are in a position to provide early screening, however, the diagnosis itself may be delayed through the unavailability of appropriate expertise. While family physicians are the frontline staff involved in developmental surveillance, the responsibility for early diagnosis rests with existing services, such as developmental pediatrics and child psychiatry. The provision of adequate training to primary and secondary healthcare workers is therefore crucial.
+
+School-Aged Children
+Services for school-aged children have also seen progress in recent years. In some provinces, the focus remains on early intervention for preschoolers, whereas other provinces have also developed services for children with ASD up to the age of 18 years. In Ontario, for example, services have developed to meet the varying needs of this population using ABA principles. Among some children with ASD, particularly those who function typically, the focus may be on social skills, often, although not necessarily, delivered in a group setting, whereas for others, it may be behavioral or adaptive needs. The emphasis is on mastering skills one at a time and then learning to apply these in everyday settings. In 2013, the median age of children accessing this service was 8 years, with 90% age 14 or younger indicating that older children and those in transition (i.e., age 17–18 years) may not be accessing services. In addition to therapy that targets the core symptoms of ASD, there is also a need for mental health services to address the high level of emotional distress and comorbidity among children with ASD (Drmic and Szatmari 2014). The impact on later outcome for therapy aimed at school-aged children will need to be fully evaluated.
+
+Adults
+Services for adults with ASD in Canada have lagged behind those for children (Stoddart et al. 2013). It is estimated that in the region of 4900, teenagers with ASD in Canada reach their 18th birthday each year (Shattuck et al. 2012). Based on current epidemiological estimates, as many as 70% of these may have IQs in the typical range (Centers for Disease Control and Prevention 2014). Despite this, however, the outcome for many is poor. For example, studies of outcomes consistently find low to modest levels of indepen-dence and the persistence of core phenotypic traits and associated developmental and mental health vulnerabilities beyond childhood (Howlin et al. 2013). It is clear, therefore, that for an individual with ASD – irrespective of their IQ – health and community/social care needs will remain significant throughout much of their lives (Stoddart et al. 2013; Autism Ontario 2008). With increases in life expectancy, this potentially represents a public health crisis. Indeed, on an individual basis, the lifetime costs associated with ASD have been estimated at up to $2.44M US dollars in the USA and UK (Buescher et al. 2014).
+
+Healthcare for adults with ASD in Canada is met according to the universal healthcare principals of the Canada Health Act. Specialist mental health services for adults with developmental disabilities do exist but are not government mandated. Such services focus on the mental health and behavioral needs of adults with IQs below 70, and as such, many adults with ASD will not meet the access criteria. For those adults who do have IQs 70 or above, it is expected that existing mental health services will meet their needs, although this is often not the case, with some excluded from community mental health services as a result of their ASD diagnosis, essentially leaving them “doubly socially excluded.” This is even more concerning when the statistics are considered: in one study examining comorbidity, as many as 70% of young adults with ASD had experienced one or more episode of major depression, with 50% experiencing recurrent depression and 50% describing an anxiety disorder (Lake et al. 2014). In Canada, vocational and social care needs, including the provision of supported accommodation, are met by the Ministry responsible for the commission of community and social services. Among those who have an intellectual disability, i.e., evidence of IQ <70 along with adaptive impairments, a variety of services are available, although the large demand for services results in long wait times. Such services include supported living and respite, supported employment and other vocationally centered programs, and behavioral services. Adults with ASD who have IQs above 70, however, are generally denied access to such services (Stoddart et al. 2013; Autism Ontario 2008). As such, the emphasis has been on private initiatives. For example, in Alberta the Sinneave Foundation in collaboration with the consulting firm Meticulon provides individuals with autism an opportunity to work with the IT field while providing them with the appropriate wages. The availability of similar initiatives exist across the country.
+
+Specific Issues
+Rural communities have identified a definite lack of resources and services when tending to the needs of their children. The lack of medical support has made life difficult for families and pro-viding proper treatment difficult as there are no professionals or workers to provide the training and insight to how to provide the best environ-ment for their children to grow (Hoogsteen and Woodgate 2013). Similar barriers to service access is seen among newly arrived immigrants to Canada (Khanlou et al. 2017). Rates of ASD have been shown to be 36% higher in children of immigrant mothers. Considering the fact that migration is an integral part of Canadian federal policy and that a significant proportion of Canada’s population is made up of newly arrived immigrants, this represents a major area of need. Addressing this requires a better understanding of the barriers to care, which will include, for example, language and knowledge of existing structures, as well as federal policy to overcome these barriers. Other specific issues relate to the organization of existing structures of care. For example, transition planning and implementation continues to present a major challenge for families, with the negotiation of adult services an extra hurdle at an already difficult time (Gorter et al. 2011). Hospitals themselves are often not set up to manage individuals with ASD and other developmental disabilities effectively. For example, it has been argued that emergency departments are poorly equipped to accommodate patients with ASD appropriately (Nicholas et al. 2016). Key problems identified are lack of communication and training. The same concern has been expressed in relation to primary care, although initiatives such as the Primary Care Developmental Disabilities Network in Ontario attempts to overcome this barrier through more adequate training (Sullivan et al. 2011).
+
+Overview of Research Directions
+Canada has a long tradition of research in ASD, with basic and applied scientific approaches being used to further the knowledgebase on ASD. These comprise a number of multisite, high-impact, studies. By way of example, two research pro-grams are briefly highlighted below.
+
+Pathways
+This large, multisite project comprises researchers across five Canadian provinces (Ontario, Quebec, Nova Scotia, Alberta, and British Columbia). It has recruited newly diagnosed children with ASD, aged between 2 and 4 years, and prospectively following these children to examine their developmental trajectories (Szatmari et al. 2015). Across time, data is collected at four separate intervals, with core symptomatology, behavior, and adaptive function all being measured in detail. This is a powerful design for a number of reasons. Importantly, most previous studies have recruited participants at different points in the natural history of their disorder. Without sampling an inception cohort (a group assembled at a common time point early in the development of the disorder), there is no way of ensuring that all subgroups of children with ASD are included in the sampling frame. Additionally, in many other studies, individuals with ASD are recruited through clinics, which may introduce bias into the outcomes being observed.
+
+Genetics
+Canada has a long tradition of genetics research in autism spectrum disorder, utilizing state-of-the-art techniques to attempt to unravel the genetic com-ponent of ASD’s etiology. In recent years, the focus has been on identifying de novo and inherited unbalanced copy number variation (CNV) (Pinto et al. 2014), including variation at the single nucleotide level (SNV). The current research activities of this multisite, multi-disciplinary group focus on the MSSNG project, a collaboration between Google and Autism Speaks under the directorship of Prof. Steve Scherer that aims to sequence the DNA of over 10,000 families with one or more members affected by autism (Yuen et al. 2017).
+
+Current Controversies
+The development of services in Canada in recent years has seen a major injection of money, particularly in relation to IBI programs aimed at young children. These programs have been developed along evidence-based lines and are constantly evaluated to make certain of clinical impact. The provincial-led nature of these programs means that there remain inequalities of care across the country as a whole, with rural communities often having the least joined-up level of care. Furthermore, adults, and particularly those who are higher functioning, have seen the least in the way of service development. The need for a federal approach to ASD-specific policy has been raised but not yet adopted.
+
+See Also
+
+References and Reading
+
+Candidate Genes in Autism
+Youeun Song1 and Abha R. Gupta2
+1Child Study Center, Yale University School of
+Medicine, New Haven, CT, USA
+2Developmental-Behavioral Pediatrics, Child
+Study Center, Yale University, New Haven, CT,
+USA
+
+Definition
+Although twin and family studies show that genes play a critical role in determining the risk for autism, its specific genetic etiology remains largely unknown. A candidate gene is one for which there is some evidence of contribution to the etiology of a disorder but for which this has not yet been definitively demonstrated. These genes are identified by a variety of techniques including linkage analysis, association studies, cytogenetic analysis, studies of copy number variation, and next-generation sequencing. Typically, once a candidate gene has been identified, it is reinvestigated via analysis in independent patients’ samples. Particularly for studies that rely on case–control comparisons, replication is essential to elevating a candidate gene to a “risk” gene.
+
+Historical Background
+Over the past decade, many studies have shown that autism is not a simple Mendelian disorder caused by a single gene at the population level. In the early phase of autism gene discovery, the majority of candidate genes were selected for study based on biological plausibility; that is, they were involved in some biological process that could conceivably play a role in ASD. These genes were then typically evaluated in candidate gene association studies in which one or a small number of common genetic polymorphisms in or near one or a small number of genes were evaluated in cases versus controls. If an overrepresentation of a particular allele or alleles was identified, the gene was considered a candidate ASD gene. These studies were based on the hypothesis that common alleles were responsible for the disorder. Across all of medicine, the majority of such studies proved difficult to replicate. In retrospect, it is clear that approach had some significant limi-tations. Among these, the chances of choosing correctly among millions of genetic variations were low, the effect sizes carried by common alleles for most common medical conditions were much smaller than anticipated (resulting in studies that were in retrospect often markedly underpowered), and there were multiple potential confounds, including ancestral mismatching of cases and controls, that were difficult to control for. More recently, the approach has been replaced for the most part by genome-wide association studies, typically of large patient cohorts, that eliminate many of these difficulties. This approach has led to the identification of replicated risk alleles in many common medical conditions, including schizophrenia and bipolar disorder.
+
+Over the last several years, the identification of candidate genes through studies of common variation has been complemented by studies of rare variation. Here again, it is common practice to pursue an initial observation with an attempted independent replication. With rare variations, the infrequency of individual mutations and the overall genetic heterogeneity of autism may make such studies difficult to mount. A variety of approaches are being developed in an effort to provide a path to confirm candidate loci: these include assessing the total amount of rare varia-tion in a gene in cases versus controls (as opposed to asking questions about one particular rare allele). This approach is often called a mutation burden analysis. In addition, there are ongoing efforts to take advantage of particular types of variation, including de novo mutations, to increase the power to detect and confirm the asso-ciation of a gene or locus with ASD risk (Sanders et al. 2011).
+
+Current Knowledge
+
+Genome-Wide Linkage Studies
+Linkage studies identify chromosomal loci inherited by affected individuals more frequently than expected by chance. These studies most often investigate multiplex families in which there is more than one affected person. DNA polymorphisms are used as markers of chromosomal loci throughout the genome. The closer the marker is to a disease gene, the more likely there is cosegregation between the marker and the phenotype under study. The likelihood that a locus is linked to the phenotype is represented as the LOD score (logarithm of the odds). For example, a LOD score of 3 means that there is 1,000 to 1 odds that the locus is linked to the phenotype. When the LOD score is more than 2.2, linkage is considered suggestive; 3.6 is considered significant (Lander and Kruglyak 1995). Linkage peaks have been found on almost every chromosome. As reviewed by Gupta and State (2007), loci with among the highest LOD scores are 3q26.32 (LOD 4.81), 2q31.1 (LOD 4.80), 17q11.2 (LOD 4.3), 17q21.32 (LOD 4.1), and 7q36.1 (LOD 3.7). For the most part, linkage studies in autism have failed to replicate each other, probably due to a number of reasons, such as nonuniform criteria for patient selection, differing sets of polymorphisms, and differing statistical methodologies. A few loci, such as 17q11-q21 and along 7q, have been highlighted by more than one study (Abrahams and Geschwind 2008). Some of the genes implicated are CNTNAP2 (contactin-associated protein-like 2), EN2 (engrailed homeobox 2), RELN (reelin), MET (MET proto-oncogene), CADPS2 (Ca2+-dependent activator protein for secretion 2), ITGB3 (integrin beta3), and SLC6A4 (solute carrier family 6) (Abrahams & Geschwind). Linkage studies have also been conducted in consanguineous families using homozygosity mapping. Homozygous regions are parts of the genome where the identical chromosomal segment is inherited from both parents due to a recent common ancestor. In homozygosity mapping, it is hypothesized that the disorder is inherited as a recessive trait. Candidate genes found by this method include DIA1 (deleted in autism-1), NHE9 (sodium/proton exchanger 9), PCDH10 (protocadherin 10), and CNTN3 (contactin 3) (Morrow et al. 2008).
+
+Candidate Gene and Genome-Wide Association Studies
+Association studies determine whether there is a statistically significant relationship between expo-sure to the variant and increased (or decreased) population risk for the phenotype. Numerous genetic association studies have investigated common variants in one or a small number of candidate genes, often selected due to hypothesis-driven disease models. Since these studies are relatively inexpensive, many genes have been evaluated for associa-tion with autism, with multiple positive results. However, very few of them have been replicated (Gupta and State 2007). Some genes identified by this method are GABRB3 (gamma-aminobutyric acid A receptor beta3), GRIK2 (glutamate receptor ionotropic kainite 2 precursor), SLC25A12 (solute carrier family 25 member 12), MET, RELN, EN2, SLC6A4, and CNTNAP2 (Abrahams and Geschwind 2008; State 2010). Rare variants can also be investigated by association studies, but this method requires comprehensive resequencing of candidate genes in large cohorts and is expensive. In addition to common variants, rare variants in CNTNAP2 have been associated with autism (Bakkaloglu et al. 2008). More recently, high-resolution SNP arrays have enabled genome-wide association studies (GWAS), which query all genes rather than investigating a few candidate genes at a time. Three loci which have been associated with autism are chromosome 5p14.1, between the genes CDH9 (cadherin 9) and CDH10 (cadherin 10), chromo-some 5p15, near the gene SEMA5A (semaphoring 5A), and chromosome 20p12.1, near the gene MACROD2 (MACRO domain containing 2) (reviewed by State 2010). CDH9 and CDH10 are interesting candidate genes since they are involved in neuronal cell adhesion. SEMA5A has been implicated in axonal guidance.
+
+Cytogenetic Analysis
+Cytogenetic analysis is the study of chromosomal abnormalities such as inversions, translocations, duplications, deletions, and aneuploidies. Traditionally, these abnormalities have been detected via karyotype analysis (microscopic examination of chromosomes). A review by Veenstra-VanderWeele et al. (2004) calculated that 4.3% of the 1826 karyotypes published in the ASD literature are abnormal. Abnormalities have been found on every chromosome, indicating that no one rearrangement is responsible for any substan-tial fraction of cases. The most common chromosomal abnormality found in ASD is maternally inherited duplications at 15q11-q13 (Abrahams and Geschwind 2008). Some genes which have been implicated by cytogenetic analysis include NLGN4X (neuroligin 4X), UBE3A (ubiquitin pro-tein ligase E3A), GABRB3, CENTG2 (centaur

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+Initiation of the ASD-CP was associated with a significant reduction in holds and restraints in both brief-stabilization and inpatient settings. Further, a 40% decrease in total length of stay approached statistical significance in this small, initial study (Kuriakose et al. 2018). A subsequent study was then conducted to examine the sustain- ability of these results, adding a third comparison group of youth who received the ASD-CP in the 18 months following the initial implementation period. Results from this study demonstrated that reductions in the use of crisis interventions, including holds, restraints, and intramuscular medications, were sustained, while the non- statistically significant trend toward decreased length of stay was no longer present (Cervantes et al. 2019). Taken together, current data suggest that the ASD-CP can be implemented and sustained with limited resources and minimal expertise and is associated with improved patient care.
+
+### Future Directions
+
+While the results of significant and continued reductions in crisis interventions are exciting and essential, research into the ASD-CP requires fur- ther development. First, these initial studies were small, and the samples were heterogeneous. Therefore, replication across sites and with larger samples is needed to improve confidence in the effects of the ASD-CP. We also do not currently have documented evidence of intervention fidel- ity. However, the process of data abstraction from medical records across the first two studies exposed inconsistent use of some of the ASD- CP tools, particularly those that required docu- mentation (e.g., staff schedule). Although it is undeterminable how ASD-CP components that do not require documentation (e.g., first-then card, simplifying language) were implemented, the inconsistent use of those that do suggests that the improvements seen may be due to a milieu change, such that changes in staff self-efficacy and understanding are responsible for the reduc- tions in their use of crisis interventions (Cervantes et al. 2019). A more formal evaluation of how patient care and outcome relates to staff fidelity on ASD-CP intervention components and staff acceptability of ASD-CP strategies is currently underway. Importantly though, because fidelity estimates are captured by record review, we are limited by a lack of formal documentation of use across strategies. Therefore, we are now creating a feasible process for assessing implementation of tools and strategies across each staff shift using a brief fidelity checklist. Improved understanding of intervention acceptability and fidelity, and how these factors might influence patient care and outcome, will allow us to identify key com- ponents of the ASD-CP and thus pare down the intervention to increase feasibility. Subsequently, staff supports will be further developed to encour- age consistent implementation of essential com- ponents. For instance, we will be adding periodic booster training sessions for all retained staff.
+
+Further, as reported, the ASD-CP was designed for a distinct subpopulation of the autism spec- trum. However, youth with ASD who do not meet criteria for the ASD-CP also require thoughtful adaptations to treatment as usual in psychiatric inpatient settings. Future research should assess the utility and feasibility of implementation of ASD-CP strategies for youth of varying severity levels and presentations. Of note, presenting con- cerns may differ between and within groups of youth who do and do not meet criteria for the ASD-CP. For example, it is not uncommon for youth with ASD to present with internalizing symptoms, such as anxiety, post-traumatic stress disorder (PTSD), depression, and/or suicidality (Siegel 2018). These individuals would likely require variations in programming that are distinct from both the ASD-CP and treatment as usual. Additional resources for assessing and addressing the unique needs of these children in non- specialized psychiatric inpatient settings are required.
+
+Finally, researchers have found that psychiatric hospitalization in specialized settings is associ- ated with lower recidivism rates for youth with ASD (Gabriels et al. 2012). It is essential that we also study long-term outcomes for patients who receive the ASD-CP. While it is promising there were demonstrated improvements in care during their stay, understanding if and how the ASD-CP improves patient utilization trajectories and tran- sitions to less restrictive care environments post discharge is integral and would have significant public health implications given the high costs associated with hospitalization. Readmission rates are often elevated in this population of chil- dren, as demonstrated by the proportion of youth excluded from evaluation due to readmission sta- tus (~10%) across our studies (Kuriakose et al. 2018). While quality of care during psychiatric hospitalization contributes to this, readmission rates are also largely driven by the considerable lack of appropriate community supports available for patients to transition to after their stay. This systemic issue of limited accessibility of supports increases both the prevalence of psychiatric hos- pitalization and the economic burden of ASD. Importantly, researchers have found that higher spending on ASD-specific outpatient services and on respite care in particular was associated with significant reductions in the likelihood of psychiatric hospitalization (Mandell et al. 2012, 2019). Therefore, not only are improvements in inpatient care necessary, but it is essential that we continue to work to increase accessibility to evidence-based treatments and family supports to prevent hospitalization and keep youth with ASD integrated in the community.
+
+### See Also
+
+*   ▶Emergency Department Utilization and Autism
+*   ▶Irritability in Autism
+*   ▶Mental Health and ASD
+*   ▶Suicidality in Children and Adolescents with Autism
+
+### Caregiver Consent to a Pediatric Neurodevelopmental Research Registry
+Luke Kalb
+Department of Mental Health, Johns Hopkins
+Bloomberg School of Public Health, Kennedy
+Krieger Institute’s Center for Autism and Related
+Disorders, Baltimore, MD, USA
+
+### Definition
+
+There has been a historical lag in the development of evidenced-based interventions for youth with neurodevelopmental disorders (NDD), including those with autism spectrum disorder (ASD). One well-known barrier to the development of empir- ically sound interventions is research recruitment. Problems with study recruitment and retention can result in the delay and/or termination of inter- vention studies. This methodological problem can also result in the selection of study participants who are not representative of the target popula- tion, leading to biased study estimates. Recruit- ment of youth with NDD can be particularly challenging, when compared to the neurotypical population, since the caregiver will have to simul- taneously manage the child’s extensive healthcare needs alongside participating in a research protocol.
+
+One potential solution to improve recruitment to NDD studies is through the use of research registries. There are many types of registries, including national or international disorder- specific registries as well as registries that recruit from a particular clinic or institution (hereafter referred to as clinic registries). Joining a clinic registry, which is governed under an institutional review board, offers parents the opportunity to hear about and potentially engage in local research opportunities. For the investigator, it pro- vides a low-cost option to actively recruit partic- ipants, rather than simply relying on passive recruitment methods (e.g., flyers, word of mouth).
+
+There is evidence to suggest that most care- givers raising a child with or at risk of NDD are agreeable to joining a clinic registry, when offered the opportunity during their child’s evaluation. This finding is valuable as it speaks to parent’s overall interest in joining the research enterprise. However, this conclusion is drawn from the only known study of this topic.
+
+There also appears to be disparate trends across settings in terms of the proportion of families who consent to join the clinic registry. Settings that primarily serve the ASD populations, rather than those serving youth with NDD as a whole, may find increased registry consent rates over time. There are many possible reasons for this finding, including the nature of the setting. If it is a one- time evaluation center, families may be less inter- ested at the prospect of an ongoing research rela- tionship compared to a setting where their child may be receiving care over an extended period of time. There may be something unique to the ASD population as well. There are numerous national organizations and initiatives that have brought science to the general conscious of the ASD com- munity, including the federally funded Autism Act, the Simons Foundation SPARK project, and Autism Speaks. These disparate efforts may have created a culture of scientific collaboration not seen in other populations.
+
+Family sociodemographic factors play an important role as to whether families consent to a clinical registry. For the caregiver, race and socioeconomic status likely influence their deci- sion. Race and ethnicity, in particular, may be related to medical mistrust given the historical research injustices (e.g., Henrietta Lacks) faced by people of color. The nature of the clinical registry, which elicits consent to contact for an unspecified prospective project, may exacerbate feelings of mistrust since caregivers are providing consent to hear about an unknown endeavor. Lastly, families with low income may be less likely to consent since they may not have the resources (e.g., ability to travel) or time to become involved in a research project.
+
+Beyond sociodemographics, it is likely the child’s clinical characteristics influence care- giver’s interest in research. There is evidence to suggest that caregivers of children with increased mental health issues may be more apt to consent. This may reflect the parents desire to enroll in a study that could assist with such problems or the desire to help other children like theirs. It would follow that increased core developmental issues, such as the presence of increased ASD symptoms, would be positively related to caregiver consent, just like mental health symptoms. However, increased ASD severity has not been associated with an increased likelihood of registry consent. There is research to suggest that these core devel- opmental issues are less stressful to caregivers than the presence of behavior problems. Perhaps the consent findings mimic this body of research on stress.
+
+Much more research is needed on trends and predictors of consent to clinic registries involv- ing youth with NDD. Qualitative approaches that identify reasons for consent, or lack thereof, are particularly needed. This work would shed light on the speculative reasons, put forth above, for why particular populations are more or less likely to consent. Future research should also seek to replicate the existing findings, identify novel predictors of consent, and examine this topic among other pediatric populations.
+
+### Caregiver Training Program
+
+*   ▶Babysitter Training Guide for Families with Individuals with ASD
+
+### Carnosine
+Fred R. Volkmar
+Child Study Center, Irving B. Harris Professor
+of Child Psychiatry, Pediatrics and Psychology,
+Yale Child Study Center, School of Medicine,
+Yale University, New Haven, CT, USA
+
+### Synonyms
+
+beta-Alanyl-L-histidine
+
+### Definition
+
+Carnosine is a compound formed from two amino acids (histidine and alanine) and is found in sev- eral organ systems including muscle and brain. A number of possible biological roles for this compound have been suggested including antiox- idant properties. It has been used experimentally in several disorders. One small double-blind study in 2002 by Chez and colleagues reported positive initial findings, although the study was criticized on various grounds and the results have not yet been well replicated in the scientific literature.
+
+### See Also
+
+*   ▶Neurochemistry
+
+### CARS
+
+*   ▶Childhood Autism Rating Scale
+
+### CARS, Second Edition, High-Functioning Version
+
+*   ▶Childhood Autism Rating Scale
+
+### CARS, Second Edition, Questionnaire for Parents or Caregivers
+
+*   ▶Childhood Autism Rating Scale
+
+### CARS, Second Edition, Standard Version
+
+*   ▶Childhood Autism Rating Scale
+
+### CARS2-HF
+
+*   ▶Childhood Autism Rating Scale
+
+### CARS2-QPC
+
+*   ▶Childhood Autism Rating Scale
+
+### CARS2-ST
+
+*   ▶Childhood Autism Rating Scale
+
+### Case Report
+
+*   ▶Case Study
+
+### Case Study
+Fred R. Volkmar
+Child Study Center, Irving B. Harris Professor of
+Child Psychiatry, Pediatrics and Psychology, Yale
+Child Study Center, School of Medicine, Yale
+University, New Haven, CT, USA
+
+### Synonyms
+
+Case report
+
+### Definition
+
+Case studies are frequent in both biomedical and behavioral psychological research. A typical case study (sometimes referred to as case report) pro- vides a focused report of an individual or series of individuals to illustrate some important issue rel- evant to clinical work or research. Many of the conditions now recognized as significant causes of developmental disability first appeared as case reports, for example, Down syndrome and child- hood autism. Sometimes case reports are used to draw attention to other relevant issues, for exam- ple, new approaches to treatment. Case studies from the behavioral literature may be used to illustrate the possible effectiveness of a new inter- vention, for example, the subject is used as his/her own control with data collected pre-, during, and postintervention. In other fields such as business or law, case studies take other formats.
+
+Case studies may be primarily descriptive or may be more theoretical in nature. Sometimes, as in the case of Down syndrome (trisomy 21), the underlying theory may prove profoundly wrong but the observation is very robust (in the case of Down syndrome, the report from Dr. Down appeared well before there was any awareness of the importance of human chromosomes in devel- opment and disease). Case studies can bring atten- tion to new phenomena, can serve as a vehicle for teaching or documenting a potentially important clinical issue, and may, over time, lead to more focused hypothesis-based research. As noted, single-subject research methods provide possibil- ities for statistically based evaluation within a report focused on a single case.
+
+While case studies have importance in focus- ing attention on new observations and stimulating hypothesis testing and theory building, they also have some important limitations. Various factors can go into the selection of the case that is reported, and generalization is therefore difficult. There is an obvious tendency on the part of editors and reviewers to support positive association reports (rather than negative ones) in case studies and again generalization can be limited. Cases may also be reported with these multiple publica- tions of the same case contributing to a perception of greater significance than actually is apparent. As a result, many journals now have limited pub- lication of case reports.
+
+One example in the autism research is provided by the many case reports of autism associated with a host of medical conditions ranging from congen- ital

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+## 15q13.3 Microdeletion Syndrome
+Christian Patrick Schaaf1 and Madelyn A Gillentine2
+1Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
+2Department of Genome Sciences, University of Washington, Seattle, WA, USA
+
+### Synonyms
+CHRNA7 deletions
+
+### Definition
+15q13.3 microdeletion syndrome (OMIM 612001, DECIPHER coordinates: chr15: 30,901,306-32,445,407, hg19) is the result of heterozygous deletions at chromosome 15q13.3, ranging in size from 350 kb to 3.9 Mb. These deletions are mediated by nonallelic homologous recombination (NAHR) between four low copy repeat (LCR) elements: breakpoints (BPs) 3, 4, and 5, as well as the D-CHRNA7-LCR. The most common of these deletions, spanning 1.5 Mb to 2 Mb are mediated by BPs 4 and 5 and encompass six genes: FAN1, MTMR10, TRPM1, KLF13, OTUD7A, and CHRNA7, as well as one micro-RNA: hsa-miR-211. Of these genes, CHRNA7 and OTUD7A are the top candidate genes (Yin et al. 2018; Gillentine and Schaaf 2015).
+
+The estimated frequency of the most common 15q13.3 microdeletions is 1 in 5525 live births (0.19%) and is estimated to be higher (0.29%) among individuals with intellectual disability and idiopathic generalized epilepsy (1%) (Gillentine et al. 2018). However, these deletions also exhibit incomplete penetrance, with about 20% of individuals not having any diagnosed phenotypes. This contributes to 15q13.3 micro-deletions being de novo (15%) and inherited (85%). Of note, studies have found that a large proportion of 15q13.3 microdeletion probands are adopted; so, while having unknown inheritance, it is likely that these are inherited from affected parents (Ziats et al. 2016).
+
+Individuals carrying 15q13.3 microdeletions have a wide range of phenotypes, including intellectual disability/developmental delay, seizures/epilepsy, autism spectrum disorder (ASD), and schizophrenia (Ziats et al. 2016). In general, probands with 15q13.3 microdeletion syndrome have height, weight, and fronto-occipital circumference within the normal range. Over half of 15q13.3 microdeletion probands exhibit cognitive deficits, with a study of 18 probands finding the average full-scale IQ to be 60 (Ziats et al. 2016; Gillentine and Schaaf 2015). The next most prevalent phenotype is seizures/epilepsy, affecting about one third of probands. Language or speech impairments are also common, affecting just under one third of probands. Other neuropsychiatric phenotypes include schizophrenia, ASD or autistic features, ADHD or attention difficulties, and mood disorders in less than 20% of probands each. Abnormal behaviors, including aggression, and impulsiveness have been observed in about a quarter of the cases. Dysmorphic features are pre-sent in about one third of probands, although there is not a consistent pattern of dysmorphia.
+
+While deletions between 1.5 Mb and 2 Mb are the most common, but both larger and smaller deletions are reported with similar clinical phenotypes. Notably, homozygous deletions at 15q13.3 have been reported and are phenotypically more severe, with probands exhibiting neonatal encephalopathy. Additionally, the reciprocal micro-duplication is also pathogenic, with incomplete penetrance as well and a similar range of phenotypes, although typically less severe (Gillentine and Schaaf 2015).
+
+Several hypotheses have been proposed to explain the variable expressivity observed among 15q13.3 microdeletion syndrome probands. These include additional copy number changes and/or single nucleotide variants contrib-uting to phenotypes and epigenetic changes. However, the most prominent hypothesis is the effect of modifier genes, in particular the human-specific fusion gene CHRFAM7A, consisting exons 5 through 10 of CHRNA7 and a sequence of unknown function, FAM7A. The fusion gene is copy variable and polymorphic among the population. Functional studies have shown that increasing amounts of CHRFAM7A can contribute to CHRNA7 dysfunction (Ihnatovych et al. 2019).
+
+Currently, there is no consistent treatment for 15q13.3 microdeletion syndrome. CHRNA7, encoding for the α7 nicotinic acetylcholine recep-tor (nAChR), has been suggested as a candidate gene. Dysfunction of the α7 nAChR is supported molecularly, with a decrease of the receptor resulting in decreased calcium flux through the channel (Gillentine et al. 2017). Due to this, α7 agonists and positive allosteric modulators (PAMs) have been suggested as a possible treat-ment and assessed among a few individuals with mixed results. One individual carrying a 15q13.3 microdeletion who exhibited recurrent rage outbursts was treated with galantamine, a nAChR allosteric modulator and acetylcholinesterase inhibitor, with positive results, although such drugs are known to have severe side effects (Cubells et al. 2011). Individuals with schizophrenia or autism spectrum disorder have also been treated with nAChR agonists in small studies with positive, but limited results (Olincy et al. 2016). To date, no large clinical trials have been performed using such compounds.
+
+### See Also
+*   ▶Angelman/Prader-Willi Locus
+*   ▶Angelman/Prader-Willi Syndromes
+*   ▶Cholinergic System
+*   ▶Chromosomal Abnormalities
+*   ▶Chromosome 15q11–q13
+
+## 16p11.2
+Stephan Sanders
+Child Study Center, Yale University, New Haven, CT, USA
+
+### Definition
+16p11.2 refers to a particular region on the short (p) arm of chromosome 16 that corresponds to an approximately 500 kilobase copy number varia-tion (CNV) that is strongly associated with the risk for ASD. The region contains 28 genes and is flanked by segmental duplications (stretches of near-identical DNA). These are known to increase the likelihood of a process known as non-homologous allelic recombination, which can lead to gains or losses of the chromosomal seg-ment flanked by these repeats.
+
+The importance of deletions and duplications at 16p11.2 in ASD was recognized simultaneously by three research groups (Kumar et al. 2008; Marshall et al. 2008; Weiss et al. 2008). These findings have since been replicated multiple times. 16p11.2 CNVs are found in about 1% of individuals with autism, compared with less than 0.1% of the population. CNVs in this region have also been associated with intellectual disability, developmental delay, schizophrenia (duplications only), and obesity (deletions only) (http://www.ncbi.nlm.nih.gov/books/NBK11167/).
+
+CNVs involving this interval are among the most well-established risk factors for ASD. They also highlight the complexity of the genetic contri-bution to these syndromes: the CNVs are neither necessary (ASD can occur without 16p11.2 CNVs) nor sufficient (ASD is not always present with the CNV) to cause ASD. Both deletions and duplica-tions can contribute to risk, and these variations may either be de novo or transmitted within fami-lies. Moreover, in some families in which one affected child carries a 16p11.2, there may be other affected family members who do not.
+
+The region contains multiple biologically plausi-ble gene candidates for ASD (see list below). At this time, it is not known whether a single gene is responsible for the ASD phenotype or if a combina-tion of genes within the region accounts for the risk. The genes in the 16p11.2 region are ALDOA, ASPHD1, C16orf53, C16orf54, CDIPT, CORO1A, DOC2A, FAM57B, FLJ25404, GDPD3, HIRIP3, INO80E, KCTD13, LOC100271831, LOC440356, MAPK3, MAZ, MVP, PPP4C, PRRT2, QPRT, SEZ6L2, SLC7A5P1, SPN, TAOK2, TBX6, TMEM219, and YPEL3.
+
+### See Also
+*   ▶Candidate Genes in Autism
+*   ▶Chromosomal Abnormalities
+*   ▶Common Disease-Rare Variant Hypothesis
+*   ▶Copy Number Variation
+*   ▶DNA
+*   ▶Genetics
+
+## 504 Plan
+Kate Snyder1, Kara Hume2 and Christi Carnahan1
+1University of Cincinnati, Cincinnati, OH, USA
+2University of North Carolina, Chapel Hill, NC, USA
+
+### Definition
+Section 504 is a regulation of the Rehabilitation Act of 1973 that extends civil rights to individuals with disabilities. Enforced by the Office of Civil Rights (OCR) within the US Department of Health and Human Services, Section 504 states that “No otherwise qualified individual with a disability in the United States . . . shall, solely by reason of her or his disability, be excluded from the participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving Federal financial assistance. . .” (29 U.S.C. § 794(a)). Section 504 applies to any organization receiving federal funding; thus, it has important implications for individuals with autism spectrum disorders (ASD) and their participation in various educa-tional, recreational, community, and employment settings.
+
+### Historical Background
+The Civil Rights Act of 1964 and its prohibition of discrimination based on race, color, or national origin was a catalyst for the development of Section 504 of the 1973 Rehabilitation Act. Senator Hubert Humphrey (D., Minnesota) led the work to add an amendment to the Rehabilitation Act of 1973 that would address the discrimination of individuals with disabilities who had not been included under the Civil Rights Act. Section 504 was the first piece of legislation that specifically addressed the civil rights of individuals with disabilities.
+
+Implementation of Section 504 was wrought with challenges. Initial responsibility for writing implementation regulations was left to the US Department of Health, Education, and Welfare (HEW). Though drafts of the regulations were written as early as 1975 (Pfeiffer 2002), by 1977, the regulations had yet to be signed and implementation of Section 504 had stalled. In response, on April 5, 1977, the American Coali-tion of Citizens with Disabilities (ACCD) led demonstrations in HEW regional offices across the country. These demonstrations and other lob-bying efforts led to the signing of the regulations on April 28, 1977. Delays in the creation of government-wide implementation slowed the pro-cess of issuing regulations within individual fed-eral agencies (National Council on Disability 2003). Each department within the executive branch of the federal government now has its own regulations for implementing the provisions of Section 504 (Yell 2006).
+
+As the first civil rights legislation for individ-uals with disabilities, Section 504 of the 1973 Rehabilitation Act paved the way for future legis-lation for individuals with disabilities, including the 1990 adoption of the Americans with Disabil-ities Act (ADA) and the Individuals with Disabil-ities Education Act (IDEA). Together, Section 504, ADA, and IDEA protect the rights and equal participation of individuals with dis-abilities in employment, in education, and in the community.
+
+### Current Knowledge
+
+#### Qualification Under Section 504
+Section 504 specifically states that to be protected under the law, an individual must be determined to (1) have a physical or mental impairment that substantially limits one or more major life activi-ties, (2) have a record of such an impairment, or (3) be regarded as having such an impairment. Though no exhaustive list of specific “mental or physical impairments” covered by Section 504 exists, regulatory provision 34 C.F.R. 104.3(j)(2)(i) defines a physical or mental impairment as “any physiological disorder or condition, cos-metic disfigurement, or anatomical loss . . .or any mental or psychological disorder.” Major life activities, as defined by the Section 504 regula-tions at 34 C.F.R. 104.3(j)(2)(ii), include func-tions such as caring for one’s self, performing manual tasks, walking, seeing, hearing, speaking, breathing, learning, and working. It is important to note that this list is also not considered exhaus-tive, and thus other activities or functions not explicitly stated may be considered “major life activities” under Section 504. Since autism is a brain-based disorder (Wass 2011), individuals with a diagnosis of ASD would “have record” of a “mental impairment” that could potentially qualify them for protection under Section 504. Qualification is determined based upon the influence of an individual’s autism on his or her ability to perform a “major life activity.” The characteristics of autism manifest in social interactions, communicative exchanges, and through restricted or stereotyped patterns of behavior, interests, or activities (American Psy-chiatric Association 2000). Though to qualify for Section 504 each person on the autism spectrum must be evaluated on an individual basis, the disorder could potentially influence many “major life activities.”
+
+#### Application of Section 504 in Education (From Preschool Through Postsecondary)
+The provisions of Section 504 extend civil rights to individuals with disabilities to ensure access to activities and programs for which they “otherwise qualify” (29 U.S.C. § 794(a)). In other words, an individual meets program or employment criteria despite his or her disability. Applied to public education, this means that the individual with a disability is of public school age. Schools provid-ing a public education must ensure that students with disabilities have equal opportunity to benefit from educational programs and facilities under Section 504 (Yell 2006).
+
+A central component of Section 504 as it applies to public schools is the provision of a free appropriate public education (FAPE). FAPE, as defined by Section 504, requires that a student with a disability be provided with regular or spe-cial education and related aids and services that are designed to meet his or her individual educa-tional needs. These provisions must meet the indi-vidual’s needs as adequately as the needs of students without disabilities are met. Examples relevant to learners with ASD include using visuals to supplement verbal instruction, provid-ing tape recorders, modifying textbooks, using behavior support techniques such as reinforce-ment, adjusting class schedules, and increasing classroom organization/structure.
+
+Section 504 also requires that all educational programs be accessible to all learners. This does not mean that schools are required to make every room or program accessible to all students but that all learners have equal access to programming. For example, a school may offer multiple sections of a biology lab in three different classrooms. If one of the lab classrooms is accessible and two are not, the school still meets the expectation of Section 504 because the educational program is accessible to all students. It is not permissible, however, to create a scenario where a dispropor-tionate number of students with disabilities are assigned to the same program or activity because of accessibility issues. Returning to the example of the biology lab, it would not be acceptable for the school to create one section of the lab in which students with disabilities were overrepresented.
+
+This issue of disproportionality, or overrepre-sentation, is related to the FAPE provision within Section 504 that students with disabilities and students without disabilities should be placed in the same setting, to the maximum extent appro-priate to meet the needs of the students with dis-abilities. In addition, students with disabilities may not be excluded from participating in any school activities, including extracurricular pro-grams such as recreational sports or special inter-est clubs, in which students without disabilities would participate (US Department of Education, Office of Civil Rights 2010).
+
+Section 504 also requires that students with disabilities access programs and services in “com-parable facilities.” In the event that a student with a disability is educated in a separate facility from their peers, a district must ensure that the facility is comparable (i.e., in terms of space, location, size) to the district’s other facilities. Thus, Section 504 protects students with disabilities from the histor-ical practice of establishing special education classrooms in areas not conducive to learning, such as storage rooms or partitioned areas (Yell 2006).
+
+#### Eligibility Determination
+Since Section 504 and IDEA both protect the rights of individuals with disabilities in public education settings (through age 21), there is often confusion about eligibility requirements. It is important to note that not all students with disabilities who qualify for an individualized plan under Section 504 will meet the requirements for special education under IDEA. However, all students protected by IDEA also qualify for pro-tections under Section 504. One reason for this distinction is that under IDEA, a disability must have an adverse impact on a student’s learning that requires special education and related ser-vices. If a student does not require specialized instruction as a result of their disability, then he or she would not meet the requirements of IDEA. While IDEA explicitly requires the involvement of special education programming, implementa-tion of Section 504 is general education responsi-bility (Yell 2006). Essentially, Section 504 provides access to an education (“to and through the schoolhouse door,” Wright and Wright 2008); however, Section 504 includes no guarantee that the individual will receive educational benefit, as specified in IDEA.
+
+In order to determine a student’s eligibility under Section 504, schools are required to follow certain procedural safeguards related to the iden-tification, evaluation, or educational placement of students with a disability (U.S. Department of Education, Office for Civil Rights 2010). An eval-uation must occur if a parent or teacher has referred a student, if a student has a medical diag-nosis, or if a student has missed an excessive number of school days due to illness. Schools must use an evaluation procedure to determine whether a student’s disability (or perceived dis-ability) limits his or her ability to perform a major life activity, but there is no standardized protocol for how this evaluation should take place.
+
+The FAPE provision requires that once stu-dents have been evaluated and determined to meet the criteria for Section 504, school teams must develop an individualized plan that outlines how services and accommodations will be pro-vided. Many students who meet the criteria of Section 504 are also protected under IDEA. These students will therefore have an individual-ized education program (IEP) that will also con-stitute their written plan. If a student’s educational needs can be met with accommodations and related services that do not include specialized instruction, they do not typically qualify for spe-cial education. These students have only a Section 504 plan that reflects their needs. Finally, a number of rights and safeguards provided by IDEA are not similarly provided to individuals under Section 504, including prior written notice, rights to independent educational evaluations, and protections from permanent expulsion.
+
+#### Overview of major differences between Section 504 and IDEA
+
+| Section 504 | IDEA |
+|---|---|
+| **Eligibility** | Individuals must qualify under the broad definition: (1) have a physical or mental impairment that substantially limits one or more major life activities, (2) have a record of such an impairment, or (3) be regarded as having such an impairment. Need for special education is not a requirement | Students (aged 3–21) must qualify under one of the fourteen disability categories; students must demonstrate need for special education services |
+| **Major provisions** | No otherwise qualified individual with disability shall solely by reason of his or her disability be: • Excluded from participation in • Denied the benefits of • • Be subjected to discrimination under any program or activity receiving federal financial assistance | Procedural safeguards and the right to free appropriate public education in the least restrictive environment as defined by IDEA |
+| **Funding** | No funding provided for Section 504 | Both state and federal funding |
+| **Overall responsibility** | Local education agency (LEA); general education | State education agency (SEA); special education |
+
+#### Application in Postsecondary Education
+Any postsecondary institution that receives fed-eral funding is required to apply the regulations of Section 504 for qualifying individuals. Qualifying individuals at the postsecondary education level are those individuals with a disability who also meet the academic or technical standards that are required for admission by the institution. Individ-uals must also meet the participation requirements for the institution’s activity or program. FAPE does not apply to postsecondary educational set-tings; instead, institutions are required to provide “appropriate academic adjustments and auxiliary aids and services that are necessary to afford an individual with a disability an equal opportunity to participate in a school’s program” (US Department of Education, Office of Civil Rights 2011). The accommodations and services provided by a postsecondary institution should not alter the individual’s program in a fundamen-tal way nor should they create an “undue burden” on the institution.
+
+Individuals with autism who meet the require-ments for Section 504 while in elementary or secondary education should recognize that they might not receive the same services or accommodations at the postsecondary level. For example, some individuals with autism may be provided support from an educational assistant while in high school. Postsecondary institutions are not required to provide the same service because it may result in an undue financial burden to the institution (US Department of Education, Office of Civil Rights 2007). Another difference in provisions at the postsecondary level is the shift in responsibility. At the elementary and secondary school level, school districts are required to iden-tify, evaluate, and ensure services for an individ-ual with a disability under Section 504. At the postsecondary level, individuals must disclose their disability to the university and follow the institution’s procedures for requesting academic adjustments. Individuals with ASD must be pre-pared to discuss their individual needs when trans-itioning to the postsecondary education setting (Adreon and Durocher 2007).
+
+#### Application in Employment Settings
+Any employer who receives federal funding must also fulfill the mandates of Section 504 that pro-tect qualified individuals with a disability. The disability criterion for protection under Section 504 in an employment setting is the same as in educational settings; however, the def-inition of “qualified” is changed. For the purposes of employment, in order to be “qualified” an indi-vidual with a disability must be able to perform the essential function of the job with reasonable accommodation (US Department of Health and Human Services, Office of Civil Rights 2006). An employer is required to take steps to accom-modate an employee’s disability unless doing so would cause an undue burden to the employer.
+
+Workplace accommodations for individuals with disabilities are somewhat intuitive in certain situations (i.e., providing a sign language inter-preter for an individual who is deaf or an access ramp for an individual with a physical disability). Workplace accommodations can sometimes be less obvious in the case of an individual with ASD but are no less important in ensuring the individual’s success in the workplace. Accommo-dations for individuals with autism in the work-place could include minor modifications to work materials or physical changes in the workplace that make the position more accessible. For exam-ple, an employer could make the reasonable accommodation of providing a quieter workspace that reduces distractions if such a change would be an appropriate accommodation for the individual with autism.
+
+### Future Directions
+
+#### Increased Prevalence
+A recent prevalence study estimated that 2–3% (1:38) of the total school-age population have an autism spectrum disorder (Kim et al. 2011). Many of these students are served in the general educa-tion setting (i.e., two-thirds of the sample in the Kim et al. study, 2011) and may not qualify for services under IDEA. This increases the likeli-hood that individuals with ASD will receive pro-tections under Section 504, which has vast implications for school staff. This resurgence in 504 cases will require that school staff is adept in identifying and implementing appropriate accom-modations and modifications for students with ASD – likely requiring additional staff training and expertise. In addition, an increase in litigation around 504 protections is expected as families and schools struggle to identify what accommodations and auxiliary aids are required. For example, Section 504 does not mandate specific education programs or models nor does it require that stu-dents with ASD receive individualized instruction in specialized settings (Katsiyannis and Reid 1999). As this population ages, the demand for Section 504 protections at postsecondary settings, including universities, community colleges, and trade schools, will likely also increase. The resources required to implement these plans, both human resources and financial resources, may create new challenges for these institutions. Finally, employers will likely face similar chal-lenges in supporting employees on the autism spectrum protected by Section 504.
+
+#### Technology
+The use of personal and portable technology with individuals with ASD is on the rise (e.g., iPad, iPod, personal digital assistants, communication devices) (Mechling et al. 2009). These tools are often used to support processing, communication, self-management, self-care, independent func-tioning, and other “major life activities” (e.g., learning and working, per Section 504). It is not clear, however, whether provisions in Section 504 provide for the procurement/use of these devices, and this ambiguity is likely to be discussed and debated in upcoming years. Though Section 504 requires that auxiliary aids such as technology are provided to individuals with specific disabilities (i.e., hearing or vision impairments) at no addi-tional cost, there is no mention of such supports for individuals with broader developmental delays such as ASD or communication impair-ments as result of such delays. The fact that no funding is allocated to school districts, post-secondary institutions, or workplaces in associa-tion with Section 504 may further complicate the issue of providing technological supports for indi-viduals with ASD.
+
+#### Social Skills Instruction
+Similar questions are likely to arise around the issue of social skills instruction. Because socializing and/or social functioning is described as one of the major life activities under Section 504, accommo-dations and modifications in this area are recommended for individuals with ASD (Bellini et al. 2007). These may include peer-mediated strat-egies, direct social skills instruction, behavioral modification, self-management, and/or other evidence-based social skill strategies. Currently, however, the state of social skills instruction for individuals with ASD who do qualify under IDEA is bleak (Bellini et al.), and little is known about the status of this type of instruction for those who are protected under Section 504. It is safe to assume that services for this population would not exceed that of those who qualify under IDEA and likely also safe to assume that social skills services for the 504 protected group are close to nonexistent. As discussed above, as this population continues to increase, particularly a higher functioning group of students who may not receive services under IDEA, an increased focus on this type of instruction will fall to those implementing Section 504 plans.
+
+### See Also
+*   ▶Academic Supports
+*   ▶Americans with Disabilities Act
+*   ▶Employment
+*   ▶Individual Education Plan
+*   ▶Individuals with Disabilities Education Act (IDEA)
+*   ▶Toilet Training
+
+## 7q11.23 Duplications
+Stephan Sanders
+Child Study Center, Yale University, New Haven, CT, USA
+
+### Synonyms
+Williams-Beuren region duplication
+
+### Definition
+7q11.23 duplications are copy number variations (CNVs) in which an extra copy of 1,400 kb of DNA from the long arm of chromosome 7 is pre-sent. Duplications in this region are associated with “non-syndromic” ASD (Sanders et al. 2011). The region contains 26 genes, listed below, and is flanked by two segmental duplications (stretches of near-identical DNA). These are known to increase the likelihood of a process known as non-homologous allelic recombination, which can lead to gains or losses of the chromosomal segment flanked by these repeats and account for the com-mon breakpoints seen in the vast majority of indi-viduals carrying duplications in this region.
+
+7q11.23 duplications have also been seen in combination with intellectual disability, speech delay, and car-diac malformations (http://www.omim.org/entry/609757?search¼7q11.23&highlight¼7q1123). Reciprocal deletions at 7q11.23 cause Williams-Beuren syndrome characterized by dis-tinctive facial features, supravalvular aortic stenosis, and intellectual disability (http://www.omim.org/entry/194050?search¼7q11.23&highlight¼7q1123). Of note, these individuals also are known for highly sociable personalities. The distinctive phenotypes resulting from opposite changes in the number of copies of this region raise the intriguing possibility that the level of expression of a gene, or genes, within the 7q11.23 region plays a key role in the develop-ment and/or functioning of the social brain.
+
+The genes in the 7q11.23 region are ABHD11, BAZ1B, BCL7B, CLDN3, CLDN4, CLIP2, DNAJC30, EIF4H, ELN, FKBP6, FZD9, GTF2I, GTF2IRD1, LAT2, LIMK1, MLXIPL, NSUN5, RFC2, STX1A, TBL2, TRIM50, VPS37D, WBSCR22, WBSCR26, WBSCR27, and WBSCR28.
+
+### See Also
+*   ▶Candidate Genes in Autism
+*   ▶Chromosomal Abnormalities
+*   ▶Common Disease-Rare Variant Hypothesis
+*   ▶Copy Number Variation
+*   ▶DNA
+*   ▶Genetics
+
+## AACAP
+Fred R. Volkmar
+Child Study Center, Irving B. Harris Professor of Child Psychiatry, Pediatrics and Psychology, Yale Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
+
+### Synonyms
+AACAP practice parameters
+
+### Definition
+One of the first comprehensive guidelines to care of individuals with autism and related disorder, the Practice Parameters of the American Academy of Child and Adolescent Psychiatry first appears in 1999 (Volkmar et al. 1999) with recommenda-tions for ascertainment and screening, diagnosis, and clinical care. The second version (Volkmar et al. 2014) appeared 15 years later and provided updated guidance for practioners. The original version synthesized available evidence in making recommendations for care anticipating some of the findings and recommendations made by the National Research Council 2 years later (National Research Council 2001).
+
+The initial version was intended to aide in the diagnosis and care and treatment of individuals with autism and related disorder. It provided an overview of the assessment and treatment recom-mendations with an emphasis on evidence-based treatment practices based on available scientific research. It also noted the need for involvement of multiple care providers with attendant issues of care coordination and so forth.
+
+The second version was updated to reflect the considerable advances in research – particularly treatment research and practice. It focused more specifically on the strength of evidence available in support to the various recommendations in the decade and a half since the first version appeared. The second version explicitly differed in that it explicitly noted the strength of the rec-ommendation – ranging from clinical standard (rigorous evidence), clinical guideline (strong evi-dence), and clinical option (some but weak or emerging evidence) and not endorsed for treat-ments that appeared to have no little efficacy based on available research. Explicit distinctions were made based on the strength of the evidence ranging from randomized clinical controlled trials, controlled trials with nonrandomized assignment, uncontrolled trials, and case reports. Issues like care coordination and co-morbidity were also explicitly discussed. Many of the recommendations made were also consistent with the use of the medical home model of care (Hyman and Johnson 2012).
+
+These practice guidelines have many similari-ties and a few differences from other official guidelines, e.g., relative to issues of screening and early diagnosis; this guideline recommends early screen and encourages early diagnosis while others do not (see, Wilson et al. 2014; McClure 2014, for a discussion). Differences often largely come from the standards for levels of scientific evidence explicitly adopted by the formulators. As with all such official guides to care, rec-ommendations should be evaluated in light of current research and practice and the circum-stances of the individual case. With that, caveat attempts of this kind are most welcome as they provide clinical guidance for a range of care providers and provide basic recommendations for care.
+
+### See Also
+*   ▶Medical Home and ASD
+*   ▶National Guideline for the Assessment and Diagnosis of Autism Spectrum Disorders in Australia
+*   ▶Screening Measures
+*   ▶Sign Language
+
+## Aarskog Syndrome
+Marc B. Taub
+Southern College of Optometry, Memphis, TN, USA
+
+### Synonyms
+Aarskog-Scott syndrome; Faciogenital dysplasia
+
+### Definition
+Aarskog syndrome was first reported in 1970 by Aarskog in a seven-patient case series. The syn-drome is characterized by short stature with peculiar facies, “shawl” scrotum (the scrotal folds encircle the penis ventrally), cryptorchidism (the testis fails to descend into its normal position in the scrotum), and abnormalities of the hands and feet (Aarskog 1970). Aarskog syndrome can be inherited as an X-linked disorder caused by FGD1 mutations (Xu et al. 2010; Volter et al. 2014) or possibly in an autosomal dominant or recessive pattern (Xu et al. 2010). Population surveys estimate that Aarskog occurs in approximately 1 per million in the general population (Gorski et al. 2000). Intelligence ranges from normal to mild mental retardation. A normal IQ distribution has been found (Pilozzi-Edwards et al. 2011). Mild learn-ing difficulties and attention deficit hyperactivity disorder have been reported (Pilozzi-Edmonds et al.). Comorbidity has been documented with autism (Schwartz et al. 2000).
+
+Birth size is often normal. Alterations occur when individuals are 2–4 years old (Shalev et al. 2006). Until puberty, most patients are short with height at or below the third “centile.” Puberty is often delayed, but these patients do display a growth spurt in the late teens resulting in adult height in the low-to-normal range. Final height is around the 10th “centile.” Serum growth hormone levels are reported as normal and treatment with growth hormone is ineffective. Spina bifida occulta, cervical spine abnormalities, and scoliosis have been documented (Taub and Stanton 2008).
+
+The nose is often described as short and stubby, with a broad nasal bridge and anteversion of the nostrils. The ears are low set and protuber-ant. They are fleshy superiorly and referred to as “jug-handle ears.” Maxillary hypoplasia and dental malocclusion has been reported as well as a transverse crease below the lower lip (Taub and Stanton 2008). Associated ophth-almic conditions include hypertelorism, tele-canthus, blepharoptosis, and antimongoloid (downward) obliquity of the palpebral fissures. Ophthalmoplegia, strabismus, hyperopic astigma-tism, retinal vessel tortuosity, nystagmus, and Brown’s syndrome have also been reported.
+
+The hands and feet are affected by this condition in several ways. The hands are often short and broad with mild syndactyly (interdigital webbing) and/or brachydactyly (shortness in comparison to the other bones and body parts). Hyperextensible joints with concomitant flexion of the distal joints (-a hallmark sign), single palmer creases, and short medially incurved fifth fingers are also found. The feet are broad and flat with metatarsus versus short, splayed bulbous toes (Taub and Stanton 2008).
+
+Genital anomalies include a “shawl” scrotum, bilateral or unilateral cryptorchidism, and macro-orchidism (abnormally large testes). Inguinal her-nia (a condition in which part of the intestine bulges through a weak area in muscles in the abdomen, specifically the groin) has been found in association with the syndrome. No characteris-tic anomaly has been documented in females (Taub and Stanton 2008).
+
+There are no specific therapies for Aarskog syndrome. Some features may require surgical intervention (Orrico et al. 2007).
+
+### See Also
+*   ▶Genetics
+*   ▶Strabismus
+
+## Aberrant Behavior Checklist
+Cristan Farmer1 and Michael G. Aman2
+1The National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
+2Nisonger Center, UCEDD, The Ohio State University, Columbus, OH, USA
+
+### Abbreviations
+ASD Autism spectrum disorder
+DD Developmental disability
+
+### Synonyms
+ABC; ABC-C; ABC-R; Aberrant behavior checklist – community; Aberrant behavior checklist – residential
+
+### Description
+The Aberrant Behavior Checklist (ABC) is an informant rating instrument that was empirically derived by principal component analysis (Aman et al. 1985a). It contains 58 items that resolve onto five subscales. The subscales and the respective number of items are as follows: (a) Irritability (15 items), (b) Social Withdrawal (16 items), (c) Stereotypic Behavior (7 items), (d) Hyperactivity/Noncompliance (16 items), and (e) Inappropriate Speech (4 items). A total score for this instrument was not psychometrically derived and is not valid. The ABC was designed to be completed by any adult who knows the client well. This could be a parent, teacher, workshop supervisor, case worker, or informants in other roles. Depending upon reading ability, completion time varies, but most raters complete the ABC in 10–15 min the first time. Thereafter, rating times usually decline.
+
+A revised version of the ABC was published in 2017, along with a detailed manual (Aman and Singh 2017) and freely available annotated bibli-ography (https://psychmed.osu.edu/index.php/instrument-resources). With respect to the actual content of the scale, although the wording of a handful of items was generalized (e.g., references to “the ward” and “patients” have been altered), the meaning of all items remains the same as in the original version. Subscale titles similarly underwent slight changes; “Irritability, Agitation, Crying” is now entitled Irritability and “Lethargy, Social Withdrawal” is now Social Withdrawal. Finally, substantive changes to the face sheet were undertaken in an effort to create more usable data. More general terms for school and other set-tings were used to facilitate comparison, as such terms are variable over time and across geographic location. Rather than querying individual diagno-ses, the face sheet now requests explanations and, where relevant, severity about various conditions that might impact behavior (e.g., sensory or physi-cal impairments, developmental disabilities, medi-cal diagnoses). The remainder of the face page is unchanged from the previous version; the rater is asked to provide the client’s sex, date of birth, and the rater’s relationship to the client, and a listing of any medicines being used by the client. In the context of treatment studies, this information (other than the subject’s name and date) is often not collected.
+
+Instructions for completing the ABC and its 58 items are found on the second page of the instrument. The period over which informants rate the client defaults to 4 weeks. However, depending on the clinical or research needs, this period can be increased or decreased. The instructions ask the informant to rate the client on a scale ranging from 0 (not at all a problem) to 3 (the problem is severe in degree). Further, the instructions ask raters to take relative frequency into account, such that if a given behavior occurs more than the client’s reference group (e.g., other children of the same age and sex), scores greater than or equal to 1 are warranted. The instructions also encourage informants to consider observa-tions and reports of other responsible adults who know the client well when making their ratings. Finally, the instructions indicate that behaviors which interfere with the client’s development, functioning, and/or social relationships should be rated as a problem, even if these behaviors do not interfere with other people around the client. The 58 behavior items consume about 1½ pages of the form.
+
+Initially, the ABC was developed primarily as a measure of treatment effects, especially as an out-come measure for pharmacological intervention. With time, the use of the ABC has expanded, and it has been employed, fairly frequently, for the following applications: (a) to examine psycho-metric characteristics of other instruments and/or the ABC itself, (b) to study the behavioral pheno-types of individuals with genetic and metabolic conditions, (c) to examine the effects of different environmental variables (e.g., size of housing arrangements) on behavior, (d) to characterize the composition of subjects within studies and/or programs, (e) to assess the effects of sleep disrup-tion on client behavior, (f) to characterize individ-uals with different types of psychiatric disorders, and (g) to evaluate quality of life.
+
+There are at least 35 languages into which it has been translated, including the following: Afrikaans, Arabic, Chinese, Czech, Danish, Dutch, Filipino, Finnish, French (Belgian, Canadian, and European), German, Greek, Hebrew, Hungarian, Indonesian, Italian, Japanese, Korean, Lithuanian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish (Colombian, Mexican, Spanish, and USA), Swedish, Thai, Turkish, Telugu (regional language of Andhra Pradesh, India), Ukrainian, Urdu, Vietnamese, and Zulu. At the time of this writing, the following language translations were revised for compatibility with the 2017 ABC revi-sion: Afrikaans, Arabic, Canadian French, Euro-pean French, Chinese (Traditional), English (USA), Filipino, Hebrew, Kannada, Korean, Nor-wegian, Polish, Portuguese, Russian, Spanish (Spain and USA), and Urdu.
+
+In 2017, a single manual for the community and residential versions of the ABC replaced pre-vious separate versions (Aman and Singh 1986, 1994). This new manual addresses an array of subjects not covered in the original manuals, including sections on giving instructions to raters, practices to avoid, and using the ABC for charac-terizing change at the individual and group levels. The ABC-Second Edition Community/Residen-tial Manual (Aman and Singh 2017) gives the history of the ABC’s development and elaborates upon the meanings of all 58 items. Average sub-scale scores and standard deviations (normative data) are provided for adults, sourced from devel-opmental centers in the United States and New Zealand. Normative data for teacher ratings of children and adolescents in special educational classes are provided in the following formats: (a) T-scores and percentiles by sex and age, (b) T-scores with all ages and sexes combined, and (c) means and standard deviations broken out by age and sex, as well as collapsed across all ages. The group home norms are presented in the following ways: (a) T-scores and percentiles by age (10-year groupings) and functional levels (mild, moderate, severe, and profound intellectual disability); (b) T-scores and percentiles collapsed across functional level, summarized for age alone and for sex alone; and (c) means and standard deviations broken out by combinations of func-tional level and age and summarized by sex alone. Normative data for parent ratings of children and adolescents with intellectual disability are pro-vided as means and standard deviations broken out by age and sex. The manual is also a compre-hensive source for information on studies of the psychometric properties of the ABC, including internal consistency, interrater reliability, test-retest reliability, criterion group validity, concurrent and discriminant validity, and corre-spondence of ratings with direct observation scores. We summarize some of the information contained in the manual herein. There have been about 450 scientific studies conducted with the ABC, providing a rich literature against which new work can be compared.
+
+### Historical Background
+The development of the ABC grew out of a prac-tical need for an instrument to assess treatment effects in people with DD (e.g., Singh and Aman 1981). Development of the ABC was closely modeled on the Behavior Problem Checklist of Quay and Peterson (Quay 1977) and the enor-mously popular Conners’ Parent and Teacher Rat-ing Scales (Conners 1969, 1970). The initial form of the ABC contained 125 items, developed after a review of residential center case records, a survey of existing instruments, and consultation with direct care staff regarding content and wording. A pilot study obtained ratings from caregivers of 418 adolescents and adults with DDs. Items endorsed for fewer than 10% of subjects were dropped, and a principal factoring method was conducted with oblique rotation, leaving 76 items. The intermediate 76-item scale was then used to rate a new group of 509 adolescents and adults. The data from both samples were analyzed independently by a principal factoring method followed by oblique rotation. A five-factor solu-tion seemed most interpretable in both analyses. Items that failed to load on the same respective factors across analyses were deleted, leaving 58 items in the ultimate ABC.
+
+Two important subsequent changes took place more or less simultaneously. First, the original ABC contained some language that was distinctly institutional in flavor (e.g., “excessively active on the ward”). This language was modified in the early 1990s (e.g., “excessively active at home, school, work, or elsewhere”) to form what was then called the ABC-community. At about the same time, investigators assessed the ABC in child samples and found that the original factor structure was maintained for children and adoles-cents (e.g., Marshburn and Aman 1992; Brown et al. 2002). The earlier version of the ABC was dubbed the ABC-Residential to distinguish it from the newer ABC-Community. Thus, at this stage, there were residential and community ver-sions available, and the Community version’s structure was validated for children, adolescents, and adults.
+
+With time, the ABC came to be used more and more in pharmacological research involving peo-ple with intellectual disability and/or autism spec-trum disorders (ASDs). Other uses are described under Clinical Uses, below. Much of the published research with the ABC can be accessed through the Annotated Bibliography on the ABC (Aman 2015; available at http://psychmed.osu.edu/resources.htm). One important development was the adoption of the ABC’s Irritability sub-scale as the primary outcome measure by the Research Units on Pediatric Psychopharmacology (RUPP et al. 2002, 2005), a network of experi-enced psychopharmacology laboratories funded by the US National Institute of Mental Health. In two studies, the RUPP network showed that ris-peridone was highly effective in reducing agitated and irritable behavior for children and adolescents with autistic disorder chosen for high initial scores on the Irritability subscale. Using data from these pivotal investigations and from another clinical trial, Johnson & Johnson Pharmaceuticals obtained a clinical indication from the United States Food and Drug Administration for the use of risperidone in children and adolescents with autism and significant agitation and irritability. At that point, it was the only medication approved by the FDA for treating patients with autism.
+
+Subsequently, Bristol-Myers Squibb Company launched two pivotal clinical trials of aripiprazole in children and adolescents with autism and agi-tated/irritable behavior, again with the ABC Irri-tability subscale as the primary outcome measure. Bristol-Myers Squibb was also able to obtain a clinical indication for its product. These developments have made the ABC a popular choice as an outcome measure for the pharmaceutical industry when targeting behavior problems in patients with DD. However, it is important to realize that individual academic investigators were using the ABC long before it was adopted as an outcome by industry. In 2015, Bearss et al. published an experiment showing that psychosocial training, administered by par-ents of children with autism spectrum disorder, was highly effective in reducing disruptive behav-ior in the children as assessed by parent ratings on the Irritability subscale of the ABC. It seems probable that the ABC will be used widely in future to assess the impact of psychosocial treat-ments in children with DDs. As noted under Clin-ical Uses, below, the ABC has been used for approximately 450 pharmacological and non-pharmacological purposes over the last 30+ years.
+
+### Psychometric Data
+There is a wealth of psychometric data on the ABC.
+
+#### Construct Validity
+There have been several independent factor analyses with the ABC which have supported its construct validity (a) across versions of the ABC, (b) across settings (large residential vs. small, within the community), and (c) across age groups. Most of these studies have been referenced and summarized in the Annotated Bibliography on the ABC (Aman 2015; freely available at http://psychmed.osu.edu/resources.htm), and they are summarized in Table 1.
+
+As shown in Table 1, all studies of construct validity essentially verify the ABC factor structure as described in the original report (Aman et al. 1985a). Two studies failed to find the Inappropriate Speech factor in children, possibly because of a lack of participants with ASDs; it is worth noting that a very large study (n ¼ 1,893) of children with ASD demonstrated excellent support for the original fac-tor structure (Kaat et al. 2014). One study (Brinkley et al. 2007) found significant changes to the Irrita-bility factor when subjects with high rates of self-injury (SIB) were included, but the factor structure was confirmed when these subjects were excluded.
+
+#### Other Forms of Validity
+The original ABC development study included several validity com-parisons (Aman et al. 1985b). Concurrent validity was established through moderate correlations with existing standardized scales (e.g., the AAMD Adaptive Behavior Scale), and compari-sons of criterion groups yielded predictable pat-terns of difference (e.g., individuals who attended formal training activities received lower subscale scores than those who did not). Further, direct observations of the individuals in their residences were well-correlated with ABC scores. Finally, compared to unmedicated individuals, those prescribed psychotropic medications had signifi-cantly higher ABC scores on all domains except Repetitive Speech.
+
+Subsequently, numerous studies have demonstrated the validity of the ABC, and the manual cites about 35 studies addressing validity. Examples of this include concurrent validity between the ABC and other formal instruments, including (a) the Psychopathology Instrument for Mentally Retarded Adults, (b) the Nisonger Child Behavior Rating Form, (c) Conners’ Teacher Rat-ing Scale, (d) Diagnostic Assessment for the Severely Handicapped-II, (e) Reiss Screen for Maladaptive Behavior, (f) Stereotyped Behavior Scale, (g) Teacher Report Form, and (h) The ADD-H Comprehensive Teacher’s Rating Scale.
+
+#### Reliability Assessments
+Many researchers, especially those who conducted factor analysis with the ABC, reported alpha coefficients – a mea-sure of internal consistency. Generally, coefficient alpha ranged from the low 0.80s to the middle 0.90s, indicating a high level of consistency.
+
+#### Interrater Reliability
+Many of the studies that examined cross-informant reliability are summa-rized in Table 2. These generally fell into the low 0.50s to high 0.60s range, which is quite adequate for both research and clinical practice. Using criteria established by Cicchetti and Sparrow (1981), these reliabilities fall into the fair to good ranges.
+
+##### Aberrant Behavior Checklist, Table 1 Studies of the construct validity of the ABC
+
+| Authors | Residential/ community children/adults | Number of factors | % of items on same factor (mean factor loading) | Coefficient of congruence (mdn) |
+|---|---|---|---|---|
+| Aman et al. (1987a) | Res, Adults | 5 (Same) | 86% (0.58) | 0.88–0.96 (0.94) |
+| Newton and Sturmey (1988) | Res, Adults | 5 (Same) | 78% and 81%a | NR |
+| Bihm and Poindexter (1991) | Res, Adults | 5 (Same) | NR | NR |
+| Freund and Reiss (1991) | Comm, Childr | 5 (Same) (parent) | 91% | 0.88–0.82 (0.86) |
+| | Comm, Childr | 5 (Same) (teacher) | 80% | 0.65–0.91 (0.81) |
+| Rojahn and Helsel (1991) | Res, Childr | 5 (Same) | NR | 0.80–0.89 (0.82) |
+| Marshburn and Aman (1992) | Comm, Childr | 4 (1–4 Same) | 84% (0.65) | 0.87–0.96 (0.90) |
+| Aman et al. (1995) | Comm, Adults | 5 (Same) | 95% (0.59) | 0.84–0.97 (0.90) |
+| Ono (1996) | Res, Childr/Adults | 5 (Same) | 83% | NR |
+| Siegfrid (2000) | Comm, Adults | 5 (Same) | 84% (0.69) | NR |
+| Brown et al. (2002) | Comm, Childr | 4 (1–4 Same) | 71% (0.51) | 0.62–0.91 (0.85) |
+| Brinkley et al. (2007) | Comm, Childr | 5 (Same for low SIB subjects) | 78% | NR |
+| | | 4 (Subscales 2–5 same for high SIB subjects) | 60% | NR |
+| Sansone et al. (2012) | Comm, Childr/ Adults | 6 (1–5 same) | 76% | NR |
+| Kaat et al. (2014) | Comm, Childr | 5 (Same) | 90% | NR |
+| Wheeler et al. (2014) | Comm, Childr/ Adults | 5 (Same) | 97% | NR |
+| Same, same factor composition; NR, not reported; mdn, median value | | | | |
+| aUsing ordinal and dichotomous coding (absent/present), respectively | | | | |
+| bSpearman correlation coefficients | | | | |
+| cIntraclass correlation coefficients | | | | |
+
+##### Aberrant Behavior Checklist, Table 2 Summary of interrater reliability studies with the ABC
+
+| Authors | Sample size | Ages of subjects | Correlation range | Median correlation |
+|---|---|---|---|---|
+| Aman et al. (1985b) | (a) 35 | Adults | 0.54–0.67 | 0.59 |
+| | (b) 40 | Adults | 0.51–0.88 | 0.71 |
+| Aman et al. (1987b) | (a) 28 | Adults | 0.52–0.74 | 0.60 |
+| | (b) 28 | Adults | 0.40–0.66 | 0.59 |
+| Freund and Reiss (1991)a | 94? | Children | 0.39–0.49b | 0.45b |
+| Rojahn and Helsel (1991) | 130 | Children/Adolescents | 0.39–0.61 | 0.49 |
+| Ono (1996) | 33 | Children/Adults | 0.58–0.78b | 0.68 |
+| Schroeder et al. (1997) | 30 | Adults | 0.12–0.53 | 0.45 |
+| Siegfrid (2000)c | 90 | Adults | 0.67–0.90 | 0.73 |
+| Miller et al. (2004) | 22 | Children | 0.72–0.80 | NR |
+| All references can be found in Annotated Bibliography on the ABC (Aman 2015). Unless indicated otherwise, all correlations were Pearson correlation coefficients. Unless coded otherwise, raters had the same roles | | | | |
+| aParent-teacher agreement | | | | |
+| bSpearman correlation coefficients | | | | |
+| cIntraclass correlation coefficients | | | | |
+
+#### Test-Retest Reliability
+Several studies that examined test-retest reliability are summarized in Table 3. Median reliability ranged from the mid-0.60s to highs in the 0.90s. In general, test-retest reliably was quite high, falling within ranges characterized by Cicchetti and Sparrow (1981) as good to excellent.
+
+### Clinical Uses
+As noted, the ABC was developed as an outcome measure for pharmacological trials in people with developmental disabilities, and it has been used heavily for this purpose (see Annotated Bibliography, Aman 2015). However, use of the scale is not confined to research. The ABC can be used, in combination with other data-based approaches, to monitor the effects of routine clinical care in people with intellectual disabil-ities and/or ASD.
+
+Its early use was primarily among individuals with intellectual disabilities alone, but in recent years it has been used a great deal to assess treat-ment outcomes in individuals with ASD. This is supported by the available data; one large study (n ¼ 1,893) produced very strong evidence for the factor validity of the ABC when used in children and adolescents with ASD. However, it is worth

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+Historically, specialists in the field of education state that classroom management encourages the establishment of student self-control through positive achievement and behavior. Classroom management is closely linked to issues of motivation, establishing a climate of respect between classroom staff and students and also consistent discipline. The teacher is at a huge advantage when she or he spends the time to set up classroom management that looks at content management (skills that cut across subjects and activities; cf. instructional management skills, sequencing and integrating additional instructional activities, as well as instruction-related discipline problems [Kounin as cited in Froyen and Iverson 1999, p. 128]), conduct management (inclusion of human diversity into one’s instructional philosophy), and covenant management (classroom group and social systems). Research demonstrates that a high incidence of disciplinary problems in the classroom results in a significant impact on effectiveness of teaching and learning. Additional research indicates that strong consistent management and organizational skills lead to fewer classroom discipline problems (Johansen et al. (2011), www.intime.uni.edu/model/teacher/teac3summary.html). Throughout the years, classroom management has created areas of debate among teachers; however, it is widely recognized that a key component of classroom management is the application and implementation of behavioral approaches. Sulzer-Azaroff (1981 in Bijou and Ruiz, p. 64) stated the use of behavior modification in the classroom parallels the development of behavior modification in the field of mental health. The majority of early studies conducted in the 1960s focused on the reduction of disruptive behaviors by changing teacher behavior; however, this early application of behavior principles did not teach the students an alternative behavior. Careful consideration of research shared by Birnbrauer et al. (1965), Brigham and Sherman (1968), and Buell, Stoddard, Harris, and Baer (1968) yielded the need to focus on using behavioral procedures to teach students in a way that classroom productivity, language development, and social skills were promoted (Sulzer-Azaroff 1981 in Bijou and Ruiz 1981, pp. 65–67). Subsequent research has contributed to a growing body of research that supports positive classroom management through the use of modeling behavior expectations and differential reinforcement procedures (Sulzer-Azaroff and Mayer 1986). Throughout the years, this research has become more refined and focused on a variety of needs that are represented in the learning characteristics of students with autism spectrum disorders (ASD).
+
+The current trends in education emphasize the establishment of positive behavior supports (PBS) and the use of positive behavioral interventions and supports (PBIS) to achieve socially important change (Sugai et al. 2000). The application of positive behavior supports (PBS) and positive behavioral interventions and supports (PBIS) is supported by the US Department of Education and Office of Special Education Programs (OSEP), with an emphasis for schools to use the PBIS framework to impact social, academic, and emotional outcomes for students with disabilities (https://www.pbis.org). Although the use of such systems is best practice, students with ASD present unique characteristics within a learning environment. The teacher is challenged to incorporate these unique learning needs into meaningful classroom management and instruction. To do this, the teacher must take into account the needs of the learner in a variety of educational settings. These settings necessitate careful thought about physical structure, instructional management, the student’s ability in the areas of communication and social skills and the need to teach the student how to learn under a variety of conditions.
+
+Classroom management for the student with ASD should include consideration of the following aspects of instruction: 1. Physical space in the classroom needs to be set up with clearly defined areas

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+identifying two bipolar dimensions: “destructive-nondestructive” behaviors and “overt-covert” behaviors. They concluded that most conduct problems could be classified within these two orthogonal dimensions. The overt-nondestructive cluster reflected the criteria for ODD, whereas the other three clusters, with features more indicative of property and status violations, represented CD symptoms. For diagnosis of CD, the individual must have presented in the past 12 months “a repetitive and persistent pattern of behavior in which the basic rights of others or major age-appropriate societal norms or rules are violated” (APA 2013). Behaviors that must be present fall into the following four categories of aggressive behavior:
+1. Aggressive acts toward people or animals
+2. Destruction of property
+3. Deceit or theft
+4. Violation of rules
+At least one of the above criteria must have been presented during the last 6 months. Moreover, the disturbance in behavior must cause clinically significant impairment in social, academic, or occupational functioning. The age of onset determines the type of CD, and the severity depends on the damage caused to others. The behaviors that characterize a mild severity include lying, truancy, and running away at night without permission. Moderate severity may include acts of vandalism and theft without confrontation. Severe cases include forced sex, physical cruelty, and use of a weapon and breaking and entering.
+
+Recent studies indicate that a significant proportion of children with ASD may also be characterized with ODD symptoms. For example, two recent studies found that the percentage of children with ASD who meet DSM-IV criteria for ODD is 13% in the age group of 3–5 years and 27% in the age group of 6–12 years according to parental ratings, and when rates from teachers were considered, the numbers were 21% and 25%, respectively (Gadow et al. 2004, 2005). Problem behaviors that are most often identified in individuals with ASD are physical aggression, self-injury, destruction of property, arguing nature, temper tantrums, and disruption. But other behaviors, such as explosive behavior, running away, stubbornness, violation of rules, defiant, threatening, or not to accept being guilty, have also been identified as moderate or severe conduct problems in people with ASD (Lecavalier 2006). Individuals with ASD present more problem behaviors than typically developing children, and overall levels of problem behavior are positively correlated with severity of ASD (Matson et al. 2009). About one third of individuals with intellectual disability (ID) who exhibit problem behavior have comorbid diagnosis of ASD (Myrbakk and von Tetzchner 2008), and the more severe the ID, the greater the risk of problem behavior (Holden and Gitlesen 2006). A common way of categorizing problem behaviors in people with ASD is based on the function of these behaviors in their natural context, but there are no systematic reviews about the possible functions that may play a role. In addition, a behavior problem may have more than one function. The most common functions that can be found in the literature are attention-seeking, avoiding, tangible benefit, or being alone (i.e., nonsocial, self-stimulatory, or automatic rein-forcement). Avoiding pain or discomfort has also been described as a possible function of problem behavior.
+
+### Epidemiology
+
+Rates of prevalence estimates of CD vary widely depending on the methodology used in each study and on the ascertainment procedures. The disorder is considered to be a common mental health problem in children and adolescents and appears to have increased in the recent years. CD may be higher in urban than in rural areas and appears more often in boys than girls. Prevalence rates for the disorder in childhood and adolescence range from 2% to 10% in nonreferred samples (APA 2013). Prevalence rates increase from infancy to adolescence and are higher in males than in females. Recent studies show prevalence rates of 9.5% (12% for males and 7.1% for females) (Nock et al. 2006). It seems that male-female ratios might be stronger in childhood than in adolescent-onset groups.
+
+Although individuals with ASD often exhibit behavior problems that could have a negative impact in everyday activities, and several studies suggest a high prevalence of aggressive behavior in people with ASD, few studies have examined the prevalence of maladaptive behaviors that warrant a clinical diagnosis of CD in individuals with ASD. Prevalence of problem behaviors within the ASD population is relatively high. Most studies indicate that at least half of the people with ASD exhibit behavior problems, with an estimated prevalence ranging between 35.8% and 94.3% (Kozlowski and Matson 2012). Lecavalier (2006) presented a study on prevalence of problem behaviors of children and adolescents with ASD. In a sample of 303 children and adolescents with ASD, he found that the proportion of children and adolescents who, according to parents and teachers, showed “conduct problems” was 13.9% (parents) and 8.4% (teachers). Behavior problems rated by parents and teachers as more frequent were stubbornness, temper tantrums, defiant behavior, arguing nature, not to accept being guilty, and explosive behavior. Stubborn behavior was rated as a moderate or severe problem for 50.7% (parents) and 44.4% (teachers). Also, temper tantrums, defiant behav-ior, not to accept being guilty, and explosive behavior were classified with a high frequency. Finally, aggressive acts, such as attacking others, were observed by teachers in 14.3% and in 9.9% by parents. Both informants (parents and teachers) indicate physical fights as moderate-to-serious problem for 5.3% of the sample. Property destruction was a moderate or severe problem for 11–12% (according to information from parents and teachers, respectively), and threatening people was rated as a moderate-to-severe problem for 4.5% (parents) and 7.6% (teachers) of the sample. The study also found that lower adaptive skills were associated with greater problem behaviors among the sample. Regarding high-functioning individuals with ASD, research and clinical observations suggest that a relatively large number of these individuals have behavioral problems at some point in their development. These symptoms may indicate the presence of ODD comorbid with ASD. They may also have symptoms of CD that are more severe in school-age time than in preschool (Gadow et al. 2005).
+
+### Natural History, Prognostic Factors, and Outcomes
+
+The onset of CD can occur very early, even at preschool age, although the most obvious symp-toms usually occur between middle childhood and middle adolescence. Onset is rare after age 16. ODD is a common precursor to the childhood-onset type. Other different factors affect the onset of symptoms of CD. The scientific literature high-lights three main factors:
+1. Personal characteristics such as a difficult tem-perament in early childhood, a callous-unemotional personality style (lack of empa-thy, remorselessness, and shallow affected), propensity for risk-taking, low to threatening and emotional reactions stimuli, reduced sen-sitivitity to cues of punishment, and low levels of conscience and moral development
+2. Bad parenting practices such as harsh, puni-tive, abusive, and/or inconsistent discipline
+3. Repeated peer rejection and socializing with a deviant peer group (Hughes et al. 2008.
+In addition, there is evidence suggesting that childhood-onset CD could be more related to per-sonal and familial factors, whereas adolescent-onset could be more related to exposure to deviant peers and environmental disadvantages associated with ethnic minority status (McCabe et al. 2001). Finally, CD in childhood is associated with other problems, including the likelihood of repeating a grade in school, being suspended or expelled from school, an earlier age of onset of alcohol depen-dence, and having to attend a greater number of treatments for drug abuse (Hughes et al. 2008). There is not a single risk factor that determines the onset of the CD. Experts emphasize that the mul-tiple risk factors mentioned above interact to facil-itate and perpetuate the disorder.
+
+Problem behaviors play a critical role in ASD. However, the heterogeneity of symptoms present in persons with ASD (differences in cognitive functioning, or in adaptive behavior, the nature and severity of autistic behaviors) and changes in the development difficult to understand how these individual differences affect the occurrence and presentation of behavior problems beyond the core symptoms that define the ASD population. Despite the differences, consequences of prob-lem behaviors are similar in most cases. These behaviors prevent the development of social rela-tionships (Matson et al. 2010; Myrbakk and von Tetzchner 2008), place the individual and their family members in very difficult situations, and interfere with effective education (Carr et al. 1991). Also, it has been shown that the fact of problem behaviors (specially aggressiveness) causing more distress to caregivers than the core autistic symptoms (Lecavalier et al. 2006) is one of the most important impediments to placement in less-restrictive environments (Shoham-Vardi et al. 1996), can also interfere with intervention efforts, and, if present during early childhood, is of particular concern given that these are critical years for intervention. Thereby problem behav-iors impact the long-term prognosis. Research on risk factors has provided some important data. Tonge and Einfeld (2003) studied a group of 118 children with autism in a period of 8 years. Results indicated that 73.5% of children with autism had behavioral alterations in a clini-cally significant range, with scores fairly stable over time. These researchers reported that chil-dren and adolescents with ASD are at high risk of severe and persistent behavioral disturbances beyond those that define the disorder. Matson et al. (2009) have studied the potential causal factors of problem behaviors in children with ASD, showing that overall levels of problem behavior were positively correlated with severity of ASD (Matson et al. 2009). Lecavalier (2006) in his epidemiological study found that lower adaptive skills were associated with greater behavioral problems, but age and gender do not seem to influence behavior problems. In a recent study, Hartley et al. (2008) examined a large sample of children with ASD, classifying 27% of sample in the clinically significant range on the CBCL Externalizing Problems’ subscale, and 22% fell within the clinically significant range on the aggression subscale. Results indicated that exter-nalizing problems were significantly correlated with poorer adaptive skills, lower nonverbal cog-nitive functioning, and poorer expressive lan-guage. Also Dominick et al. (2007) found that individuals with ASD with low cognitive func-tioning and adaptive behavior and with low-expressive language skills exhibit more problem behavior than high-functioning individuals (Dominick et al. 2007).
+
+### Clinical Expression and Pathophysiology
+
+CD may manifest itself in various symptoms that are classified into four categories: aggression toward people or animals, destruction of property without aggression, deception or theft, and serious violation of the social rules. These symptoms are behaviors that usually occur in early childhood. Many children commit acts of aggression, break property of others, commit petty thefts, say some lies, and violate some social rules. But in the case of children who have a CD, all these behaviors are very frequent and persistent, and some appear in an age too early such as running away from home at night (with no objective reasons to escape such as being abused) or truancy before age 13. The clinical expression of problem behaviors in ASD will depend on the subject’ age and on whether it is associated with ID individuals with greatest deficits engaging in more severe problem behaviors. Severity of ASD symptomatology affects the severity of problem behaviors. Also, symptoms of other disorders such as attention-deficit hyperactivity disorder or obsessive com-pulsive disorder will affect the clinical expression of conduct problems in people with ASD. But it remains unknown whether the comorbidity of ASD with these disorders leads to different clini-cal expressions of behavior problems. This is important to better understand the pathophysiol-ogy of CD (and also of ASD). The neurobiologi-cal disorder of ASD results in difficulties in social cognition with its own characteristics, such as the difficulty to infer mental states and recognize (e.g., intentions, beliefs, desires, etc.) in self and others, which can interact with environmental fac-tors leading to atypical behavioral patterns and behavior problems. Some CD symptoms (such as physical aggres-sion, lying, and stealing) are relatively common in early childhood, and to distinguish them from normal childhood behavior, the clinician must take into account the frequency and persistence of problem behavior beyond the age of four. In childhood, most of the manifestations are limited to family and school contexts, but they affect the overall functioning of the child. In adolescence, behavior problems tend to have more serious con-sequences encompassing the whole adolescent’s social setting and including behavior problems that are much more serious.
+
+### Evaluation and Differential Diagnosis
+
+CD is a complex problem affecting multiple domains of functioning and often showing a high rate of comorbidity with other disorders. Assessment requires a comprehensive approach encompassing the child, family, school, peers, and community factors. Well-trained profes-sionals should conduct assessments, to ensure the proper treatment. Otherwise, the behavior problems will continue or even worsen. Language disorders and intellectual disability usually found in ASD complicate the assessment and diagnosis. Even mild deficiencies in language can make challenging the study and identification of subtle differences in emotions, health status (presence of pain or physical discomfort), or feel-ings of annoyance at being unable to control own decisions. Assessment requires combining qualitative and quantitative methods to obtain information, and collecting data from several sources such as parents, teachers, and peers is mandatory. Infor-mation from different sources should be compared to detect and prevent possible biases caused by the partial view of each reporter and for a better understanding of the disorder being evaluated. Behavior rating scales can be completed by parents, teachers, and children to obtain compara-ble information. Clinicians and researchers con-sider behavior rating scales a time-efficient method of collecting reliable information and pro-viding an assessment of several domains of behavior, usually both on the healthy functioning and the maladaptive. Behavioral observation is a useful tool that is normally used in assessing problem behaviors, both to describe the function (i.e., functional assess-ment) of problem behavior and to monitor treat-ment progress. Functional assessment is the process of identifying the variables that predict and maintain problem behavior. The literature review on the treatment of problem behaviors sug-gests that interventions based on functional assess-ment are more likely to produce the reduction of problem behavior (Horner et al. 2002). The process of conducting a functional assessment typically involves the following: (1) identifying the problem behavior, (2) building hypotheses about the events that occasion and maintain problem behavior, (3) testing/confirming the functional hypothesis, and (4) designing an intervention based on the confirmed information (Horner et al. 2002).
+
+### Treatment
+
+A variety of treatment procedures have been devel-oped for children and adolescents with CD, but only some have been shown to reduce CD behaviors. Intervention procedures seem to be more effective in children under 8 years, when behavior problems have recently begun and when it includes a multimodal and multicomponent strategy, specifically adapted to the individual needs (Hughes et al. 2008). The evidence-based recommendations empha-size the need for a multicomponent intervention aimed at prevention and early intervention. The treatment mainly consists of psychological, edu-cational, and social interventions focusing on chil-dren, parents, and teachers. Psychotropic medication and applied behavior analysis are the most frequently used treatments. Child-directed interventions aim to improve skills to manage anger and control of aggressive impulses, and improve empathy with others, strengthening relationships with peers. With the family it is necessary first to ensure commitment and motivation and then to begin training within the family context and subsequently generalized to other places of the community (INSERM 2005). Pharmacological treatment is appropriate when used in the context of a comprehensive psychoeducational evaluation and provided as part of a global treatment strategy (Connor 2002). The best practice for psychoeducational treat-ment of behavior problems should be based on principles and methods of positive behavior sup-port (Rogers and Vismara 2008). That is, it should be a treatment that uses functional assessment to determine the function (or functions) of problem behavior. After identifying the function, a positive behavior support plan must be designed and implemented, aimed at teaching new functional behaviors that serve to replace the problem behav-ior. Horner et al. (2002) suggest that a support plan should take into account several elements, and a few of those are as follows:
+1. Prevent behavior problems by organizing the environment in order to experience less nega-tive events and greater accessibility of reward-ing activities.
+2. If there are behavioral problems, conduct a functional assessment.
+3. Build a behavioral intervention to make the problem behavior irrelevant, by teaching socially appropriate behaviors that make the person much more competent in the context and produce the same effect in the context of the problem behavior.
+4. Organize the consequences for appropriate behavior to compete with problem behavior, avoiding also the reinforcement of problem behavior.
+5. Ensure that the procedures are within the skills, resources, and values of those who must implement them.
+
+Parent training based on principles of applied behavior analysis has great empirical support. Successful programs for individuals with ASD and problem behavior include a parent training component usually based on principles of applied behavior analysis. Common parent training ele-ments include teaching behavioral principles and management techniques, role-playing, homework assignments, teaching play and social skills, use of visual schedules, and home visits or telephone consultation, among others. Pharmacotherapy is common among individuals with ASD with behavior difficulties. The most used agents include selective serotonin reuptake inhibi-tors, antipsychotics, alpha 2 adrenergic agonists, psychostimulants, and anticonvulsants. But empiri-cal support for use of these agents in ASD varies widely. A classic study on the use of Risperidone (McCracken et al. 2002) concluded that this psy-choactive drug is effective and well tolerated for the treatment of tantrums, aggression, or self-injurious behavior in children with ASD, although the short period of the trial in this study limits inferences about adverse effects. Risperidone, like other atyp-ical antipsychotics, is associated with adverse events, such as weight gain, and the subsequent risk of metabolic syndrome. A recent trial by Aman et al. (2009) tested whether combined treat-ment with risperidone and parent training in behav-ior management is superior to medication alone in improving severe behavioral problems. Results indicated that parent training plus medication pro-duced greater reduction of problem behavior than medication alone in children with ASD.
+
+Confidentiality
+
+### Definition
+
+Confidentiality refers to a general standard of professional conduct, as well as, in some cases, the legal requirement for providers and/or researchers to maintain the privacy of a patient’s personal health information and records unless consent to release the information is obtained from the patient. In the case of minors, this con-sent may be obtained from the patient’s parent (s) or legal guardian(s), and rules around confi-dentiality related to minors may vary by state.
+
+### Historical Background
+
+Confidentiality is important in order to establish trust between providers and patients and to protect patient privacy. Confidentiality can be bound by both ethical and legal standards. For example, the American Psychological Association’s Ethical Principles of Psychologists and Code of Conduct provides ethical guidelines regarding a psycholo-gist’s obligation to take reasonable precautions to protect confidential information related to the patient (Standard 4; APA 1992). In 1996, The U.S. Department of Health and Human Services (HHS) established the Health Insurance Portabil-ity and Accountability Act (HIPAA), Public Law 104–191, and issued a Privacy Rule to implement the legal requirement for providers to protect the privacy of a patient’s medical records or personal health information, including information related to mental health. In the case of minors, according to HIPAA, parents have the legal right to access their child’s records and personal information. In certain specific situations, providers may share information without the patient’s consent. Com-mon exceptions may include cases that involve a court order to release protected information, cases in which there is suspicion of domestic violence, abuse or neglect of children, the elderly, or people with disabilities, or in order to protect the patient or the public from serious harm (Tarasoff 1974, 1976).
+
+### Current Knowledge
+
+Since it was created, the HIPAA has undergone several revisions and updates in order to further protect the confidentiality and privacy of health information, including electronic protected health information (PHI). In 2002, the HHS modified the Privacy Rule associated with HIPAA to set national standards for the protection of individu-ally identifiable health information by health plans, health care clearinghouses, and health care providers. In 2003, HHS published a final Secu-rity Rule to set national standards for protecting the confidentiality of electronic PHI.
+
+### Future Directions
+
+Confidentiality remains a sensitive and, at times, controversial topic, particularly as it relates to determining exceptions or limitations to confiden-tiality, such as in the treatment of minors or when the risks may outweigh the benefits (e.g., Tarasoff 1974, 1976). Some have proposed guidelines and recommendations related to dealing with issues of confidentiality when working with various populations (APA 1992; Gustafson and McNamara 1987; Koocher and Keith-Spiegel 1990). Future investigations may focus on further delineating the ethical and legal boundaries and limits around cases of confidentiality. Furthermore, research should explore the important considerations to make with regards to assessing competence and respecting confidentiality in individuals with autism spectrum disorders and other developmen-tal disabilities.
+
+Congenital Disorders
+
+### Definition
+
+Congenital disorders are those disorders that are present at the time of birth and involve an abnor-mality of structure and/or function that has arisen during development. Congenital disorders are not necessarily genetic though do include genetic dis-orders. All genetic disorders are congenital as they are present at birth even if they are not yet detected at birth. Congenital disorders may arise as a result of the intrauterine environment, errors in embry-onic development, and infections. The outcome of such disorders varies widely and is dependent upon the disorder itself and the availability of possible postnatal treatments. Examples of con-genital disorders include diseases such as cystic fibrosis, physical anomalies such as having a sixth finger on the hand, metabolic diseases such as congenital adrenal hyperplasia, and trisomy 21 which is also known as Down syndrome.
+
+Conners’ Continuous Performance Test
+
+### Description
+
+The Conners’ Continuous Performance Test is an attention test for research and clinical settings (Conners 1995). It is used for measuring pro-cesses related to vigilance, response inhibition, signal detection, and other aspects of performance (Conners et al. 2003). The test is presented in a game-like format where 360 letters (approximately 1 in. in size and bold faced) appear on the computer screen, one at a time, for approximately 250 ms. Respondents are required to press the space bar or click the mouse button when any letter except the letter “X” appears on the screen (Conners and MHS Staff 2000). The CPT-II standard paradigm consists of six blocks, with each block divided into three 20-trial sub-blocks. Each sub-block has a separate inter-stimulus interval (i.e., the time in between the letter presentations). The inter-stimulus intervals (ISIs) are 1, 2, or 4 s. The order of the three different ISI conditions varies from block to block (Conners et al. 2003). The CPTII can be completed in 14 min. The test can be administered to participants 6 years of age and above. After the test session, the program generates a report that includes response times, omission errors (i.e., when a response is not given after a non-X appears on screen), commission errors (i.e., when a response is given after an X appears on screen), change in reaction time speed and consistency as the test progresses, and change in reaction time speed and consistency for different inter-stimulus intervals. Examination of the results by blocks and varying ISIs allows for the assessment of vigilance and the ability to adjust to changing tempo and task demands (Conners and MHS Staff 2000).
+
+### Historical Background
+
+The continuous performance test was first intro-duced by Rosvold and colleagues in 1956 (Spreen and Strauss 1998) to detect lapses of attention in patients with petit mal epilepsy. In this early ver-sion, the participants were required to press a key in response to a rare target, such as the letter “X.” Subsequent CPTs have made changes to this orig-inal paradigm including having the participants press a key when the target letter is preceded by another letter (e.g., “X” preceded by “A”) or upon the second successive presentation of a letter (e.g., S-S). There have also been variations with regard to modality (i.e., visual or auditory), the type of stimuli (e.g., letters, numbers, colors, or geometric figures), and the type of data that are evaluated (e.g., omissions, commissions, inter-stimulus interval, measures of sensitivity; Spreen and Strauss 1998). Conners’ introduction of his version of the CPT in 1995 represented a departure from the more traditional CPT paradigm. In the earlier ver-sions, participants typically sit passively while observing the presentation of nontarget stimuli and must respond to the occasional target stimulus (usually an “X”). In Conners’ version, which is also sometimes called the “not-X” CPT, partici-pants are asked to press a button on each trial (usually letters), except for the letter X. Barkley (2006) notes that this task requires a different form of response inhibition. Conners et al. explain that Conners’ “not-X” CPT places a greater demand on response inhibition due to the frequent responding interrupted by the occasional nontar-gets (the less probable “X”) as opposed to the more passive responding of the conventional “X” task. Conners has since come out with an updated version of his CPT, the Conners’ Continuous Per-formance Test (2nd ed.; Conners’ CPT-II; Conners and MHS Staff 2000). The updated ver-sion differs from the previous version in that it is based on new and expanded norms that include a large subsample of neurologically impaired indi-viduals. This allows for comparison of responses to general population norms, ADHD norms, and neurologically impaired norms. The program itself includes validity checks to flag certain con-ditions that may adversely affect CPT II adminis-tration and a Confidence Index that enables the practitioner to gauge the certainty of the assess-ment/classification.
+
+### Psychometric Data
+
+The CPT-II normative data included 2,521 partic-ipants. Of this, 1,920 were healthy individuals from the general population, 378 were diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD), and 223 were adult individuals identi-fied with some type of neurological impairment (e.g., head injuries, dementias). Normative data were collected from 30 sites in 16 states and three Canadian provinces. The multi-site, nonclinical data came from schools, organizations, science centers, and controlled research settings. The norms were divided into eight age groups. For children of ages 4 through 17, norms were pro-vided in 2-year increments. For adults aged 18 and older, they were divided into three age groups (18–34, 35–54, and 55 +). The applicability of CPT-II norms to Asian and African American groups was also assessed. Scores for the Asian group were consistent with those obtained in the general population. However, the African Ameri-can group made slightly fewer commission errors than the general population, and showed slightly better discriminatory power as measured by the statistic d prime. Overall, the general population norms were reportedly applicable to these minor-ity groups. In fact, there were no significant dif-ferences on the overall profile indexes (Conners and MHS Staff 2000).
+
+Three types of reliability information were provided on the CPT-II manual: Split-half reli-ability, test-retest reliability, and standard error of measurement (Conners and MHS Staff 2000). The split-half reliability information from the original CPTwas cited. These appeared adequate and ranged from 0.66 to 0.95. Test-retest reli-ability was obtained using 23 participants in the standardization of the CPT-II. The average inter-val between administrations was 3 months. The test-retest reliability estimates ranged from 0.05 to 0.92 with most of the variables showing good consistency across administrations. However, the Block change and ISI change statistics have low test-retest correlations, suggesting that these variables do not produce good consistency across administrations. When measures are com-bined into indices for ADHD and neurological assessment, the test-retest reliabilities were excellent, 0.89 and 0.92 respectively. Using the same test-retest data, it was also demonstrated that the CPT-II had no significant practice effect. In addition, information on standard error of measurement and standard error of prediction for the various CPT-II measures across gender and age was presented. Conners and the MHS Staff (2000) cited research to support the clinical utility of the CPT. In a study based on the original standardiza-tion sample, significant differences were seen between the ADHD group and other diagnoses across most of the CPT variables. The ADHD group responded more slowly, had greater vari-ability of reaction times, made more omission and commission errors, and was more affected by changes in ISI. In similar analyses using the updated CPT-II data, no significant difference was observed between ADHD and nonclinical groups; for all other analyses, there was a large and significant difference between ADHD and nonclinical groups with the ADHD groups performing worse on all of the measures. For the adults aged 18 years and older, planned compari-sons were done to see if the nonclinical group differed from the clinical groups, and if the two clinical groups differed from each other. As pre-dicted, the clinical groups performed significantly worse than the nonclinical group. Compared to the ADHD group, the Neurological group made significantly more omission errors, had signifi-cantly slower reaction times, and was significantly less consistent across the interstimulus intervals.
+
+### Clinical Uses
+
+The CPT paradigm has traditionally been included in evaluations for ADHD. Barkley (2006) states that, “A wide-ranging literature has shown it to be the most reliable of psychological tests for discriminating groups of children with ADHD from nondisabled children” (p. 377). Spreen, Risser, and Edgell (1995) report that on a continuous performance task hyperactive chil-dren make more errors of omission and of com-mission, show more rapid deterioration in performance than controls, and are less able to inhibit premature or repetitive responding, indi-cating poor impulse control. Lezak, Howieson, and Loring (2004) state that on the CPT, adults with ADHD have a high reaction time variability and higher rate of commission errors than con-trol subjects, which suggests that they have trou-ble inhibiting responses. According to Spreen and Strauss, the CPTs have also been shown to distinguish between normal controls and certain patient groups including adults with head inju-ries and children with conduct disorder, learning disabilities, and those at high risk for schizophre-nia. In addition, Barkley and Spreen and Strauss report that CPTs are sensitive to stimulant drug effects among children and adolescents with ADHD.
+
+Barkley has raised some concern about the diagnostic utility of the Conners’ CPT, in particu-lar, in ADHD assessments. Citing one study that investigated associations between Conners’ CPT scores and several other measures, including par-ent and teacher ratings as well as neuropsycholog-ical and achievement tests, Barkley reported that the Conners CPT’s overall index failed to relate to parent and teacher ratings. In addition, only half of those participants who met criteria for ADHD “failed” the CPT. Barkley also reported poor dis-criminant validity, in that children with a reading disability actually performed more poorly than children with ADHD. In another study on the ecological validity of the CPT-II in a school-based sample, Barkley cited findings showing nonsignificant relationships between CPT perfor-mance and three other kinds of measures (parent ratings, teacher ratings, and classroom observa-tions). He also reported negative correlation between IQ and omission errors on the CPT-II, suggesting that the CPT-II may measure letter recognition skills or phonological awareness rather than impulsivity or inattention per se. Despite these concerns, Barkley still holds that the CPT is the only psychological measure that seems to directly assess the core symptoms of ADHD, namely, impulsivity and attention. How-ever, he warns that if a child performs well on this measure, it does not indicate that the child is nondisabled or without ADHD because of the high rate of false negatives (i.e., children who are rated by parents and teachers as having ADHD, but who obtain average scores on the test) associated with CPTs. He joins Conners (2000) in reminding the clinician that the test pro-vides one source of information to be integrated with other sources (e.g., self-report data, observer-based data, historical information, interview data, and results from other tests) in reaching a final diagnostic decision.
+
+The use of continuous performance tests in assessing children with autism spectrum disorders is less common. Three studies were found but none of them used the Conners’ CPT-II. In the first study, 23 children with autism were compared with two control groups (one matched based on verbal men-tal age and another based on performance mental age) on several attention measures including three versions of the traditional CPT paradigm (Pascualvaca et al. 1998). The results showed that none of the CPT versions differentiated between the groups. In the second study, Schatz, Weimer, and Trauner (2002) explored the use of the Test of Variables of Attention (TOVA) in assessing atten-tion deficit symptoms in a group of eight children and young adults with Asperger’s Syndrome (AS). The TOVA is a continuous performance test that is similar to the Conners’ CPT but with an additional auditory component. Five of eight subjects with AS received scores that suggested the presence of an attention deficit, whereas only two of eight subjects received scores suggestive of an attention deficit. The authors looked at this as a pattern that could be explored using a bigger sample. In the third study, Corbett and Constantine (2006) compared children with autism spectrum disorder (ASD) with those that have been diagnosed with ADHD and typically developing children using the Integrated Visual and Auditory Continuous Performance Test, another version of a CPT. They found that children with ASD show significant deficits in visual and audi-tory attention and greater deficits in impulsivity than children with ADHD or typically developing children. The authors note that the findings suggest that many of the children with ASD demonstrate significant ADHD-like symptoms. They point out that this study adds to the growing literature that calls into question the current exclusionary practice of offering a diagnosis of ADHD in pervasive developmental disorders. Two other variables that might be relevant in the performance of individuals with autism spec-trum disorders on the CPT-II are anxiety and intelligence. Conners and the MHS Staff pre-sented data showing that anxiety may affect a participant’s CPT-II response style and lead to response inhibition for physiological anxiety, and decrease in response inhibition for cognitive anxiety. In terms of intelligence, the manual included two studies that showed nonsignificant correlations between IQ as measured by the WISC and CPT performance. Still it was noted in the manual that some individuals with severe cogni-tive impairment, agitation, or severe psychotic symptoms cannot be administered the CPT-II.
+
+Conners’ Parent Rating Scale
+
+### Description
+
+The Conners’ Parent Rating Scale (CPRS) is a parent-report measure that assesses children’s problem behaviors, particularly symptoms of attention deficit hyperactivity disorder (ADHD) and related disorders (including oppositional defi-ant disorder and conduct disorder). At the time of publication, the Conners 3-P (2008) is the current version of the CPRS. Parents of children with autism spectrum disorders may be asked to com-plete the Conners 3-P because of the shared symp-toms between ADHD and autism spectrum disorders. The Conners 3-P was developed by C. Keith Conners, Ph.D., who also designed two related measures: the Conners’ Teacher Rating Scales (CTRS), a teacher-report measure, and the Conners’ Self-Report Scales (CSRS), a self-report measure for children and adolescents. Because these measures are meant to be used in conjunction, the family of Conners’ tests is con-sidered to be a “multi-informant” mode of assess-ment. This is valuable because it can yield information about children’s behaviors in multiple settings. For example, asking a parent to complete the Conners 3-P and asking a teacher to complete the Conners 3-T (for “teacher”) can shed light on how a child’s behavior may differ between home and school.
+
+### Historical Background
+
+Gianarris, Golden and Greene (2001) provide a detailed overview of the multiple versions of the Conners’ Parent Rating Scales. The roots of the Conners 3-P date back to the 1960s, when C. Keith Conners, Ph.D., created behavior rating scales based on his multiple observations of chil-dren and adolescents with behaviors consistent with what is now known as ADHD. He performed factor analysis to determine how these behaviors fit together and first published his findings in 1970. One of the earliest aims of Conners’ work was to track changes in children’s behavior fol-lowing medication use. In 1973, Conners released a 93-item checklist of behaviors that came to be known as the original Conners’ Parent Rating Scale. It was quickly adopted as a diagnostic tool even though it did not have a normative sample of the kind structured for empirical sup-port that is required to establish a new assessment tool today. It was not until 1989 that Conners’ sample was formalized and expanded, and that the CPRS was published and shared widely. In 1998, the CPRS-R (for “revised”) was released, and in 2008, the third and most recent edition, the Conners 3-P, was released. The Conners 3-P is currently available in English and Spanish. The similarities and differences between these ver-sions are explored below in the section “Psycho-metric Data.”
+
+### Psychometric Data
+
+The format and response style of the measure have remained quite consistent throughout the revi-sions of the Conners 3-P. In all three versions, the respondent (the parent completing the mea-sure) is asked to reflect on his or her child’s actions over the past month and to respond to a series of items describing mainly problem behaviors (e.g., “gets distracted when given instructions to do something”). For each item, the respondent is asked to mark 0 (“not true at all/never/seldom”), 1 (“just a little true/occasionally”), 2 (“pretty much true/often/quite a bit”), or 3 (“very much true/very often/very frequent”) to describe the extent to which their child engages in or demon-strates the given behavior. The Conners 3-P has a long-form version (110 questions), a short-form version (45 of the 110 total questions), an ADHD Index (10 of the 110 total questions), and a Global Index (10 of the 110 questions). Previous versions also offered longer and shorter forms. There are circumstances in which one form would be preferable over another; this largely has to do with the reason for testing. For example, using the long-form version of the Conners 3-P might be preferable to using one of the shorter forms when conducting an initial assessment. Revisions were made (e.g., transitioning from the CPRS to the CPRS-R, and again from the CPRS-R to the Conners 3-P) in order to strengthen the psychometric properties of the instrument. Most available literature focuses on the psychometric properties of the CPRS-R and the Conners 3-P. The CPRS-R contains the fol-lowing subscales: Oppositional (long and short forms), Social Problems (long form only), Cogni-tive Problems/Inattention (long and short forms), Psychosomatic (long form only), Hyperactivity (long and short forms), DSM-IV Symptom Sub-scales (long form only), Anxious-Shy (long form only), ADHD Index (long and short forms), Per-fectionism (long form only), and Conners’ Global Index (long form only). The same subscales appear on the CTRS-R with the exception of the Psychosomatic subscale, which is not included. Both raw scores and T scores are generally reported for each subscale; T scores are standard-ized scores with a mean of 50 and a standard deviation of 10. For example, a child with a T score of 50 on the Oppositional subscale would have about the same level of oppositional behaviors as the average child his or her age in the normative sample. Higher T scores are associated with higher levels of problem behaviors. Internal consistency coefficients of the CPRS-R (in other words, measures of how well the individual items of the measure “hang” together and form the subscales listed above) for the total sample range from .77 to .96. The test-retest reliability coefficients – a measure of how similarly a child will be rated shortly following an initial assessment with the CPRS-R – range from .47 to .86. As previously discussed, the CPRS-R is designed to be sensitive to changes in behavior, particularly following medication use, and this might explain partially the lower test-retest reli-ability coefficients.
+
+Some significant changes were made to the CPRS-R to create the Conners 3-P. The publishers of the Conners 3-P point to the large normative sample (n ¼ 1200) that reflects the 2000 US Census information on race and ethnicity, gender, and parental education level as particular strength of the measure. While the test creators were care-ful to include a diverse normative sample in their development of the Conners 3-P, it is important to point out that the validity of the Conners’ tests in diverse cultures has not yet been established and represents an active area to study. The Conners 3-P includes the use of 1-year, instead of 3-year, age bands to compare children’s scores to the normative sample (e.g., 4-year-olds are now com-pared only to other 4-year-olds, instead of to 4-, 5-, and 6-year-olds). Also, the Conners 3-P includes optional combined gender norms for boys and girls – the norms had been strictly sep-arated by gender in the previous versions – and the combined norms can be helpful for understanding behavior within the context of settings, such as the classroom, that are very frequently coed. The Conners 3-P also has a greater focus on differen-tial diagnosis (or, in other words, teasing apart symptoms of ADHD from symptoms of related disorders) and this is reflected in its normative sample. Additionally, the age range of the Conners 3-P, at 6–18 years old, is slightly narrower than the age range, 3–17 years, of the previous CPRS versions. The age range was extended to 18 years, 11 months to capture adjust-ment through the end of high school; it was lim-ited to 6 years, 0 months so that early experiences can be assessed more thoroughly with a separate measure, the Conners Early Childhood (EC). The Conners 3-P contains the following con-tent scales: inattention, hyperactivity/impulsivity, learning problems, executive functioning, aggres-sion, and peer/family relations. It also contains four symptoms scales – ADHD Inattentive, ADHD Hyperactive-Impulsive, Conduct Disor-der, and Oppositional Defiant Disorder – that map onto the diagnostic criteria put forth in the Diagnostic and Statistical Manual of Mental Dis-orders, fourth edition, text revision (DSM-IV-TR). New to the Conners 3-P include validity scales (which indicate how a parent is responding to the items and whether or not the information he or she provides is interpretable), screening items for childhood anxiety and depression, critical items (which require immediate follow-up by the researcher or clinician administering the measure), and impairment items (which indicate decreased functioning in certain life areas, like social relationships). The Conners 3-P is also notable for the way in which it maps onto the Individuals with Disabilities Education Act (IDEA); in other words, how a child is rated by his or her parent on the Conners 3-P might carry implications for the services he or she is eligible to receive in school.
+
+Both test-retest reliability and internal consis-tency have been found to be very good for the Conners 3-P, and for the overall family of Conners 3 assessments. According to data put forth by the publisher, internal consistency coefficients (in other words, measures of how well the indi-vidual items of the measure “hang” together and form the subscales listed above) for the overall content scales is .91 and for the DSM-IV-TR scales, .90. A breakdown of internal consistency coefficients by Conners 3-P subscale are the fol-lowing: inattention ¼ .93, hyperactivity/impulsiv-ity ¼ .94, learning problems ¼ .90, executive functioning ¼ .92, aggression ¼ .91, peer relations ¼ .85, ADHD Inattentive ¼ .93, ADHD Hyperactive-Impulsive ¼ .92, Conduct Disorder ¼ .83, and Oppositional Defiant Disorder ¼ .83. The 2–4-week test-retest reliabil-ity coefficients – a measure of how similarly a child will be rated 2 weeks and again 4 weeks following an initial assessment with the Conners 3-P – was also very good in the overall Conners’ sample (Cronbach’s alpha ¼ .71 to .98, with all correlations significant at the p <.001 level). Inter-rater reliability coefficients (a measure of how likely two different respondents, such as a mother and father, or a parent and teacher, are to rate the same child’s behavior) in the overall Conners’ sample are also acceptable to excellent, ranging from .52 to .94. Continuity between the CPRS-R and the Conners 3-P was demonstrated, and tests of factorial, convergent, divergent, and discriminant validity were also performed on the Conners 3-P.
+
+### Clinical Uses
+
+The Conners 3-P is a useful tool when a child is experiencing behavioral difficulty at home or at school. The Conners 3-P may indicate whether a child’s symptoms are consistent with ADHD or a related disorder. While it includes ques-tions about different symptoms in parent-friendly and accessible language, it does not elicit all of the information that would be needed to make a formal diagnosis. It is impor-tant to note that a high score on the Conners 3-P alone is not sufficient to diagnose a child; instead, it is only one piece of information that clinicians will consider when making a diagnosis, if one is warranted. If a child has a diagnosis, then the Conners 3-P can be used to track changes in his or her behavior over time; this is especially important if a child receives intervention, medication, or special services to address his or her behavioral challenges. Some-times, the Conners 3-P is used in the absence of any behavioral problems – it can be used as a screener, or in a routine manner. Because of the overlap between behaviors – particularly externalizing ones – associated with ADHD and those associated with autism spec-trum disorders, it is helpful to understand the function of the Conners 3-P. Also, since autism spectrum disorders sometimes coexist with intel-lectual disabilities (ID), it is important to under-stand how the psychometric properties of the Conners’ tests hold up among children with ID. Deb, Dhaliwal and Roy (2008) undertook this research with the CPRS-R and the CTRS-R and found that parents and teachers differed sig-nificantly in how they rated children with ID (whereas significant correlations between their reports would be expected, based on previously published psychometric data). Also, the authors noted that some of the items were not applicable to children with severe or profound ID, and/or who were nonverbal (as are some children with autism spectrum disorders). These findings have implications for using the Conners’ tests to assess children with known autism spectrum dis-orders and ID.
+
+Conners’ Teacher Rating Scale
+
+### Description
+
+The Conners’ Teacher Rating Scale (CTRS) is a teacher-report measure that assesses children’s problem behaviors, particularly symptoms of attention deficit hyperactivity disorder (ADHD) and related disorders (including oppositional defi-ant disorder and conduct disorder). At the time of publication, the Conners 3-T (2008) is the current version of the CTRS. Teachers of children with autism spectrum disorders or suspected autism spectrum disorders may be asked to complete the Conners 3-T because of the shared symptoms between ADHD and autism spectrum disorders. The Conners 3-T was developed by C. Keith Conners, Ph.D., who also designed two related measures: the Conners’ Parent Rating Scales (CPRS), a parent-report measure, and the Conners’ Self-Report Scales (CSRS), a self-report measure for children and adolescents. Because these measures are meant to be used in conjunc-tion, the family of Conners’ tests is considered to be a “multi-informant” mode of assessment. This is valuable because it can yield information about children’s behaviors in multiple settings. For example, asking a teacher to complete the Conners 3-Tand a parent to complete the Conners 3-P can shed light on how a child’s behavior may differ between home and school.
+
+### Historical Background
+
+The Conners 3-T shares a great deal of history with the Conners 3-P. The roots of these assess-ments date back to the 1960s, when C. Keith Conners, Ph.D., created behavior rating scales based on his multiple observations of children and adolescents with behaviors consistent with what is now known as ADHD. He performed factor analysis to determine how these behaviors fit together and first published his findings in 1970. One of the earliest aims of Conners’ work was to track changes in children’s behavior fol-lowing medication use. In 1973, Conners released a 93-item checklist of behaviors that came to be known as the original Conners’ Parent Rating Scale. It was quickly adopted as a diagnostic tool even though it did not have a normative sample of the kind structured for empirical sup-port that is required to establish a new assessment tool today. It was not until 1989 that Conners’ sample was formalized and expanded, and that the CTRS, along with the CPRS, was published and shared widely. In 1998, the CTRS-R (for “revised”) was released, and in 2008, the third and most recent edition, the Conners 3-T, was released. The Conners 3-T is currently available in English and Spanish. The similarities and differ-ences between these versions is explored below in the section “Psychometric Data.”
+
+### Psychometric Data
+
+The format and response style of the measure have remained quite consistent throughout the revi-sions of the Conners 3-T. In all three versions, the respondent (the teacher completing the mea-sure) is asked to reflect on his or her student’s actions over the past month and to respond to a series of items describing mainly problem behav-iors (for example, “leaves seat when he/she should stay seated”). For each item, the respondent is asked to mark 0 (“not true at all/never/seldom”), 1 (“just a little true/occasionally”), 2 (“pretty much true/often/quite a bit”), or 3 (“very much true/very often/very frequent”) to describe the extent to which their student engages in or dem-onstrates the given behavior. The Conners 3-T has a long-form version (115 questions), a short-form version (41 of the 110 total questions), an ADHD Index (10 of the 110 total questions), and a Global Index (10 of the 110 questions). Previous versions also offered longer and shorter forms. There are circumstances in which one form would be preferable over another; this largely has to do with the reason for testing. For example, using the long-form version of the Conners 3-T might be preferable to using one of the shorter forms when conducting an initial assessment. Revisions were made (e.g., transitioning from the CTRS to the CTRS-R, and again from the CTRS-R to the Conners 3-T) in order to strengthen the psychometric properties of the instrument. Most available literature focuses on the psychometric properties of the CTRS-R and the Conners 3-T. The CTRS-R, like the CPRS-R, contains the following subscales: Oppositional (long and short forms), Social Problems (long form only), Cognitive Problems/Inattention (long and short forms), Hyperactivity (long and short forms), DSM-IV Symptom Subscales (long form only), Anxious-Shy (long form only), ADHD Index (long and short forms), Perfection-ism (long form only), and Conners’ Global Index (long form only). Unlike the CPRS-R, the CTRS-R does not contain the Psychosomatic sub-scale. Both raw scores and T scores are generally reported for each subscale; T scores are standard-ized scores with a mean of 50 and a standard deviation of 10. For example, a child with a T score of 50 on the Oppositional subscale would have about the same level of oppositional behaviors as the average child his or her age in the normative sample. Higher T scores are associated with higher levels of problem behaviors. Internal consistency coefficients of the CPRS-R (in other words, measures of how well the individual items of the measure “hang” together and form the subscales listed above) for the total sam-ple range from .77 to .96. The test-retest reliability coefficients – a measure of how simi-larly a child will be rated shortly following an initial assessment with the CPRS-R – range from .47 to .86. As previously discussed, the CPRS-R is designed to be sensitive to changes in behavior, particularly following medication use, and this might explain partially the lower test-retest reli-ability coefficients.
+
+Some significant changes were made to the CTRS-R to create the Conners 3-T. The publishers of the Conners 3-T point to the large normative sample (n ¼ 1200) that reflects the 2000 US Census information on race and ethnicity, gender, and parental education level as particular strength of the measure. The Conners 3-T includes the use of 1-year, instead of 3-year, age bands to compare children’s scores to the normative sample (e.g., 4-year-olds are now compared only to other 4-year-olds, instead of to 4-, 5-, and 6-year-olds). Also, the Conners 3-T includes optional combined gender norms for boys and girls – the norms had been strictly separated by gender in the previous versions – and the combined norms can be helpful for understanding behavior within the context of settings, such as the classroom, that are very frequently coed. The Conners 3-T also has a greater focus on differential diagnosis (or, in other words, teasing apart symptoms of ADHD from symptoms of related disorders) and this is reflected in its normative sample. Additionally, the age range of the Conners 3-T, at 6–18 years old, is slightly narrower than the age range, 3–17 years, of the previous CTRS versions. The age range was extended to 18 years, 11 months to capture adjust-ment through the end of high school; it was lim-ited to 6 years, 0 months so that early experiences can be assessed more thoroughly with a separate measure, the Conners Early Childhood (EC). The Conners 3-P contains the following con-tent scales: inattention, hyperactivity/impulsivity, learning problems, executive functioning, aggres-sion, and peer relations. It also contains four symptoms scales – ADHD Inattentive, ADHD Hyperactive-Impulsive, Conduct Disorder, and Oppositional Defiant Disorder – that map onto the diagnostic criteria put forth in the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR). New to the Conners 3-T include validity scales (which indi-cate how a teacher is responding to the items and whether or not the information he or she provides is interpretable), screening items for childhood anxiety and depression, critical items (which require immediate follow-up by the researcher or clinician administering the measure), and impair-ment items (which indicate decreased functioning in certain life areas, like social relationships). The Conners 3-T is also notable for the way in which it maps onto the Individuals with Disabilities Edu-cation Act (IDEA); in other words, how a child is rated by his or her teacher on the Conners 3-T might carry implications for the services he or she is eligible to receive in school.
+
+Both test-retest reliability and internal consis-tency have been found to be very good for the Conners 3-T, and for the overall family of Conners 3 assessments. According to data put forth by the publisher, internal consistency coefficients (in other words, measures of how well the indi-vidual items of the measure “hang” together and form the subscales listed above) for the total sam-ple range from .77 to .97. The 2–4-week test-retest reliability coefficients – a measure of how simi-larly a child will be rated 2 weeks and again 4 weeks following an initial assessment with the Conners 3-T – was also very good (Cronbach’s alpha ¼ .71 to .98, with all correlations significant at the p < .001 level). Inter-rater reliability coef-ficients (a measure of how likely two different respondents, such as a mother and father, or a parent and teacher, are to rate the same child’s behavior) are also acceptable to excellent, ranging from .52 to .94. Continuity between the CTRS-R and the Conners 3-T was demonstrated, and tests of factorial, convergent, divergent, and discrimi-nant validity were also performed on the Conners 3-T.
+
+### Clinical Uses
+
+The Conners 3-T is a useful tool when a child is experiencing behavioral difficulty at school or at home. The Conners 3-P may indicate whether a child’s symptoms are consistent with ADHD or a related disorder. While it includes questions about different symptoms in user-friendly and accessi-ble language, it does not elicit all of the informa-tion that would be needed to make a formal diagnosis. It is important to note that a high score on the Conners 3-T alone is not sufficient to diagnose a child; instead, it is only one piece of information that clinicians will consider when making a diagnosis, if one is warranted. If a child has a diagnosis, then the Conners 3-T can be used to track changes in his or her behavior over time; this is especially important if a child receives intervention, medication, educational supports, or other services to address his or her behavioral challenges. Sometimes, the Conners 3-T is used in the absence of any behavioral prob-lems – it can be used as a screener, or in a routine manner. Frick, Barry, and Kamphaus (2009) note that the Conners 3-T has several strengths that suit it well for school-based assessments. For example, it focuses on ADHD and other disorders involving externalizing behaviors that can interfere with children’s school performance. Also, its short ver-sions, with demonstrated validity and reliability, may be more accessible and user-friendly for teachers in busy school environments. However, the Conners 3-T has its drawbacks too, which include minimal assessment of childhood depres-sion and anxiety, which frequently include internalizing symptoms. Also, the normative sam-ple of the Conners 3-T is racially and ethnically diverse, but not to the same degree as the Conners 3-P. Additionally, there is little independent vali-dation of the Conners 3-T, aside from the data put forth by the instrument authors. Because of the overlap between behaviors – particularly externalizing ones – associated with ADHD and those associated with autism spectrum disorders, it is helpful to understand the function of the Conners 3-T. Also, since autism spectrum disorders sometimes coexist with intellectual dis-abilities (ID), it is important to understand how the psychometric properties of the Conners’ tests hold up among children with ID. Deb et al. (2008) undertook this research with the CPRS-R and the CTRS-R and found that parents and teachers differed significantly in how they rated children with ID (whereas significant correlations between their reports would be expected, based on previ-ously published psychometric data). Also, the authors noted that some of the items were not applicable to children with severe or profound ID, and/or who were nonverbal (as are some children with autism spectrum disorders). These find-ings have implications for using the Conners’ tests to assess children with known autism spectrum dis-orders and ID.
+
+Consent
+
+### Definition
+
+**In General**
+Consent is a voluntary agreement to participate in medical treatment, procedure, or research. A physician or other health care provider must obtain the consent of the patient or of someone legally authorized to give consent for the patient before initiating such activity. This requirement is based on the principle that every individual of sound mind has a right to determine what shall be done with his own body and to control the course of his medical treatment.
+
+**Standard**
+The historical standard for legally sufficient dis-closure was the customary disclose practices of physicians in the community. The current stan-dard, however, is a more patient-oriented one. It focuses on what material information about risks a reasonable physician would believe a reasonable patient would want to know to make a decision. It thus remains objective, but with due regard for both the patient’s informational needs and physi-cian’s situation.
+
+**Current Law**
+General principles of consent are the same in all jurisdictions, though specific details of the doctrine may vary. The right of consent requires several elements, including capacity (the patient is compe-tent to make decisions), information (the patient is informed of the benefits and risks), and voluntari-ness (the patient is not coerced into giving consent). Consent to medical treatment can be oral or written, express, or implied. In some jurisdictions, statutes specify the form that a patient’s consent must take. Consent is generally not required in certain circum-stances, including emergencies, therapeutic privi-lege, when the patient is incompetent, and when the patient waives having to consent. A physician’s failure to obtain consent from a patient prior to medical treatment can serve as a factual predicate to a malpractice action.
+
+**ASD Application**
+Patients with developmental disabilities, such as ASD, may have cognitive, social, and mental impairments that limit their ability to provide legal consent. Minors generally cannot give consent. Instead, parents or legal guardians must give consent, preferably with the child’s assent when feasible.
+
+Consequence-Based Interventions
+
+### Definition
+
+Consequence-based interventions are implemented in response to inappropriate behaviors. These inter-ventions are designed to decrease the future likeli-hood of the inappropriate behavior occurring. Interventions are determined based on the results of a functional behavior assessment. For a consequence-based intervention to be successful, it must be in response to the function of the behav-ior. Often, antecedent and consequence-based inter-ventions are used in combination to decrease the probability of inappropriate behavior occurring in the future. Some examples of consequence-based interventions are differential reinforcement and its variants, extinction, response cost, and redirection.
+
+Conservatorship (Full Conservatorship and Limited Conservatorship)
+
+### Definition
+
+A conservatorship is a legal relationship in which a court gives one person, a conservator, the duty and power to make decisions about financial and property matters for the benefit and protection of a beneficiary (also referred to as a person subject to conservatorship) (Garner 2014).
+
+### Principles of Conservatorship
+
+Conservatorships can be a useful special needs planning tool for individuals with autism spec-trum disorder (ASD) and their caregivers (Werner & Chabany 2016). The term “conserva-tor” is generally understood as a guardian, protec-tor, or preserver of another person’s property. State courts have jurisdiction in conservatorship matters to protect individuals who are unable to care for their estate because of a severe impair-ment or disability (Uniform Law Commission, 2007, 2017). A conservatorship beneficiary’s estate includes all personal assets, real property, and funds. Beneficiaries can be minors or adults. Each conservatorship must be appointed by a judge. A court order authorizes a conservator to manage and safeguard a beneficiary’s estate. Con-servators are legally required to act in a beneficiary’s best interests at all times (Devi 2013). Depending on the scope of a judge’s conservatorship order, a con-servator’s scope of authority can be broad and include such matters as deciding an individual’s spending priorities, budgets for activities, and trust savings allocations. In the United States, conservatorship decision-making laws contain three key principles:
+1. Ensuring that a conservatorship is invoked only as a last resort and after considering the availability of support to assist people with financial decision-making.
+2. Ensuring a conservatorship is as confined in scope and duration as is reasonably possible.
+3. A conservator’s decision-making should always respect the will, preferences, and rights of the individual beneficiary (National Council on Disability 2018).
+
+### Conservatorship Terminology
+
+In the past, the terms “ward,” “incompetent per-son,” and “incapacitated person” were regularly used to describe a person with a conservatorship. Today, there is a growing movement among dis-ability rights advocates to discard these terms as outdated, derogatory, and disrespectful. Modern terminology in conservatorship cases increasingly emphasizes the importance of each individual’s right to self-determination and general autonomy (Page & Hinrichs 2017). When describing a per-son who receives conservatorship services, the following terms are widely used: beneficiary, conservatee, individual subject to conservator-ship, adult who is the subject of a conservatorship proceeding, protected individual, protected per-son, and principal (Dinerstein 2012). Conservatorships are largely governed by the probate laws of the individual states. The title of conservator is not used consistently across all state codes, and a conservator’s requirements and functions can differ greatly among state laws. Though a small number of state laws con-sider the term “conservatorship” a synonym for a “guardianship,” the majority of states do not use the words interchangeably (Kohn et al. 2013). As a general rule, a guardian’s decision-making abil-ities include decisions about both personal well-being and financial/property interests. In contrast, a conservator’s decisions are limited to only finan-cial/property matters (National Council on Disability 2019). In most states, there are two types of conserva-torships: full and limited (American Bar Associa-tion Commission on Law and Aging 2016). A full conservatorship is designed to care for all of the essential elements of a beneficiary’s estate. If an individual’s circumstances do not require a full conservatorship, a judge may decide to order a limited conservatorship (also known as a partial conservatorship). In limited conservatorship cases, a conservator has a narrow scope of work in matters related to a beneficiary’s estate – these conservators can only make decisions about spe-cific situations regarding the beneficiary’s prop-erty matters (Wilber et al. 2001). In some states, the title of “custodianship” is used to describe a full conservatorship or various types of conserva-torship with limited authority.
+
+### Legal Capacity in Conservatorship Cases
+
+Before issuing a court order to appoint a conser-vator, a judge must first declare that a beneficiary lacks “legal capacity (Parker 2016). Historically, the term legal capacity was also referred to as “legal competency.” Individuals do not have legal capacity when they are deemed entirely incapable of managing financial affairs. In these instances, the individual is unable to fully safe-guard himself or herself against harm to personal wealth and/or property. A judge will determine that a beneficiary lacks legal capacity based on evidence presented to the court, including medical records and testimony from treating physicians, mental health professionals, caregivers, family members, friends, and the individual under review for the conservatorship appointment (United States General Accounting Office 2004). For a conservatorship appointment, a beneficiary’s legal capacity must be distinguished from his or her mental ability (Dudley & Edmonds 2018). Legal capacity embodies individual legal rights to have standing to bring and defend law-suits and enter into/execute legally binding con-tracts. In contrast, mental or cognitive capacity issues in conservatorship matters focus on a per-son’s ability to express personal will and inten-tions. Depending on the jurisdiction in question, state law may require conservators to take a beneficiary’s preferences into account when making life-changing decisions about finances and property (Kohn & Blumenthal 2014). Under all state laws, if the circumstances that originally mandated a conservatorship have mate-rially changed, courts are obligated to perform a review to confirm whether the conservatorship is still required or if another type of protective mea-sure is more appropriate.
+
+### Starting a Conservatorship
+
+When a judge considers whether a conservator-ship appointment is necessary, the court may pro-ceed with the following steps:
+1. Hold an evidentiary hearing.
+2. Order individuals produce evidence or give testimony (including the proposed beneficiary under review for a conservatorship, health-care treatment providers, family members).
+3. Order a medical evaluation or assessment of the proposed beneficiary’s abilities and impairments.
+4. Order any appropriate investigation of any pro-posed conservator or other individuals involved in a conservatorship proceeding.
+5. Review a certified copy of the transcript or other hearing records of from conservatorship proceedings in other courts.
+6. Issue an order authorizing the release of med-ical, financial, criminal, or other relevant infor-mation, including protected health information subject to state and federal privacy laws for records.
+
+### Ending a Conservatorship
+
+There are two ways to end a conservatorship: with a court order or when the beneficiary passes away. When the beneficiary dies, his or her conservator-ship terminates automatically. If a conservator’s assistance is no longer needed because of a sig-nificant change in the beneficiary’s cognitive and/or physical state, the court may decide to formally terminate the conservatorship. Anyone who is interested in the beneficiary’s welfare can petition a court to end a conservatorship, includ-ing the conservator, a family member, friend, or the beneficiary himself or herself. Upon this peti-tion, a judge will decide whether a conservator-ship should: (1) continue as is, (2) be modified to limit the conservator’s responsibilities, or (3) end (Uekert et al. 2018). Ultimately, a judge will con-sider what is in the best interest of the beneficiary and only deem a conservatorship inappropriate when there is enough evidence in the court’s records to show that a “good cause” exists for termination. If a conservator wishes to resign from his or her appointment, a judge must file a resignation request with the court. Before a judge approves a conservator’s resignation request, the judge will appoint another person to serve as conservator (Van Arsdale & Oakes 2020).
+
+Constipation
+
+### Definition
+
+The definition of constipation varies among indi-viduals. To some, it is hard stools; to others, it is large stools; and to many more, it is infrequent stools. Because the word “constipation” has different meanings for different people, it has been difficult to compile data on normal and abnormal patterns in children (Schuster 1984). Webster’s English Dictio-nary reads “a term used to describe the subjective complaint of passage of abnormally delayed or dry, hardened feces, often accompanied by straining and/or pain” (Webster’s ninth new collegiate dictio-nary 1986). Guidelines of the North American Soci-ety for Pediatric Gastroenterology, Hepatology, and Nutrition (NASPGHAN) similarly define constipa-tion as “a delay or difficulty in defecation, present for 2 or more weeks and sufficient to cause significant distress to the patient” (Baker et al. 1999). At present, the most widely accepted definitions for childhood functional constipation are the Rome III definitions (Hyman et al. 2006 and Rasquin et al. 2006) (12,13). An evidence-based recommendations from ESPGHAN (European Society of Pediatric Gastro-enterology, Hepatology and Nutrition) and NASPGHAN in 2014 defined constipation as fol-lows (Tabbers et al. 2014). In the absence of organic pathology, two of the following must occur:
+For a child with a developmental age < 4 years
+1. 2 defecations per week
+2. At least 1 episode of incontinence per week after the acquisition of toileting skills
+3. History of excessive stool retention
+4. History of painful or hard bowel movements
+5. Presence of a large fecal mass in the rectum
+6. History of large-diameter stools that may obstruct the toilet
+Accompanying symptoms may include irrita-bility, decreased appetite, and/or early satiety, which may disappear immediately following pas-sage of a large stool.
+For a child with a developmental age ≥ 4 years with insufficient criteria for irritable bowel syndrome
+1. 2 defecations in the toilet per week
+2. At least 1 episode of fecal incontinence per week
+3. History of retentive posturing or excessive volitional stool retention
+4. History of painful or hard bowel movements
+5. Presence of a large fecal mass in the rectum
+6. History of large-diameter stools that may obstruct the toilet
+One can conclude then that normal stool fre-quency ranges from an average of four per day during the first week of life to two per day at 1 year of age. The normal adult range of three per day to three per week is attained by 4 years of age. These data reflect the average stool frequency in normal infants and children in industrialized countries, not in developing countries, where the normal diet is rich in fiber and normal stool frequency may be different.
+
+Many experts believe that constipation is the delay in defecation for approximately 2 weeks or difficulty in defecation. The causes of constipa-tion are many and may be organic or nonorganic; medications can be a potential cause. Children with ASDs can have sensory processing abnor-malities and develop stool-withholding behaviors or constipation related to altered pain responses. Even children with ASDs who have daily bowel movements may have retention of stool that is not evident to parents, teachers, or health-care pro-viders (Buie et al. 2010). Estimates suggest that 95% of childhood constipation may be functional, without an underlying physiologic cause, and many children with ASD present with nonorganic toileting prob-lems that may precipitate or play a role in the development of constipation, including absent or delayed acquisition of bowel training (Whiteley 2004) and higher rates of problem behaviors related to changes in toileting routine. Fecal reten-tion in ASD may also occur secondary to diffi-culty with sensory stimuli, sensory processing, and motor problems, leading to altered gastroin-testinal motility and defecation physiology (Peeters et al. 2013). It is also possible that ele-vated rates of constipation may be related to the ubiquity of food selectivity in this population, as the dietary patterns often associated with ASD involve high intake of processed food and lack fiber-containing fruits and vegetables, which pro-vide a natural laxative effect and decrease intesti-nal transit time.
+
+The evaluation of all children who present with constipation should include a thorough medical history and physical examination. Understanding what the family or child means when they use the term “constipation,” the fre-quency of bowel movements, the consistency and size of stool, and the presence or absence of abdominal pain is important. A history of stool-withholding behavior points more toward func-tional causes of constipation. For children with ASDs, the physical examination may not iden-tify palpable stool, and a careful rectal exami-nation might not be feasible. Every attempt should be made to examine the rectum, although at times it cannot be accomplished. The rectal examination enables the assessment of stool retention, anal tone, and occult mass, as well as the presence or absence of blood, and helps to reassure the family that the child’s anatomy is normal. A plain radiograph of the abdomen may reveal a rectal fecal mass not palpable on the abdominal examination, but due to conflicting evidence for the accuracy of radiologic diagnosis of constipation, routine radiography is not recommended. Diagnostic clues can help to identify some organic causes of constipation. Hirschsprung’s disease is com-mon in children with ASDs, and a history of delayed passage of stool after birth should raise the suspicion of aganglionosis. Anatomic abnormalities such as an anterior displacement of the anus, which is more common in girls than boys, can be diagnosed by careful inspec-tion of the anal area. Drugs added to behavior management for constipation are often benefi-cial. Mineral oil, magnesium hydroxide, lactulose, sorbitol, polyethylene glycol (PEG), or a combination of lubricant (mineral oil) and laxative is recommended for the daily manage-ment of constipation in children.
+
+Contactin-Associated Protein 2
+
+The gene CNTNAP2 encodes the protein contactin-associated protein-like 2 (recommended UniProt name; also known as Caspr2), a member of the neurexin superfamily, and of a class of genes functioning in the nervous system as cell adhesion molecules and receptors acting at the cell mem-brane (EntrezGene). This gene is less frequently referred to as AUTS15, CDFE, CASPR2, PTHSL1, NRXN4, KIAA0868, and DKFZ-p781D1846 (UniProt; BioGrid).
+
+### Structure
+
+CNTNAP2 is located at chromosome 7q35; the genic region of this gene is quite large, spanning 2.3 MB (Nakabayashi and Scherer 2001). In humans, there is a single established isoform (or version of the gene) consisting of 24 exons. The final protein product is 1,331 amino acids. The protein spans the cell membrane one single time (UniProt). The protein consists, in order, of a signal peptide (a short sequence directing the pro-tein product where to go in the cell), a FA58C domain (“cell surface-attached carbohydrate-binding domain”), two laminin G domains (common in extracellular proteins), an epidermal growth factor-like domain (also frequently found in extracellular proteins), a fibrinogen C-terminal domain, another laminin G domain, another epi-dermal growth factor-like domain, another lami-nin G domain, a helical transmembrane domain (spanning the membrane between the inside and outside of the cell), and a cytoplasmic domain (the only portion of the mature protein that is inside the cell). This transmembrane and cytoplasmic end of the protein also contains a putative band 4.1 homologues’ binding motif (common to neurexins as well as syndecans and glycophorin C intracellular C-termini, all of which are cell surface proteins) (SMART; UniProt).
+
+### Function
+
+CNTNAP2 was first described by Poliak, Gollan, Martinez, et al. (1999) and was shown to be a member of the neurexin superfamily that localized within juxtaparanodal regions of myelinated axons and clustering with potassium channels. The juxtaparanode is the region next to the para-node, which is on either side of the node of Ranvier, an unmyelinated (unsheathed) region of the axon that allows for efficient signal conduc-tion. Homologs of CNTNAP2 are found as far back as insects (D. melanogaster, A. gambiae) and nematodes (C. elegans) (EntrezGene), further implying important neural function. CNTNAP2 was shown to interact with CNTN2 (TAG-1); in the absence of CNTN2, CNTNAP2 failed to localize at juxtaparanodes and potassium chan-nels did not accumulate normally (Traka et al. 2003). The transcription factor FOXP2, itself implicated in language function (EntrezGene), was shown by Vernes, Newbury, Abrahams, et al. (2008) to directly regulate CNTNAP2 expression. CNTNAP2 was also shown to have higher expression in circuits involved in higher cortical function, like language (Abrahams et al. 2007). A recent mouse knockout (an animal in which both copies of a gene have been removed) of CNTNAP2 showed dysfunction in neuronal migration (ectopic neurons occurring in the cor-pus callosum) and reduced numbers of interneurons (Peñagarikano et al. 2011). One study has shown evidence of CNTNAP2 in rat forebrain synapses (Bakkaloglu et al. 2008), but it has not been definitively characterized as a synaptic molecule.
+
+### Pathophysiology
+
+CNTNAP2 has been implicated in several psychi-atric disease phenotypes, including Tourette syn-drome and obsessive-compulsive disorder (Verkerk et al. 2003), but has received particular attention for its possible association with autism. Initially, linkage evidence suggested a language-linked gene on chromosome 7q35 (Alarcon et al. 2002, 2005). A homozygous mutation in CNTNAP2 was associated with a cortical dyspla-sia and focal epilepsy phenotype, resulting in sei-zures, language and social impairments, mental disability, and autistic traits (Strauss et al. 2006). CNTNAP2 was additionally implicated in multi-plex autism families by a linkage peak at 7q35 and showed subsequent significant association with a single DNA base change, or single nucleotide polymorphism (SNP) within the gene in an anal-ysis of parent-affected child trios (Arking et al. 2008). A fine-scale analysis of the 15 mb region (7q34-36) encompassing the implicated 7q35 lan-guage region in 172 parent-autistic child trios reinforced CNTNAP2 as an autism candidate gene; follow-up analysis in 304 separate parent-autistic child trios showed association of a SNP in CNTNAP2 with age at first spoken word, again underscoring a possible connection to language phenotype as suggested by Vernes et al. (2008). Parallel work in this study showed CNTNAP2 expression very specific to brain circuits established as essential for executive function in humans (Alarcon et al. 2008). Also in 2008, large-scale resequencing of the CNTNAP2 gene in 635 autism cases and 942 unaffected controls showed overrepresentation of rare deleterious (i.e., changing the amino acid in a way that is predicted as harmful) mutations in autistic patients compared with unaffected controls, but not at a statistically significant level. A specific mutation, I869T (the 869th amino acid changed from isoleu-cine to threonine), however, occurred four times in three unrelated families and was not seen in any controls, reaching statistical significance. In each case, it was inherited from an ostensibly unaffected parent, suggesting again that idiopathic (nonsyndromic) autism may sometimes involve harmful mutations in multiple neurological genes at once. A 2010 study described two siblings with a deletion spanning CNTNAP2 who showed mental retardation and language delay (Sehested et al. 2010); a separate study described a boy with a complex chromosomal rearrangement (pieces of chromosomes breaking off and rearranging sponta-neously) disrupting CNTNAP2 who presented with speech delay and ASD (Poot et al. 2010). More recently, a rare, predicted deleterious CNTNAP2 variant was observed when 20 exomes of patients with autism were sequenced (O’Roak et al. 2011), perhaps acting in concert with a FOXP1 mutation in that patient. Finally, a report of a mouse model with both copies of the mouse homolog of CNTNAP2 knocked out (Peñagarikano et al. 2011) described mice with deficits in the 3 ASD core features, in addition to epileptic seizures (reminiscent of Strauss et al. 2006) and hyperactivity. The evidence in CNTNAP2 to date, as well as the implication of several other neurological genes (e.g., NLGN3, NLGN4 X-linked, SHANK3, NRXN1) involved neuron and synapse structure and development, means that further research on CNTNAP2 will undoubtedly continue and, hopefully, better eluci-date its precise role(s) in autism spectrum disorders.
+
+Contingencies of Reinforcement
+
+### Definition
+
+Contingencies of reinforcement, in their simplest form, are comprised of antecedents (events that occur immediately before a behavior), responses or behaviors, and consequences (events that occur immediately after a behavior). The term con-tingencies refers to the relationship or interre-lationship (Skinner 1969) between these events. Reinforcement refers to consequences that in-crease the probability of the behavior occur-ring again under similar circumstances. Thus, contingencies of reinforcement describe an antecedent-behavior-consequence link in which the consequence increases the likelihood that a behavior will occur again in the presence of an antecedent. Contingencies of reinforcement are a key component in applied behavior analysis (ABA) approaches to the treatment of autism spectrum disorders (ASD). For