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+Diagnostic Interviews
+Ann S. Le-Couteur and Thomas P. Berney
+Institute of Health and Society, Sir James Spence
+Institute, Newcastle University, Royal Victoria
+Infirmary, Newcastle upon Tyne, UK
+
+Definition
+The diagnostic interview (DI) is a central component of the process (diagnostic process) in which, for a variety of reasons ranging from research to the development of an intervention plan, a decision is made as to whether there is sufficient evidence in an individual’s symptoms and signs for a diagnosis of one or more of the “disorder(s)” defined by the criteria of the internationally agreed diagnostic classification systems (▶DSM-5).
+
+Historical Background
+Following the initial descriptions of autism and Asperger syndrome in the 1940s, agreed criteria emerged slowly and a number of checklists were developed which matched a list of symptomatology against the criteria evolving at the time in ICD 9 (1975) and DSM-II (1980) (DSM-III) focusing on accounts of observable behavior, particularly in childhood, notably the E-2 (Rimland diagnostic form for behavior disturbed children (E-2)) and the Autism Behavior Checklist (ABC). In the 1960s, in both America and the UK, the search for greater consistency and precision in psychiatric diagnosis led to the development of standardized diagnostic interviews; initially schedules of standard questions, these became elaborated into a more clinical interview that encouraged the interviewer to cross-examine the patient until the nature of the symptom was clear (Wing et al. 1967). A decade later, the same model led to the development of more systematic interviews in making the diagnosis of autism (as the prototypical disorder of the pervasive developmental disorders). Wing and Gould produced the Handicaps, Behavior, and Skills Schedule (HBSS) which they later refined into the Diagnostic Interview for Social and Communication Disorder (DISCO), Schopler and Reichler developed the Childhood Autism Rating Scale (CARS), and Le Couteur, Rutter, and Lord produced the Autism Diagnostic Interview (later revised to become the ADI-R) (Autism Diagnostic Interview-Revised). These standardized diagnostic instruments consist of a semi-structured interview (based on the agreed symptom criteria) with an adult informant and became recognized as the “gold standard” in terms of their comprehensiveness and reliability in obtaining a clinical history.
+
+The identification of a broader spectrum of autism disorders (ASD), going beyond the original narrow definition for autism, led to an extension of the content and form of ▶diagnostic instruments in autistic spectrum disorders. Examples of these are the Asperger Syndrome Diagnostic Interview (ASDI) and the Autism Questionnaire (AQ) (Baron-Cohen et al. 2001) for Asperger syndrome, the Pervasive Developmental Disorder in Mental Retardation Scale (PDD-MRS) for people with intellectual disability (Kraijer and de Bildt 2005), and the Diagnostic Interview Guide for use in general adult psychiatry (Royal College of Psychiatrists 2011). The recognition of autistic traits (broader autism phenotype – broader spectrum prevalence) in the relatives of people with ASD has led to the development of a variety of interviews to identify these behavioral and personality characteristics. In many of these, the emphasis was on obtaining material from informants (usually parents) about behavior. At the end of the 1990s, the Autism Diagnostic Observation Schedule (ADOS) was developed as a play and activities based assessment with the individual; this assessment is described as a series of tightly defined, detailed observations which systematically elicits autistic symptomatology. In the last decade, the number of instruments, their use varying from screening to diagnosis, has reflected the mounting interest in ASD, while increased public awareness and the Internet have fostered the growth of self-rating scales and the demand for confirmatory diagnostic interviews.
+
+Current Knowledge
+The Content of the Interview
+There are a variety of models for conducting a diagnostic interview. The structure or framework for the DI is important, but there is no compelling evidence to recommend any particular interview format for any specific situation. For all DIs (irrespective of the interview format), the underlying context is the social engagement and interaction between the interviewer and the interviewee. The interviewing skills and attitudes of the interviewer (clinician or researcher) affect the quality of the interaction which in turn influences the success of the information-gathering process. The responses of the interviewee (also affected by many factors including whether they already know the interviewer; the interviewee is in fact the subject of the interview; his or her intellectual and communicative ability, motivation, emotional state, and so on) and the setting can also influence the outcome and “success” of the diagnostic interview (DI).
+
+It should include:
+1.  An account of the individual’s current concerns – the symptoms that have brought to interview at this particular time and their development.
+2.  A systematic survey of the symptomatology associated with ASD, especially that which is directly related to the diagnostic criteria. This review should also include consideration of other behavioral features known to be commonly associated with ASD such as motor coordination, sensory and perceptual symptoms, and feeding and bowel problems. It should include any other behavioral problems recognizing that these can occur in response to a variety of potentially modifiable influences from toothache to a change in school timetable or work colleagues.
+3.  The wider setting – the individual’s everyday life and activities, relationships, and accomplishments.
+4.  The structure of their family and any history of developmental or psychiatric disorder.
+5.  An account of the individual’s development and their acquisition of skills, not just in infancy and early childhood but subsequently, through school and after, to give a detailed “developmental history”.
+6.  An account of any other anomaly, past or present, including developmental, psychiatric, or medical disorder as well as of any other adversity including deprivation or substance abuse.
+
+The DI will usually be complimented by a direct examination of the individual together with the collation of background reports (including direct observation in other settings). All these sources of information will contribute to the accuracy and value of the final, “best estimate,” diagnostic conclusions which, in turn, will inform the multiagency needs- and skills-based management plan.
+
+While the DI and examination are conceptually distinct, in practice, there is likely to be a substantial overlap. For example, when an individual is being interviewed and asked to provide their own account, the clinician will be considering the way the account is being given, the quality and content of the social interaction, and other individual characteristics (such as their appearance, behavior, and communication). These factors will inevitably affect the interaction between the clinician and the interviewee, thus shaping the course of the DI.
+
+How the DI progresses is at least in part dependent on the skills of the interviewer, their training and expertise, as well as the setting of the interview and the expectations of the interviewees. All these different aspects can foster a “dialogue” between clinician and individual. Instruments may be combined for history-taking and observation although, in the end, the distinction between them is one of emphasis rather than clear-cut. For example, while the framework of observational ratings is central to the ADOS, it is also a semi-structured interview.
+
+The Format of the Diagnostic Interview
+The interview may take a range of formats depending on its purpose:
+
+1.  Unstructured. The structure is not immediately apparent, but the interviewer’s impression determines the content, purpose, and conclusions of the interview. Its primary purpose may be a different one with diagnosis as a secondary consideration. Such an assessment depends greatly on the individual clinician’s experience, and for this reason it is likely to be difficult to understand or replicate.
+
+2.  Semi-structured/interviewer based. The interview, usually based on a predetermined diagnostic framework, has well-defined symptoms to be explored. Usually conducted in a conversational style, it takes the form of required questions supplemented by additional, optional, open-ended prompts as necessary until there is sufficient information for the trained interviewer to make the coding judgment for each item and section of the interview. The precision and clarity with which symptoms and their codings are defined contribute to the quality of the instrument.
+
+3.  Structured/respondent based. The trained interviewer closely follows a defined format without deviation; the interview may be restricted further by giving the interviewee a limited number of choices. The interviewer is not called upon to make any clinical judgment (and, indeed, may not know very much about ASD and other diagnoses to complete the interview). The result is a relatively high inter-rater reliability and an interview that lends itself to being turned into a self-completion questionnaire. This can be administered as a pre-interview contribution or completed in a computerized format (e.g., the E-2 or Autism Spectrum Quotient (AQ) questionnaires). Increasingly for some individuals, access to this type of questionnaire has been a staging post in their journey to diagnosis.
+
+4.  A composite. The interview incorporates the material from a preinterview questionnaire. Not only is this a more effective use of time, substantially shortening the DI, but many individuals are more comfortable (and therefore more open) with the impersonality of a self-completion questionnaire. Examples of DIs that use information-collected preinterview include the Developmental, Dimensional, and Diagnostic Interview (3Di) (Skuse et al. 2004) and the Adult Asperger Assessment (AAA) (Baron-Cohen et al. 2005).
+
+It is difficult to define the point at which the self-completion or screening checklist becomes a more formal diagnostic instrument as this will depend on the skill, experience, and intent of those employing it.
+
+The more standardized the format for gathering and organizing the information, the greater the consistency in the data collected and the diagnoses arrived at by clinicians and researchers of varied experience and views and from different centers. However, validity is lost with increasing rigidity that limits the clinician’s skills. Using agreed diagnostic systems permits prospective research as well as making clinical material available for retrospective review for service and academic analysis. The whole process is more transparent and can be taught to trainees.
+
+The style of interview has to be appropriate to the task in hand: a structured interview, with its very narrow, specific remit, will be used for screening or surveys and as such can be administered by a technician. The semi-structured interview provides the framework for a more in-depth assessment when a definitive research or clinical diagnosis is required and usually includes one or more summary algorithms to identify ASD using prespecified thresholds. However, the protean presentations of ASD and the demands of clinical work mean that, in the end, even the best of these instruments does not remove the need for knowledge and experience of ASD in coming to a clinical diagnosis which will inform the diagnostic formulation and intervention planning. There are cases, notably in adulthood, of individuals with less clear-cut presentations where it is difficult to discern the pattern of symptoms. It is here that the experience of working with a wide variety of people across the variations of age, ability, gender, ethnicity, and comorbidity makes it possible to appreciate the characteristic impairments of ASD. In addition, within the assessment team, there needs to be sufficient knowledge and experience to recognize the developmental and psychiatric disorders that are associated with ASD (notably attention deficit hyperactivity disorder (ADHD) (▶Attention Deficit/Hyperactivity Disorder) and developmental coordination disorder (▶Developmental Coordination Disorder)).
+
+The choice to use a particular diagnostic instrument will be informed by both the purpose of the interview and the features of the instrument. For example, the ADI-R (▶Autism Diagnostic Interview-Revised) provides a summary lifetime diagnosis, using information about early childhood and the current state for key aspects of behavior and development and a record of the particular unusual behaviors (such as restricted, repetitive mannerisms and stereotyped behaviors) relevant to the decision as to whether probably all references to Pervasive developmental disorder should now be replaced with autism spectrum disorder is present or not. The frequency and intensity of each symptom is carefully graded to give a detailed quantified picture of key components. The DISCO (▶Diagnostic Interview for Social and Communication Disorders) takes a rather broader approach to arrive at a systematic description that allows the identification of other developmental disorders. The 3Di is a computer-based interview designed to focus on current functioning to assess autistic traits, social impairments, and comorbidities in children of normal ability. The content of the interview generates a structured report with summary algorithms of symptom profiles for autism and common non-autistic comorbidities. By contrast, the CARS (▶Childhood Autism Rating Scale) draws on observation as well as interview. The format is much less structured, guiding the interviewer through the relevant domains rather than individual symptoms, requiring the researcher/clinician to reach the coding decisions through the integration of information from subject and informants.
+
+Most structured instruments (▶Diagnostic Instruments in Autistic Spectrum Disorders) have been designed for a specific group, often defined by age (e.g., childhood) or ability. This means that the phrasing or materials might not be suitable for a different “client” group when adaptation of materials and further reliability and validity studies would be required.
+
+As adults come forward for diagnosis, including, for example, those with a severe intellectual disability, women of normal ability, and individuals with preexisting psychiatric and personality disorder diagnoses, the challenge will be how best to tailor the format and content of the DI appropriately. A particular issue is the necessity of a developmental history to confirm that the evidence of delayed or deviant development dates back to early childhood. This becomes particularly important in adulthood should there be a need to differentiate ASD from other disorders (such as schizophrenia (▶Schizophrenia) or dissocial or obsessive-compulsive personality disorders (▶Obsessive-Compulsive Disorder (OCD)). However, it is this client group who may experience real difficulty finding an informant with accurate knowledge about their early development.
+
+Whatever the format of the DI, training in its use is required. This applies especially to standardized instruments where the more structured the interview, the more straightforward the training. While it may be obtained by attending a specific training course, receiving in-house individual tuition, or using a self-taught program, it should include a check that clinician/researcher has reached an acceptable standard of accuracy and reliability. This should be followed by regular opportunities to maintain consistency and reliability over time. Undertaking the rating of standardized videos or attending joint sessions with colleagues can help to maintain best practice in administration of the procedure as well as reliability between colleagues and different centers. However, because this is time consuming and may be seen as additional pressure on scarce resources, it is all too easily overlooked.
+
+Implementing the Interview
+A DI may take place as a single event in one setting or be spread across several sessions and settings. The venue (clinic, specialist center, home, school, or other setting) will depend on the needs of individual, their family/carers, clinicians, and services. For example, a very anxious individual or a disabled relative may only be accessible in the home; a clinic may be the only place to get the opinion of a busy clinician or be the best place to provide the structured, calm setting needed to see someone at their best. It may be necessary to go to a school, nursery, or workplace to see the context and thereby understand what is happening there. Observation in different settings may allow some distinction to be made between what behavior is pervasive and what is situational and in response to a particular environment or set of circumstances.
+
+The DI must provide sufficient information for the interviewer to decide whether the symptoms and signs are:
+1.  Sufficiently pronounced in their intensity or frequency to cross the threshold that separates so-called normal variation for developmental progress and personal characteristics from disorder: threshold that may well vary according to the problems experienced by the individual, the context and situation, and the “demands” and expectations placed upon them. For example, a young child who has managed well in their home with a supportive family may find it much more difficult to settle into an educational setting such as preschool if they do not have sufficiently flexible communication, social and play skills to join in with other young children or cope with new and unexpected changes in routine in an otherwise familiar environment. Similarly, an adult who may have learnt to manage effectively in a particular workplace may still find that he/she is less able to succeed in social and more personal relationships. For the diagnostic interview to be successful, the interviewer needs to understand the importance of gathering information about the development of the individual’s behavior in different settings and contexts over time. This may well require (especially for children and young people but often also for adults) information from other informants who know the subject well in different settings.
+2.  Sufficiently close to the currently agreed criteria (▶DSM-5) for a diagnosis of ASD or might be explained better by some other disorder. ASD is a neurodevelopmental disorder defined by its onset in early childhood, something that may be difficult to confirm in later adulthood.
+
+The interview therefore has to enable the clinician to distinguish the signals of ASD against the background noise of other, complicating disorders, particularly other developmental and psychiatric disorders such as intellectual disability, specific speech and language disorders, attention deficit hyperactivity disorder (ADHD), epilepsy, and/or mental health problems such as anxiety or obsessive-compulsive disorder.
+
+The interview must also be appropriate to its immediate purpose: for example, the requirements for inclusion in a research study might be more stringent than those needed as the basis for clinical or administrative planning.
+
+A diagnosis may be sought for many reasons, ranging from inclusion in a research study, accessing specific treatments and interventions, eligibility for particular education provision, achieving financial benefits, and gaining family understanding, through to assisting a court to understand the needs of the individual. Most importantly, it can give the individual a more complete understanding of their profile of strengths and impairments. The diagnostic interview also provides a benchmark against which subsequent progress can be measured. It has to be tuned accordingly to meet these specific requirements.
+
+The results of the interview should be valid (i.e., that others would agree with the diagnostic conclusions) and reliable (they would be the same if repeated, whether by the same clinician or others). The process needs to be acceptable to all, sufficiently transparent to be understood, and sufficiently valued for the results to be useful.
+
+Most instruments require the interviewer to make judgments and ascribe a numerical score to each item in the assessment. These scores may be collated to give symptom and/or domain scores which can be summarized within one or more instrument-specific diagnostic algorithms. For a number of instruments, usually those that have been developed for research, the reliability and validity of the algorithm scores and instrument-specific diagnostic thresholds have been tested and refined in different populations. However, it is important to recognize that a diagnostic algorithm score derived from a particular instrument may contribute to, but is not equivalent to, a clinical diagnosis. This is something broader, using an internationally agreed diagnostic classification system, based on information gathered from several sources, and often involving professionals working in different agencies to provide a multidisciplinary assessment. This information, in turn, will contribute to, but is not sufficient for, the development of a management plan. The DI, which may include the use of a structured instrument, is an opportunity for the development of a dialogue between the interviewer, the individual, and the family/carer and, as such, can also provide the context for sharing the outcome of the multi-agency assessment.
+
+One of the great values of using an agreed diagnostic classification system is that it facilitates the possibility of successful research collaborations between clinical academic centers as well as making clinical material available for service review and analysis. With greater transparency between services and centers, there is an increase in research capacity, the ability to share new knowledge and significant developments, and in opportunities for trainees to learn from the experiences of their colleagues.
+
+Future Directions
+A number of standardized instruments are now in routine use for the DI providing both a valuable framework for the history as well as the basis for the start of a therapeutic relationship with individuals and families. Many are time consuming and resource intense, and this has to be balanced against the benefits of the therapeutic alliance and detailed descriptions of behavior. While the use of a detailed DI may well be appropriate for a behavioral syndrome that has such a variety of presentations and underlying disorders, there is great pressure to develop briefer processes and ever greater consistency while maintaining validity.
+
+The value of increasingly sophisticated online questionnaires as an adjunct to the DI needs to be investigated. New measures will also be required as further understanding of the complexity of the autism spectrum across the lifespan becomes available. However, the development of new instruments is a complex and expensive task. An equally important challenge is to investigate the best ways of getting reliable information from different sources to complement the DI this will enable the clinician/researcher, referred individual, and family to achieve a valid diagnostic formulation that in turn leads to an accurate needs- and skills-based management plan. The recognition of autistic traits in the families of people with autism has led to the development of instruments to identify these which, once sufficiently validated and standardized, will be published. In spite of many claims and much research, there is still no reliable laboratory test for ASD. However, even if such a test were ever developed, its results would complement the diagnostic interview rather than replace it, a model seen in other medical conditions as, for example, the use of genetic testing in the clinical diagnosis of Down or Rett syndrome. With increasing awareness and understanding of ASD, there is likely to be greater emphasis on the identification of the strengths, skills, needs, and impairments of the individual and their family, as well as on diagnosis, to inform a dimensional diagnosis and profile across different domains of functioning. Although separate assessments may be needed to measure different aspects of an individual’s functioning (e.g., social responsiveness, language and flexibility, anomalies in sensory sensitivity and motor coordination), this information will always need to be collated alongside the findings of a DI to achieve a diagnostic formulation. At least for the foreseeable future, classification systems used in clinical and research practice, together with other social and resource pressures, will continue to require a categorical diagnosis of ASD.
+
+See Also
+▶Anecdotal Observation
+▶Asperger Syndrome Diagnostic Interview
+▶Autism Behavior Checklist
+▶Autism Diagnostic Interview-Revised
+▶Autism Diagnostic Observation Schedule
+▶Broader Autism Phenotype
+▶Childhood Autism Rating Scale
+▶Developmental Coordination Disorder
+▶Diagnostic Interview for Social and Communication Disorders
+▶Diagnostic Instruments in Autistic Spectrum Disorders
+▶Diagnostic Process
+▶Dimensional Versus Categorical Classification
+▶DISCO
+▶DSM-III
+▶DSM-5
+▶Dyspraxia
+▶Evaluation of Sensory Processing
+▶Informal Assessment
+▶Obsessive-Compulsive Disorder (OCD)
+▶Psychotic Disorder
+▶Schizophrenia
+▶Sensory Impairment in Autism
+▶Theory of Mind
+
+References and Reading
+Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17.
+Baron-Cohen, S., Wheelwright, S., Robinson, J., & Woodbury-Smith, M. (2005). The Adult Asperger Assessment (AAA). A diagnostic method. Journal of Autism and Developmental Disorders, 35(6), 807–819.
+Kraijer, D., & de Bildt, A. (2005). The PDD-MRS: An instrument for identification of autism spectrum disorders in persons with mental retardation. Journal of Autism and Developmental Disorders, 35(4), 499–513.
+Royal College of Psychiatrists. (2011). Diagnostic interview guide for adults with autism spectrum disorder (ASD). http://www.rcpsych.ac.uk/pdf/asperger_interview_use_this_one.pdf
+Skuse, D., Warrington, R., Bishop, D., Chowdhury, U., Lau, J., & Mandy, W. (2004) The developmental, dimensional and diagnostic interview (3Di): A novel computerized assessment for autism spectrum disorders. http://www.ixdx.org/
+Wing, J. K., Birley, J. L. T., Cooper, J. E., Graham, P., & Isaacs, A. D. (1967). Reliability of a procedure for measuring and classifying “present psychiatric state.”. The British Journal of Psychiatry, 113, 499–515.
+
+Diagnostic Overshadowing
+Steve Kanne
+Department of Health Psychology, School of
+Health Professions Thompson Center for Autism
+and Neurodevelopmental Disorders,
+University of Missouri, Columbia, MO, USA
+
+Definition
+Diagnostic overshadowing refers to the negative bias impacting a clinician’s judgment regarding co-occurring disorders in individuals who have intellectual disabilities or other mental illness. Symptoms or behaviors that may be due to a specific mental illness are attributed to another disorder, historically Mental Retardation, without considering alternative etiology.
+
+Historical Background
+Reiss, Levtan, and Szyszko first coined the term “diagnostic overshadowing” to describe the tendency to assess individuals with intellectual disability less accurately (Reiss et al. 1982a, b; Reiss and Szyszko 1983). Subsequent research has consistently demonstrated that the cognitive deficits displayed by an individual negatively impacted the ability of clinicians to make accurate judgments with regard to other co-occurring disorders (c.f., Jopp and Keys 2001; White et al. 1995). Jopp and Keys provide a review of the concept of diagnostic overshadowing in addition to possible moderators (Jopp and Keys 2001). Their review indicated that most clinician-based variables, such as nature of clinical position (e.g., school, clinical and counseling psychologists, social workers), educational level (e.g., graduate student vs. Ph.D.), and years of experience, were not associated with the strength of the bias. Moreover, though the presence of multiple disabilities would presumably be more inherently difficult to disentangle for a diagnosing clinician, the research clearly indicated that the clinician’s perception of the cognitive deficits present in the individual being assessed was the most salient feature reducing diagnostic accuracy. Diagnostic overshadowing causes clinicians to overlook a range of comorbid mental illness in individuals with intellectual disability, including phobias, schizophrenia, avoidant personality disorder, and depression (Jopp and Keys 2001). As Jopp and Keys note, the bias potentially serves to reduce both sensitivity and specificity – two important components of accurate diagnosis. Sensitivity refers to the ability to accurately diagnose individuals who have a disorder, while specificity refers to the ability to accurately rule out individuals who do not have a particular disorder. Diagnostic overshadowing may reduce sensitivity by creating more false negatives, such as when a child with a cognitive deficit is not diagnosed with an anxiety disorder that they truly have. It may also reduce specificity by increasing the number of false positives, such as when a child is diagnosed with an intellectual disability, when they really have another disorder that has caused the cognitive deficit. Only one factor has been found to moderate the impact of diagnostic overshadowing, which is how clinicians process information, termed cognitive complexity (Jopp and Keys 2001). That is, when a clinician is able to view a patient’s behaviors in a multidimensional fashion, incorporating a wide range of thoughts, feelings, and behaviors, which in turn leads to generating multiple hypotheses, the impact of the patient’s cognitive deficits and the resulting diagnostic overshadowing can be reduced. The concept of diagnostic overshadowing has direct epidemiological implications. If diagnostic accuracy is impacted and individuals are missed with regard to a diagnosis, or misdiagnosed, then prevalence data may be misleading or incorrect. Moreover, epidemiological studies not only inform prevalence and incidence of a disorder and its associated characteristics, but can also help guide etiological understanding. For example, this was especially the case in autism wherein the initial report of the prevalence of co-occurring epilepsy in autism led to scientists to examine biological mechanisms in contrast to the non-biological theories promulgated at the time (Bryson and Smith 1998; Lotter 1974). If diagnostic overshadowing causes clinicians to overlook important co-occurring disorders, advancements in etiological understanding may also be impacted.
+
+Current Knowledge
+More recently, clinicians and researchers have extended the notion of diagnostic overshadowing beyond individuals with cognitive deficits to those with other disorders such as autism. In addition, diagnostic overshadowing has been extended beyond the diagnostic process to discussions regarding how it may impact treatment. For example, some researchers have found that diagnostic overshadowing has direct treatment implications. How an individual is diagnosed affects what treatments are recommended by their treating providers. If the treating provider is affected by diagnostic overshadowing and thus does not recognize other disorders, those other difficulties will not be appropriately treated. Minnes and Steiner found that parents of children with Down syndrome, for example, reported more problems receiving treatment for the co-occurring illnesses, such as cataracts, thyroid problems, and possible dementia (Minnes and Steiner 2009). Researchers have proposed that the same mechanism biasing clinicians who work with individuals with cognitive deficits may also apply to clinicians who work with individuals with autism. More specifically, given the wide range of cognitive abilities in addition to the other symptoms of autism, such as communication problems and other challenging behaviors, clinicians may be underdiagnosing comorbid disorders in individuals with autism, despite the accumulation of evidence that demonstrates a high prevalence of co-occurring disorders in autism such as mood disorders, attentional disorders, and behavioral disorders (Simonoff et al. 2008). Others have demonstrated how diagnostic overshadowing has impacted epidemiological research results. For example, Charman and colleagues, using the Special Needs and Autism Project sample (i.e., total population cohort of 56, 946 children in the UK ages 9–10), compared the concordance of their research-based diagnosis to the diagnoses derived from local services in children with IQs above and below 70. They found that the amount of children diagnosed with an autism spectrum disorder from local services who had cognitive impairment was less than those in that group that had been diagnosed through their epidemiological research design, 25% compared to 45% (Charman et al. 2009). These results demonstrate the potential diagnostic overshadowing bias and its impact on prevalence rates of autism depending on method of ascertainment.
+
+Future Directions
+In their 2001 review, Jopp and Keys noted four areas in need of research with regard to diagnostic overshadowing which remain relevant despite the broadening of diagnostic overshadowing beyond intellectual disability: (1) improve specification of clinical decisions that make up diagnostic overshadowing, (2) note the processes whereby diagnostic overshadowing occurs, (3) increase the appreciation of other variables such as the environment as they impact overshadowing, and (4) explore overshadowing more fully using qualitative and other methodologies (Jopp and Keys 2001). How much overshadowing actually takes place in local and “real world” clinics, as opposed to the vignettes used in the research that explore its presence, needs to be more fully explored, as well as a better delineation of how diagnostic overshadowing is impacting other diagnoses, such as autism, in addition to cognitive deficits alone.
+
+See Also
+▶Autism
+▶Epidemiology
+
+References and Reading
+Bryson, S. E., & Smith, I. (1998). Epidemiology of autism: Prevalence, associated characteristics, and implications for research and service delivery. Mental Retardation and Developmental Disabilities Research Reviews, 4, 9–103.
+Charman, T., Pickles, A., Chandler, S., Wing, L., Bryson, S., Simonoff, E., et al. (2009). Commentary: Effects of diagnostic thresholds and research vs service and administrative diagnosis on autism prevalence. International Journal of Epidemiology, 38(5), 1234–1238. author reply 1243–1234.
+Jopp, D. A., & Keys, C. B. (2001). Diagnostic overshadowing reviewed and reconsidered. American Journal on Mental Retardation, 106(5), 416–433.
+Lotter, V. (1974). Factors related to outcome in autistic children. Journal of Autism and Childhood Schizophrenia, 4(3), 263–277.
+Minnes, P., & Steiner, K. (2009). Parent views on enhancing the quality of health care for their children with fragile X syndrome, autism or Down syndrome. Child: Care, Health and Development, 35(2), 250–256.
+Reiss, S., & Szyszko, J. (1983). Diagnostic overshadowing and professional experience with mentally retarded persons. American Journal of Mental Deficiency, 87(4), 396–402.
+Reiss, S., Levitan, G. W., & McNally, R. J. (1982a). Emotionally disturbed mentally retarded people: An underserved population. American Psychologist, 37(4), 361–367.
+Reiss, S., Levitan, G. W., & Szyszko, J. (1982b). Emotional disturbance and mental retardation: Diagnostic overshadowing. American Journal of Mental Deficiency, 86(6), 567–574.
+Simonoff, E., Pickles, A., Charman, T., Chandler, T. L., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47(8), 921–929.
+White, M. J., Nichols, C. N., Cook, R. S., Spengler, P. M., Walker, B. S., & Look, K. K. (1995). Diagnostic overshadowing and mental retardation: A meta-analysis. American Journal on Mental Retardation, 100(3), 293–298.
+
+Diagnostic Process
+Johnny L. Matson and Julie Worley
+Department of Psychology, Louisiana State
+University, Baton Rouge, LA, USA
+
+Definition
+Autism spectrum disorders (ASDs) are a group of heterogeneous disorders that share overlapping diagnostic criteria. These include deficits in communication and socialization, and restricted interests and repetitive behaviors. Deficits in socialization are the hallmark of all ASDs, and deficits in this area are diagnostically required to meet criteria for all of the ASDs. Important to note is that even with these three core domains of impairment, heterogeneity across these symptoms exists on an individual basis. As such, the classification systems used to categorize the core impairments in ASD have been amended over time. Even still, the diagnostic process of ASD is complicated by numerous factors including the differential diagnosis within ASDs, a push to identify symptoms of ASD at very young ages, and the stability of ASD diagnoses over time. Thus, this entry will review these factors in regard to the diagnostic process of ASD.
+
+Historical Background
+Autism spectrum disorders were first introduced into the diagnostic nomenclature in 1980 (i.e., Diagnostic and Statistical Manual of Mental Disorders, Third Edition [DSM-III]; American Psychiatric Association [APA] 1980) under the category of pervasive developmental disorders. However, two of the currently recognized diagnoses, pervasive developmental disorder not otherwise specified and Asperger’s disorder, were not introduced as diagnostic disorders until 1987 and 1994, respectively. Although the diagnostic categories have changed throughout the different editions of the DSM, the main areas of impairment (i.e., symptom domains) have remained largely consistent. For example, deficits in interpersonal relationships, impairment in communication, and bizarre responses to the environment were the three main symptom domains in the DSM-III. Currently, the three main symptom domains include impairment in social interaction, impairment in communication, and restricted interests and repetitive behaviors (APA 2000).
+
+Current Knowledge
+ASD is an umbrella term used to encompass five disorders: autistic disorder (AD), Asperger’s disorder (AS), pervasive developmental disorder not otherwise specified (PDD-NOS), Rett’s disorder, and childhood disintegrative disorder. Given the very low incidence of these latter two conditions, the focus of this overview is related to AD, AS, and PDD-NOS. A child is referred for an assessment of ASD if developmental milestones are not met or after observations of behaviors related to diagnoses on the autism spectrum. Initial observations of symptoms or concerns regarding developmental milestones are most often made by teachers, day-care providers, pediatricians, and parents. As with other psychiatric disorders, best practices in regard to the assessment of ASD is to incorporate multiple informants and multiple methods. Informants come in the form of teachers, day-care providers, parents, grandparents, guardians, and other therapists familiar with the child (e.g., physical therapist, speech therapist). The assessment for a diagnosis of ASD should include an interview, an observation, and the administration of at least one assessment measure that has been psychometrically investigated to screen/diagnose ASD. It is also common practice to utilize measures of cognitive functioning and adaptive function to assess for a comorbid diagnosis of intellectual disability. During the entirety of the assessment sessions, clinicians assess for the triad of impairments indicative of an ASD diagnosis: deficits in communication, impairments in socialization, and the presence of repetitive motor movements (e.g., hand flapping) or intense and restricted interests (e.g., will only play with cars). Clinicians should also be mindful of the high rates of comorbid psychopathology and challenging behaviors, and should use ASD measures that also address these issues.
+
+More recently, there has been a move to diagnose ASD at very young ages. Fortunately, assessments designed to screen for symptoms of ASD in young populations have been developed. The measures with the best research to support them for this purpose are the Modified Checklist for Autism in Toddlers (M-CHAT; Robins et al. 2001) and the Baby and Infant Screen for Children with aUtIstic Traits-Part1 (BISCUIT-Part1; Matson et al. 2007). Both measures are rating scales that can be administered in 30 min or less, have determined cutoff scores, and present with sound psychometric properties. Given the push to identify symptomatology indicative of ASD at younger ages, researchers have explored the diagnostic stability of symptoms using samples of toddlers. Outcomes of such investigations have provided support for the diagnostic stability for ASD for children under age three (Worley et al. 2011). If diagnostic status changes, it is often from one ASD to another (e.g., PDD-NOS to AD; Cox et al. 1999; Eaves and Ho 2004; Kleinman et al. 2008). Thus, at this time, research supports the need and the ability to reliably diagnose ASD during the toddler years. Diagnosing ASD at very young ages is important, as early intervention is key for long-term success.
+
+Another factor to consider when choosing an assessment tool is the ability of the measure to differentiate between the various ASDs, given the blurred boundaries of the various disorders comprising the spectrum. The reader will note that with the appearance of the DSM-V, all ASDs will be collapsed together into one diagnostic category. However, for the purpose of service planning, evaluating the severity and symptom profiles will remain very important. Although ASD can be reliably differentiated from other developmental disorders, differential diagnosis between AD, AS, and PDD-NOS remains difficult. This phenomenon is largely due to the overlapping diagnostic criteria used to define these disorders in the diagnostic nomenclature. More specifically, the diagnostic criteria for PDD-NOS are ill defined with no specific number of criteria established to obtain this diagnosis. In addition, the diagnostic symptoms for AD and AS overlap exactly in the area of socialization and repetitive behavior and restricted interests. As a result, many researchers have examined differences between disorders comprising the spectrum. However, findings are largely inconsistent. Nonetheless, it is still important to assess for the different ASDs as a means of conforming with the current diagnostic classification system. Two measures that assist in the differential diagnosis between the various ASDs are Autism Spectrum Disorders Diagnostic for Child (ASD-DC; Matson and González 2007) and the Pervasive Developmental Disorders Behavior Inventory (PDDBI; Cohen and Sudhalter 1999). Both tests are rating scales that can be completed in 20 min or less.
+
+In addition to the need to differentially diagnose between different psychiatric disorders, medical conditions also need to be ruled out as symptoms of certain medical conditions may simulate symptoms of certain psychiatric disorders. As such, a medical assessment should be conducted prior to making an ASD diagnosis. The most important factors to assess during the medical evaluation would be the child’s hearing, vision, and oral functioning. Ruling out any problems with the aforementioned is vital to ensure that symptoms of ASD are not better accounted for by medical conditions. For example, individuals with ASD present with delays in communication and socialization. If a child is having trouble hearing or having oral motor problems, these challenges would affect their ability to speak and, subsequently, their ability to socialize with others. In addition, visual impairments could account for other symptoms such as failure to initiate and sustain eye contact and joint attention.
+
+Lastly, intellectual disability (ID) is a highly comorbid condition with ASD. As such, the assessment process should incorporate evaluations of both adaptive skills and intellectual functioning to assess for deficits specific to these areas. Deficits in cognition and adaptive behavior are required to meet criteria for a diagnosis of ID. The assessment of intellectual functioning also assists with the differential diagnosis of ASDs, specifically between AS and AD. For instance, individuals diagnosed with AS typically fall within the average range of cognitive functioning whereas those diagnosed with AD often have a comorbid diagnosis of ID.
+
+Conversely, individuals diagnosed with AS tend to have higher verbal than performance IQs, and those diagnosed with HFA tend to have higher performance than verbal IQs. In sum, the assessment process is conducted to arrive at a diagnosis of either AD, AS, or PDD-NOS or to rule out these diagnoses. First, AD is characterized by impairments in all three core domain areas. Children with AD are often referred for an assessment at very young ages since parents’ first concerns typically arise during the first year of life. In contrast, individuals meeting diagnostic criteria for AS are often not identified until later in childhood. Likely, this is due to deficits in socialization which are the most impairing symptom associated with a diagnosis of AS. As social demands increase with age, these deficits become more pronounced and more obvious. Thus, deficits in this area become more apparent to the outside observer as the child develops and has more social interactions with others. In addition, unlike children diagnosed with AD, language development is not delayed for children meeting diagnostic criteria for AS. Instead, individuals diagnosed with AS tend to have exceptional vocabularies. Therefore, as toddlers, there is no obvious cause for concern for children eventually meeting criteria for an AS diagnosis. Lastly, a diagnosis of PDD-NOS is given when symptoms of ASD are present, but the individual does not meet the criteria for another disorder on the spectrum. Therefore, the diagnostic category of PDD-NOS is a subthreshold category. Children comprising this diagnostic category have less severe deficits in socialization and may have minimal deficits in communication or less presentation of restricted interests or repetitive behaviors when compared to a child meeting criteria for AD. In addition, it may be that these children present with the same symptoms of a child meeting criteria for AD, but the age of onset occurs after 36 months of age.
+
+Future Directions
+The current classification system for ASD is categorical. However, this approach is problematic due to the poorly defined boundaries of the disorders comprising the autism spectrum. Due to these poorly defined boundaries, there has been a failure to find consistent differences between AD, AS, and PDD-NOS in regard to diagnostic criteria. As such, a dimensional approach to diagnosing has been proposed. This approach to diagnosing ASD is supported in the literature by researchers who have examined the underlying latent structure of symptoms of ASDs utilizing cluster analytic techniques or taxometric analyses. Although results often contradict each other, it has been suggested that the underlying taxon of ASD is dimensional (Boisjoli 2010; Verté et al. 2006). As a result of the overlap in the behavioral phenotype of ASDs, the APA (2010) has proposed revisions for ASD to be included in the DSM-V, set to be published in 2013. The revisions include utilizing a dimensional approach to diagnosing ASD. As such, there will be no subcategories of ASD, but instead one diagnostic entity referred to as autism spectrum disorder. In regard to diagnostic criteria, impairment related to socialization and communication would be amalgamated into one domain. The second domain refers to symptoms of restricted interests and repetitive behaviors. The third domain would indicate that symptoms need to be present in early childhood; however, early childhood is not further defined. Lastly, the symptoms must cause impairment in everyday functioning (APA 2010). These changes will bring about further modifications to the diagnostic process. For instance, parceling out differences between the various ASDs would no longer be necessary, since PDD-NOS, AS, and AD would no longer represent discrete diagnostic entities. However, given what will be even greater heterogeneity within the ASD diagnostic category, being able to identify symptom severity will still be critical. Even more important, existing measures that assess for symptoms of ASD would need to be renormed to follow the new diagnostic criteria and continuing emerging research.
+
+See Also
+▶Asperger Syndrome
+▶Autistic Disorder
+▶Diagnostic Interviews
+▶Pervasive Developmental Disorder Not Otherwise Specified
+
+References and Reading
+American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.
+American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders – Text revision (4th ed.). Washington, DC: Author.
+American Psychiatric Association. (2010). DSM-V. Retrieved October 20, 2010, from www.dsm5.org
+Boisjoli, J. (2010). A taxometric analysis of autism spectrum disorders in toddlers. Unpublished doctoral dissertation, Louisiana State University.
+Cohen, I. L., & Sudhalter, V. (1999). PDD behavior inventory: Professional manual. Lutz: Psychological Assessment Resources.
+Cox, A., Klein, K., Charman, T., Baird, G., Baron-Cohen, S., Swettenham, J., et al. (1999). Autism spectrum disorders at 20 and 42 months age: Stability of clinical ADI-R diagnosis. Journal of Child Psychology and Psychiatry, 40, 719–732.
+Eaves, L. C., & Ho, H. H. (2004). The very early identification of autism: Outcome to age 4 § – 5. Journal of Autism and Developmental Disorders, 34, 367–378.
+Kleinman, J. M., Ventola, P. E., Pandey, J., Verbalis, A. D., Barton, M., Hodgson, S., et al. (2008). Diagnostic stability in very young children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 38, 606–615.
+Matson, J. L., & González, M. L. (2007). Autism spectrum disorders – diagnosis – child version. Baton Rouge: Disability Consultants, LLC. Translated into Italian, Chinese, Hebrew, and Japanese.
+Matson, J. L., Boisjoli, J., & Wilkins, J. (2007). The baby and infant screen for children with autism traits (BISCUIT). Baton Rouge: Disability Consultants, LLC.
+Robins, D. L., Fein, D., Barton, M. L., & Green, J. A. (2001). The modified checklist for autism in toddlers: An initial study investigating the early detection of autism and pervasive developmental disorders. Journal of Autism and Developmental Disorders, 31, 131–144.
+Verté, S., Geurts, H. M., Roeyers, H., Rosseel, Y., Oosterlaan, J., & Sergeant, J. A. (2006). Can the children’s communication checklist differentiate autism spectrum subtypes? Autism, 10, 266–287.
+Worley, J. A., Matson, J. L., Mahan, S., Kozlowski, A. M., & Neal, D. (2011). Stability of symptoms of autism spectrum disorders in toddlers: An examination using the Baby and Infant Screen for Children with aUtIsm – Part1. Developmental Neuroreh-abilitation, 14, 36–40.
+
+Diagnostic Substitution
+Paul Shattuck
+George Warren Brown School of Social Work,
+Washington University, St. Louis, MO, USA
+
+Definition
+Diagnostic substitution has been hypothesized as one possible explanation for why growing numbers of children have been classified with a label of autism in publicly funded service systems such as special education and state systems of care for people with developmental disabilities. The term has been used in two related ways. One refers to a historical shift in the probability of being labeled with autism, whereby some proportion of children labeled with autism in recent years would have been classified with a different label had they been served by the same organization at a previous point in time. The other refers to individual children initially being labeled with one diagnosis and then being reclassified with autism at a later age. In each case, the hypothesis predicts that as enrollment tallies in the autism category increased, there would be some corresponding decrease in the number of children being enrolled and labeled in other administrative categories (e.g., ▶Intellectual Disability).
+
+Evidence testing the diagnostic substitution hypothesis has been mixed. One study of data from California’s service system for people with developmental disabilities from 1987 to 1994 found little change in the administrative prevalence of intellectual disability, whereas autism rates increased nearly fivefold (Croen et al. 2002; Croen and Grether 2003). Another study examined special education enrollment data from Minnesota for the years 1991–2001 and found no substantial decrease in administrative prevalence for other disabilities while autism enrollment counts were increasing (Gurney et al. 2003). A study using state-level special education data for the whole United States found that the growing administrative prevalence of autism from 1994 to 2003 was strongly associated with decreasing prevalence in other disability categories, though not in every state (Shattuck 2006). A study of special education enrollment in British Columbia from 1996 to 2004 found that nearly one third of growing autism prevalence was explained by children who had initially been classified with some other type of disability being relabeled with autism (Coo et al. 2008).
+
+See Also
+▶Intellectual Disability
+
+References and Reading
+Blaxill, M. F., Baskin, D. S., & Spitzer, W. O. (2003). Commentary: Blaxill, Baskin, and Spitzer on Croen et al. (2002), The changing prevalence of autism in California. Journal of Autism and Developmental Disorders, 33(2), 223–226.
+Coo, H., Ouellette-Kuntz, H., Lloyd, J., Kasmara, L., Holden, J., & Lewis, M. (2008). Trends in autism prevalence: Diagnostic substitution revisited. Journal of Autism and Developmental Disorders, 38, 1036–1047.
+Croen, L. A., & Grether, J. K. (2003). Response: A response to Blaxill, Baskin, and Spitzer on Croen et al. (2002), “The changing prevalence of autism in California”. Journal of Autism and Developmental Disorders, 33(2), 227–229.
+Croen, L. A., Grether, J. K., Hoogstrate, J., & Selvin, S. (2002). The changing prevalence of autism in California. Journal of Autism and Developmental Disorders, 32(3), 207–215.
+Gurney, J. G., Fritz, M. S., Ness, K. K., Sievers, P., Newschaffer, C. J., & Shapiro, E. G. (2003). Analysis of prevalence trends of autism spectrum disorder in Minnesota. Archives of Pediatrics and Adolescent Medicine, 157, 622–627.
+Shattuck, P. (2006). The contribution of diagnostic substitution to the growing administrative prevalence of autism in U.S. Special education. Pediatrics, 117(4), 1028–1037.
+
+Diastat
+▶Diazepam
+
+Diazepam
+Rizwan Parvez
+Yale Child Study Center, New Haven, CT, USA
+
+Synonyms
+Diastat; Valium
+
+Definition
+A long-acting anxiolytic medication in the benzodiazepine class. Diazepam is commonly used in the treatment of anxiety disorders, agitation, and spasticity. Diazepam and other medicines of this class bind to benzodiazepine receptors, enhancing the inhibitory effects of γ-aminobutyric acid (GABA). Side effects of benzodiazepines can include sedation, dizziness, fatigue, and confusion. Additionally, prolonged use of diazepam or other benzodiazepines may lead to tolerance and physical dependence.
+
+See Also
+▶Anxiety
+
+References and Reading
+Stahl, S. (2009). The prescriber’s guide: Stahl’s essential psychopharmacology (pp. 139–143). Cambridge: Cambridge University Press.
+
+DiBAS-R Validation
+Tanja Sappok
+Berlin Center for Mental Health in Intellectual
+Developmental Disabilities, Ev. Krankenhaus
+Königin Elisabeth Herzberge (KEH), Berlin,
+Germany
+
+Synonyms
+Autism spectrum disorder (ASD); Diagnostic Behavioral Assessment for Autism Spectrum disorders-Revised (DiBAS-R); Intellectual disability (ID); Social communication and interaction (SCI); Stereotyped and restrictive behaviors and sensory interests (SRS)
+
+Description
+The Diagnostic Behavioral Assessment for Autism Spectrum disorders-Revised (DiBAS-R) is a quick screening scale for adults with intellectual disability (ID) who are suspected of having autism spectrum disorder (ASD). It is based on the DSM-IV/5 and ICD-10 (APA 2013; Dilling 2014) criteria for ASD and consists of 19 items that are rated on a 4-point scale from “certainly true” to “never true.” It is completed by close caregivers and focuses on current behaviors within the past 3 months. Each question is worded in plain language (e.g., “Can you tell how he/she feels by his/her facial expression?” or “Does he/she show challenging behavior when unpredictable changes occur?”) to allow the administration by individuals without any specific knowledge of ASD. The scale is self-explanatory and thus does not require any preparatory training. Completion and scoring takes about 5 min. Factor analysis indicated two subscales, namely, the Social Communication and Interaction (SCI) and the Stereotypy, Rigidity, and Sensory Abnormalities (SRS) subscale. The result of the DiBAS-R is indicative of ASD if an individual shows increased scores on the overall score (>29) and on both subscales (> 21 on the SCI subscale and > 5 on the SRS subscale). A linear regression model revealed a significant association between the level of ID and the DiBAS-R scores, indicating an association between higher levels of ID and an increased ASD-specific symptom load. Therefore, the DiBAS-R is especially useful in individuals with mild to moderate ID. The questionnaire exists in German and English.
+
+Historical Background
+Due to the lack of appropriate screening instruments for adults with ID, a population at risk for comorbid ASD, a 20-item questionnaire, the Diagnostic Behavioral Assessment for ASD (DiBAS), was developed (Sappok et al. 2014b). It is derived from the ICD-10 and DSM-5 criteria for ASD. Information from a review of the literature focusing on symptoms that differentiate between persons with ID with and without additional ASD, an analysis of the Autism Diagnostic Observation Schedule (ADOS; Lord et al. 1989) items and the Autism-Checklist (ACL; Sappok et al. 2014c) items, and experiences from clinical experts in the field of ID and ASD endorsed the item formulation process. The items score on a 4-point ordinal Likert scale with 3 points for “certainly true,” 2 points for “often true,” 1 point for “sometimes true,” and 0 points for “never true.” The final scores range from 0 to 60 with higher scores indicating a higher ASD symptom load. These items were evaluated in terms of diagnostic validity using the final diagnostic classification of the multidisciplinary case conference as an external criterion. In a pilot study, the DiBAS was applied to 91 patients with ID and suspected ASD (Sappok et al. 2014b). The DiBAS revealed an AUC of 0.81, a sensitivity of 83%, a specificity of 64%, and a kappa of 0.47 (Sappok et al. 2014b). As 8 of the original 20 items did not differentiate sufficiently between ASD and non-ASD, in a next step, these items were replaced by another eight ICD-10/DSM-5-based questions to replenish the DiBAS-Revised (DiBAS-R).
+
+Psychometric Data
+A first study assessed the DiBAS-R in a sample that consisted of 219 adults with ID and who were suspected of having ASD who were admitted to a department of psychiatry in Berlin, Germany, from January 2012 to July 2013 (Sappok et al. 2014a). The mean age was 35 years, 57% were males, and 35% of participants were diagnosed with additional ASD. Factor analysis yielded 2 consistent dimensions with 12 items in the Social Communication and Interaction subscale (SCI) and 7 items in the Stereotypy, Rigidity, and Sensory Abnormalities subscale (SRS). The internal consistencies were Cronbach’s alpha = 0.91 for the SCI subscale, 0.84 for the SRS subscale, and 0.91 for the overall scale. The DiBAS-R revealed sensitivity and specificity values of 81% each (Sappok et al. 2014b). The reliability was excellent: inter-rater reliability ICC = 0.88 and test-re-test reliability r = 0.93. The DiBAS-R scores were highly correlated with other ASD-specific measures (Social Communication Questionnaire (SCQ) r = 0.52 Berument et al. 1999; Rutter et al. 2003; Scale of Pervasive Developmental Disorder in Mentally Retarded Persons (PDD-MRS) r = 0.5 Kraijer and de Bildt 2005; Autism-Checklist (ACL) r = 0.59 Sappok et al. 2014c). The low correlation with the Modified Overt Aggression Scale supported the divergent validity of the instrument.
+
+In a second study, an independent sample of 381 adults with ID and who were suspected of having ASD was recruited from August 2013 to December 2016 (Heinrich et al. 2018). The mean age was 40.5 years, 58% were males, and 24% were finally diagnosed with ASD. The diagnostic validity of the DiBAS-R including its given cutoff points was showing an overall agreement with the reference diagnoses of 70% (sensitivity, 82%; specificity, 67%). Sensitivity (79%) and specificity (84%) were more balanced in individuals with mild to moderate ID (sensitivity, 79%; specificity, 84%). Specificity decreased to 34% in persons with severe to profound ID, while the sensitivity was still good (83%). The level of ID as well as its interaction with ASD explained a significant proportion of the variance in the DiBAS-R scores. Thus, decreasing levels of functioning may result in an increasing overlap in ASD-like symptomatology in individuals with and without ASD, which in turn may lead to a decrease in the diagnostic utility of the DiBAS-R scores.
+
+In a third study, the combination of the DiBAS-R and the ACL were evaluated in 148 persons with ID who were suspected of having ASD (Mutsaerts et al. 2016). The specificity values increased from 75% for each instrument used alone to 88% when two positive screening results were used in combination. The sensitivity values increased from 75% for the DiBAS-R and 91% for the ACL to 95% when at least one positive screening result was used. Different combinations of the ASD screening instruments DiBAS-R and ACL lead to improvements in sensitivity and specificity. The instruments can therefore be used in a complementary way to further improve the overall accuracy of each scale.
+
+Clinical Uses
+The DIBAS-R can be used as a quick screening instrument for ASD in adults with ID. It is easy to administer and can be applied in various settings, e.g., hospitals or outpatient clinics or in population-based studies. No special knowledge of the diagnostic criteria of ASD is needed to complete the questionnaire, and no training is necessary to compute the final scoring. Thus, the scale can be used by general practitioners, psychiatrists, or psychologists to screen for ASD in research and clinical practice. The administration of the scale requires about 5 min for scoring and computing and is independent of the collaboration of the examined individual. The validity and reliability results suggest that this scale represents an efficient approach to initial ASD screening of adults with ID. However, it cannot replace a comprehensive assessment for ASD such as interview-based procedures like the Autism Diagnostic Interview-Revised (ADI-R; Lord et al. 1994) or direct behavioral observations in a standardized setting like the Autism Diagnostic Observation Schedule (ADOS; Lord et al. 1989). Its psychometric properties particularly support its use in persons with mild to moderate ID. The DiBAS-R can be applied for clinical and research purposes.
+
+See Also
+▶ADI-R
+▶ADOS
+▶PDD
+▶SCQ
+
+References and Reading
+APA. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.
+Berument, S. K., Rutter, M., Lord, C., Pickles, A., & Bailey, A. (1999). Autism screening questionnaire: Diagnostic validity. The British Journal of Psychiatry, 175, 444–451.
+Dilling, H. (Ed.). (2014). Internationale Klassifikation psychischer Störungen: ICD-10 Kapitel V (F), Klinisch-diagnostische Leitlinien (9th ed.). Bern: Huber.
+Heinrich, M., Böhm, J., & Sappok, T. (2018). Diagnosing autism in adults with intellectual disability: Validation of the DiBAS-R in an independent sample. Journal of Autism and Developmental Disorders, 48(2), 341–350.
+Kraijer, D., & de Bildt, A. (2005). The PDD-MRS: An instrument for identification of autism spectrum disorders in persons with mental retardation. Journal of Autism and Developmental Disorders, 35(4), 499–513.
+Lord, C., Rutter, M., Goode, S., Heemsbergen, J., Jordan, H., Mawhood, L., et al. (1989). Autism diagnostic observation schedule: A standardized observation of communicative and social behavior. Journal of Autism and Developmental Disorders, 19(2), 185–212.
+Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659–685.
+Mutsaerts, C. G., Heinrich, M., Sterkenburg, P. S., & Sappok, T. (2016). Screening for ASD in adults with ID-moving toward a standard using the DiBAS-R and the ACL. Journal of Intellectual Disability Research, 60, 512–522.
+Rutter, M., Bailey, A., Lord, C., & Berument, S. K. (2003). Social communication questionnaire. Los Angeles: Western Psychological Services.
+Sappok, T., Gaul, I., Bergmann, T., Dziobek, I., Bölte, S., Diefenbacher, A., et al. (2014a). The diagnostic behavioral assessment for autism spectrum disorder – Revised: A screening instrument for adults with intellectual disability suspected of autism spectrum disorders. Research in Autism Spectrum Disorders. https://doi.org/10.1016/j. rasd.2013.12.016.
+Sappok, T., Gaul, I., Dziobek, I., Bölte, S., Diefenbacher, A., & Bergmann, T. (2014b). Der Diagnostische Beobachtungsbogen für Autismus Spektrumstöungen (DiBAS): Ein Screeninginstrument für Erwachsene mit Intelligenzminderung bei Autismusverdacht. Psychiatrische Praxis, 41, 1–7.
+Sappok, T., Heinrich, M., & Diefenbacher, A. (2014c). Psychometrische Eigenschaften der Autismus-Checkliste (ACL) für erwachsene Menschen mit Intelligenzminderung. Psychiatrische Praxis, 41, 37–44.
+Sappok, T., Gaul, I., Bergmann, T., Dziobek, I., Bölte, S., Diefenbacher, A., & Heinrich, M. (2015). DiBAS-R Der Diagnostische Beobachtungsbogen für Autismusspektrum-Störungen – Revidiert. Ein Screening-Instrument für Erwachsene mit Intelligenzminderung. Bern: Hogrefe Verlag.
+
+Dichotic Listening
+Jennifer McCullagh
+Department of Communication Disorders,
+Southern Connecticut State University, New
+Haven, CT, USA
+
+Description
+Dichotic listening is the auditory process that involves listening with both ears. Dichotic listening can be broken into two different processes: binaural integration and binaural separation. Binaural integration is the ability to perceive different acoustic messages presented to the left and right ears at the same time. Binaural separation is the ability to perceive an acoustic message in one ear while ignoring a different acoustic message in the other ear. In order to perceive the acoustic messages in both ears, the outer, middle, and inner ears must be working properly, but more importantly, the auditory brainstem nuclei, auditory cortical neurons, as well as neurons in the corpus callosum must be functioning properly. Individuals with dichotic listening deficits often have difficulty hearing in the presence of background noise.
+
+Historical Background
+Dichotic speech testing was first introduced by Broadbent in 1954. It requires the simultaneous presentation of different speech stimuli to each of the ears; the listener must repeat back everything that is heard (binaural integration) or what is heard in one ear only (binaural separation). In 1961, Kimura first used these tests to demonstrate hemispheric asymmetry and cortical dysfunction. She demonstrated contralateral auditory deficits following temporal lobe lesions. These findings indicate that if a lesion is located in the left temporal lobe, the auditory signal presented to the right ear will not be perceived. In typical individuals (those with normal hearing and no known lesions in the central auditory nervous system), a right ear advantage has consistently been reported (Berlin et al. 1973; Dirks 1964; Kimura 1961a, b). Right ear advantage refers to the ability to better perceive the auditory signal presented to the right ear than the speech signal in the left ear. The right ear advantage exists because the hemisphere in the brain responsible for processing the speech signal is in the left hemisphere. Since the contralateral pathways are stronger, the speech signal presented to the right ear travels directly to the left hemisphere for processing; however, the speech signal presented to the left ear must travel to the right hemisphere and across the corpus callosum to the left hemisphere to be processed (creating a slight time delay). Currently, a variety of dichotic listening tests are available for clinical use. Common dichotic speech tests, specifically binaural integration tests, are the Dichotic Digits Test (Musiek 1983; Musiek and Wilson 1979; Musiek et al. 1979); the Staggered Spondaic Word Test (SSW; Katz 1962), and Dichotic Consonant-Vowels (Dichotic CVs; Berlin et al. 1973). Some common binaural separation tests are Competing Sentences (Willeford 1977) and the Synthetic Sentence Identification with Contralateral Competing Message (SSI-CCM; Jerger 1970).
+
+Psychometric Data
+Dichotic listening tests, such as the Dichotic Digits, Dichotic CVs, SSW, Competing Sentences, and SSI-CCM, have been shown to be sensitive and specific to central auditory nervous system lesions, including interhemispheric lesions (for review, see Musiek and Pinheiro 1985). Peripheral hearing sensitivity should be symmetrical and normal when using dichotic listening tests since any hearing differences between ears can influence test results.
+
+Clinical Uses
+Dichotic listening tests are used in clinical audiology to evaluate the central auditory processes of binaural integration and separation. These tests may be used in the assessment of children or adults with possible central auditory nervous system dysfunction. Since dichotic listening tests (tests of binaural integration and separation) evaluate two critical auditory processes, it is important for these tests to be included in a central auditory processing test battery. Clinically, the person administering dichotic listening tests should take into consideration the cognitive, hearing, speech, and language abilities of the individual being tested as these factors may affect performance. Thus, dichotic listening tests are not typically administered to children with autism, and performance on these tests should be interpreted with caution in this population.
+
+See Also
+▶Central Auditory Processing Disorder
+▶Corpus Callosum
+▶Temporal Lobes
+
+References and Reading
+Berlin, C., Lowe-Bell, S., Cullen, J., Thompson, S., & Loovis, C. (1973). Dichotic speech perception: An interpretation of right-ear advantage and temporal offset effects. Journal of the Acoustical Society of America, 53, 699–709.
+Broadbent, D. (1954). The role of auditory localization in attention and memory span. Journal of Experimental Psychology, 47, 191–196.
+Chermak, G., & Musiek, F. (1997). Central auditory processing disorders: New perspectives. San Diego: Singular Publishing Group.
+Dirks, D. (1964). Perception of dichotic and monaural verbal material and cerebral dominance for speech. Acta Otolaryngologica, 58, 73–80.
+Efron, R. (1985). The central auditory system and issues related to hemispheric specialization. In M. Pinheiro & F. Musiek (Eds.), Assessment of central auditory dysfunction: Foundations and clinical correlates (pp. 143–155). Baltimore: Williams & Wilkins.
+Jerger, J. (1970). Development of the synthetic sentence identification (SSI) as a tool for speech audiometry. In C. Rojskjaer (Ed.), Speech audiometry. Odense: Danavox.
+Katz, J. (1962). The use of staggered spondaic words for assessing the integrity of the central auditory system. Journal of Auditory Research, 2, 327–337.
+Kimura, D. (1961a). Some effects of temporal lobe damage on auditory perception. Canadian Journal of Psychology, 15, 156–165.
+Kimura, D. (1961b). Cerebral dominance and the perception of verbal stimuli. Canadian Journal of Psychology, 15, 166–171.
+Musiek, F. (1983). Assessment of central auditory dysfunction: The Dichotic Digit test revisited. Ear and Hearing, 4, 79–83.
+Musiek, F., & Pinheiro, M. (1985). Dichotic speech tests in the detection of central auditory dysfunction. In M. Pinheiro & F. Musiek (Eds.), Assessment of central auditory dysfunction: Foundations and clinical correlates (pp. 201–219). Baltimore: Williams & Wilkins.
+Musiek, F., & Wilson, D. (1979). SSWand Dichotic Digits results pre- and post-commissurotomy: A case report. Journal of Speech and Hearing Disorders, 44, 528–533.
+Musiek, F., Wilson, D., & Pinheiro, M. (1979). Audiological manifestations in split-brain patients. Journal of the American Auditory Society, 5, 25–29.
+Willeford, J. (1977). Assessing central auditory behaviour in children: A test battery approach. In R. Keith (Ed.), Central auditory dysfunction (pp. 43–73). New York: Grune & Stratton.
+
+Didactic Approaches
+Sarita Austin
+Unlocking Language, London, UK
+
+Synonyms
+ABA; Adult-/clinician-/teacher-directed approaches; Behavioral approaches; Direct instruction
+
+Definition
+A didactic approach to teaching refers to a manner of instruction in which information is presented directly from the teacher to the pupil, in which the teacher selects the topic of instruction, controls instructional stimuli, obligates a response from the child, evaluates child responses, and provides reinforcement for correct responses and feedback for incorrect ones. Intervention methods for early communication in children with autism spectrum disorder (ASD) are often divided into three categories: didactic, naturalistic, and pragmatic or developmental. Didactic approaches utilize a variety of concepts from behavioral theory, including massed trials, operant conditioning, shaping, prompting, chaining, and reinforcement. Difficulty with the generalization and maintenance of behaviors learned through this method along with the passive communication acquired by many children (i.e., waiting on adults’ lead during interactions) are some of the drawbacks associated with this approach. Still, the effectiveness of this approach in initiating and expanding expressive language and developing attention to language and comprehension in preverbal children with ASD is supported by research from numerous case studies and several group studies. As this approach requires a notable amount of adult direction, a passive role as responder by the child, repetitive drills and practice, and specific events that should occur before and after a child’s response, this method is ideally effective if the instructor consistently monitors the student’s interest level, readiness for the information being conveyed, and the motivational value of reinforcers. The passivity and prompt dependence that can result from these methods led to the development of more naturalistic instructional techniques (e.g., contemporary applied behaviors analysis).
+
+See Also
+▶Applied Behavior Analysis (ABA)
+▶Teach Me Language
+
+References and Reading
+Goldstein, H. (2002). Communication intervention for children with Autism: A review of treatment efficacy. Journal of Autism and Developmental Disorders, 32, 373–396.
+Howard, J. S., Sparkman, C. R., Cohen, H. G., Green, G., & Stanislaw, H. (2005). A comparison of intensive behavior analytic and eclectic treatments for young children with Autism. Research in Developmental Disabilities, 26(4), 359–383.
+Paul, R. (2008). Interventions to improve communication in Autism. Child and Adolescent Psychiatric Clinics of North America, 17(4), 835–856.
+Remington, B., Hastings, R., Kovshoff, H., Degli Espinosa, F., Jahr, E., Brown, T., et al. (2007). Early intensive behavioral intervention: Outcomes for children with Autism and their parents after two years. American Journal of Mental Retardation, 112, 418–438.
+Rogers, S. (2006). Evidence-based intervention for language development in young children with Autism. In T. Charman & W. Stone (Eds.), Social and communication development in autism spectrum disorders: Early identification, diagnosis, and intervention (pp. 143–179). New York: Guilford Press.
+
+Differential Ability Scales
+▶Differential Ability Scales (DAS and DAS-II)
+
+Differential Ability Scales
+(DAS and DAS-II)
+Celine A. Saulnier
+Department of Pediatrics, Emory University
+School of Medicine, Atlanta, GA, USA
+
+Synonyms
+Cognitive measures; DAS; DAS-II; Differential ability scales
+
+Description
+The Differential Ability Scales, Second Edition (DAS-II; Elliott 2007) is an individually administered test designed to measure distinct cognitive abilities for children and adolescents ages 2 years, 6 months to 17 years, 11 months. The DAS-II is comprised of individual subtests that evaluate strengths and weaknesses of a broad range of learning processes. A General Conceptual Ability (GCA) composite score is generated that reflects conceptual and reasoning abilities. Three cluster scores of the DAS-II measure more specific learning processes: verbal, nonverbal reasoning, and spatial abilities. There is also a Special Nonverbal Composite that can be derived for an individual of any age where the verbal demands are too taxing to obtain standardized results. The core subtests of the DAS-II tap into specific cognitive processes that are used to estimate the cluster and GCA scores, and the abilities they assess are directly related to educational needs at each age range. There are also Diagnostic subtests that measure memory, processing speed, and early school learning abilities. These scores do not contribute to the overall cluster or GCA scores; however, they are still important foundational skills that address a child’s profile of cognitive strengths and weaknesses, as well as educational needs.
+
+Core Batteries of the DAS-II
+There are two batteries of the DAS-II: Early Years and School Age. Within Early Years, there are two levels. The first level is for children ages 2 years, 6 months through 3 years, 5 months. This lower level consists of 4 core subtests (Verbal Comprehension [VCom], Naming Vocabulary [NVoc], Picture Similarities [PSim], and Pattern Construction [PCon]) and yields a Verbal Ability (VCom + NVoc) and Nonverbal Ability (PSim + PCon) cluster score, as well as the GCA. The upper level is for children ages 3 years, 6 months to 6 years, 11 months and has 6 core subtests (VCom, NVoc, PSim, Matrices [Mat], PCon, and Copying [Copy]) that yield three cluster scores: Verbal Ability (VCom + NVoc), Nonverbal Ability (PSim + Mat), and Spatial Ability (PCon + Copy), as well as the GCA. The School-Age battery of the DAS-II can be administered on children ages 7 years, 0 months to 17 years, 11 months, and it is comprised of six core subtests (Word Definitions [WDef], Verbal Similarities [VSim], Mat, Sequential and Quantitative Reasoning [SQR], Recall of Designs [RDes], and PCon) that yield three cluster scores: Verbal Ability, Nonverbal Reasoning Ability, and Spatial Ability, as well as the GCA. Both the Early Years and School-Age batteries of the DAS-II are normed on children between the ages of 5 years, 0 months and 8 years, 11 months. This allows the School-Age subtests to be administered for brighter young children and, in contrast, the Early Years subtests to be administered for older, less cognitively able children.
+
+Diagnostic Subtests of the DAS-II
+The Early Years battery of the DAS-II consists of the following ten Diagnostic subtests: Early Number Concepts [ENS], Matching Letter-like Forms [MLLF], Phonological Processing [PhP], Recall of Sequential Order [SeqO], Recall of Digits Forward [DigF], Recall of Digits, Backward [DigB], Speed of Information Processing [SIP], Rapid Naming [RNam], Recall of Objects – Immediate and Delayed [RObI, RObD], and Recognition of Pictures [RPic]. Seven of these subtests contribute to three cluster scores: School Readiness (ENC + MLLF + PhP), Working Memory (SeqO + DigB), and Processing Speed (SIP + RNam). The School-Age battery of the DAS-II only consists of seven Diagnostic subtests that yield two cluster scores: Working Memory (SeqO + DigB) and Processing Speed (SIP + RNam). The School Readiness subtests from the Early Years battery are not included in the School-Age norms, with the exception of PhP, which has norms up to age 12 years, 11 months.
+
+Historical Background
+The original Differential Ability Scales (DAS; Elliott 1990) was modeled after the British Ability Scales (BAS; Elliott et al. 1979). Both instruments were unique in the field of intelligence tests in that their focus was on distinct subtest scores that could be used to flush out cognitive profiles of strengths and weaknesses rather than on an overall intelligence quotient or estimation of IQ. This conceptualization of cognitive assessment sets the DAS and subsequent second edition (DAS-II; Elliott 2007) aside from other commonly used measures, such as the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV; Wechsler 2003) or Stanford-Binet Intelligence Scales, Fifth Edition (SB5; Roid 2003), where the theoretical models tend to focus more on generalized intelligence than on distinct cognitive abilities. Nevertheless, the DAS and DAS-II have an overall composite score that reflects general cognitive functioning (i.e., General Conceptual Ability score) and that is derived from those subtests which load highest on the factor of general intelligence, or g. This results in the GCA being a more refined score than other measures of global intelligence that are derived from a broader collection of subtests. However, examiners are cautioned against interpreting the GCA as a global measure of functioning, as many children have a variable cognitive profile that one general score cannot appropriately encapsulate. This is particularly the case for children with autism spectrum disorders (ASD), where scatter within a cognitive profile is the norm rather than the exception (e.g., Klin et al. 2005). Although the theoretical development of the BAS, DAS, BAS-II (Elliott 1996), and DAS-II predated theoretical work on the Cattell-Horn-Carroll theory of intelligence (CHC; McGrew 2005), the structure of the DAS-II fits well into the seven-factor CHC model. For instance, the DAS-II Verbal Ability cluster measures crystallized intelligence (Gc), the Nonverbal Reasoning cluster measures fluid intelligence (Gf), the Spatial Ability cluster measures visual-spatial processing (Gv), the Working Memory diagnostic cluster measures short-term memory (Gsm), the Recall of Objects subtest measures long-term storage and retrieval (Glr), the Processing Speed cluster measures cognitive processing speed (Gs), and the Phonological Processing subtest measures auditory processing (Ga).
+
+Psychometric Data
+The DAS-II has been standardized on a normative sample of 3480 children ages 2 years, 6 months to 17 years, 11 months that is representative of the general population. Data are also available for a range of clinical samples, including developmental risk, learning disabilities, attention deficit/hyperactivity disorder, mild to moderate intellectual disability, and the gifted and talented. On the DAS and DAS-II, Verbal, Nonverbal, Spatial, and Special Nonverbal cluster scores, as well as the GCA score, are reported in standard scores that have a mean of 100 and standard deviation of 15 and that range from 30 to 170. Individual subtest scores are reported as Tscores that have a mean of 50 and a standard deviation of 10 and that range from 10 to 90. T scores are derived from ability scores, which are based on the number of correct responses (i.e., the raw scores) and on the difficulty of administered items, following the Rasch Model of item response theory. The administration and scoring system of the DAS and DAS-II is also different from other common measures in that raw scores are computed based on the number of items administered within a response set, rather than calculating this number in addition to items below the basal. In this way, children are administered only those set of items that are appropriate in difficulty to their ability level. Subtest scores can be presented as age equivalents that represent the median ability score for each child’s performance, and descriptive categories are provided for standard scores that range from “Very High” (70 and above) to “Very Low” (69 and below).
+
+The DAS-II has strong internal reliability, with average reliability coefficients for the Early Years subtests ranging from .79 to .94 and for the School-Age subtests ranging from .74 to .96. The average reliability for the DAS-II GCA is .95 for Early Years and .96 for School Age. Confirmatory factor analyses were conducted to assess the internal validity of the DAS-II, and general results confirmed the existing clusters; for instance, the structure of cognitive abilities varies with age, with fewer models emerging for the youngest children (e.g., Verbal and Nonverbal clusters) and additional models emerging with age (e.g., Spatial, Short-term Memory, and Cognitive Speed clusters).
+
+Correlations between the DAS and DAS-II are strong, with .88 for the GCA and .85 for the SNC. The correlation between the DAS-II GCA and the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III; Wechsler 2002) Full Scale IQ is .87; however, WPPSI-III Index and FSIQ scores range from 1.7 to 5.1 points higher than DAS-II cluster scores. WISC-IV Index and FSIQ scores also range from 1.2 to 6.6 points higher than DAS-II cluster scores, with a correlation coefficient of .84 between the two measures. In nonclinical samples, the correlation between the DAS-II GCA and measures of academic achievement is as follows: .82 with the total score of the Wechsler Individual Achievement Test, Second Edition (WIAT-II; Harcourt Assessment 2005); .81 with the Comprehensive Achievement Composite of the Kaufman Test of Educational Achievement, Second Edition (KTEA-II; Kaufman and Kaufman 2004); and .80 with the Total Achievement score of the Woodcock-Johnson III Tests of Cognitive Abilities (WJ-III; Woodcock et al. 2001).
+
+Clinical Uses
+There are several clinical benefits to using the DAS-II when assessing individuals with autism spectrum disorders (ASD; Klin et al. 2005; Saulnier et al. 2011). These advantages include the following:
+1.  The teaching items that are provided within each DAS-II domain are extraordinarily useful when complex instructions impede a child’s ability to comprehend a given verbal request. When the examiner is allowed to model or demonstrate the correct response, the child is better able to comprehend the nature of the task and successfully complete a subtest on which they otherwise might have failed to obtain a basal level of performance.
+2.  The extended norms on the DAS-II Early Years battery allow for obtaining standard scores for older, more impaired individuals through age 8 years, 11 months – an option not available in other measures (Elliott 2007).
+3.  The extended norms of the School-Age battery down to age 5 allow for adequately testing younger children with ASD with more advanced cognitive skills.
+4.  The Special Nonverbal Composite makes it particularly appealing for individuals on the autism spectrum with significant language vulnerabilities for whom the language demands on the verbal tasks are too taxing. The SNC is also useful for other unique samples, such as children with speech, language, and/or hearing impairments or children who are not fluent in English.
+5.  The results can generate recommendations for educational and treatment programming that are clinically relevant to each child.
+
+The DAS-II is also extremely useful for clinical research in ASD. First, the extensive age range makes it possible to conduct scientific studies on both cohort and longitudinal studies of children between the ages of 2 and 17. Second, the extended norms allow for utilizing the same battery for varying levels of functioning. Finally, the core subtests can be administered quickly while generating a more comprehensive measure of cognitive functioning than an abbreviated measure of intelligence.
+
+There have been several studies using the DAS that highlight its utility in detecting learning disabilities and cognitive delays. For instance, in one study comparing composite scores between the DAS and WISC-III, children with learning disabilities evidenced a specific weakness in the Nonverbal Reasoning cluster of the DAS that was not demonstrated on the Perceptual Reasoning Index of the WISC-III (Dumont et al. 1996). The majority of research on cognitive profiles in autism spectrum disorders (ASD) has been conducted using the Wechsler Scales. Less research has investigated DAS and DAS-II profiles in ASD, despite the fact that many researchers have used both measures as part of the characterization process for research paradigms. A study conducted by Joseph, Tager-Flusberg, and Lord (2002) used the DAS on a longitudinal sample of children with and without ASD. They found that the majority of preschool-aged children exhibited lower verbal than nonverbal cluster scores and that greater discrepancies between verbal and nonverbal abilities were detected in ASD vs. the normative sample, with this gap widening with age. Furthermore, children with larger gaps between their verbal and nonverbal skills had greater social impairments, and impaired social functioning was independent of their verbal skills.
+
+See Also
+▶Achievement Testing
+▶Cognitive Skills
+▶Educational Testing
+▶Intelligence Quotient
+▶Psychological Assessment
+▶Standardization
+▶Standardized Tests
+▶Wechsler Preschool and Primary Scale of Intelligence
+▶Woodcock-Johnson Cognitive and Achievement Batteries
+
+References and Reading
+Dumont, R., Cruse, C., Price, L., & Whelley, P. (1996). The relationship between the Differential Ability Scales (DAS) and the Wechsler Intelligence Scale for Children, Third Edition (WISC-III) for students with learning disabilities. Psychology in the Schools, 33, 203–209.
+Elliott, C. D. (1990). Differential ability scales. San Antonio: The Psychological Corporation.
+Elliott, C. D. (1996). British ability scales (2nd ed.). Windsor: NFER-Nelson.
+Elliott, C. D. (2007). Differential ability scales (2nd ed.). New York: The Psychological Corporation.
+Elliott, C. D., Murray, D. J., & Pearson, L. S. (1979). British ability scales. Windsor: National Foundation for Educational Research.
+Harcourt Assessment. (2005). Wechsler individual achievement test (2nd ed.). San Antonio: Author.
+Joseph, R., Tager-Flusberg, H., & Lord, C. (2002). Cognitive profiles and social-communicative functioning in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 43(6), 807–821.
+Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman Assessment Battery for Children, Second Edition (KABC-II). Circle Pines: American Guidance Service.
+Klin, A., Saulnier, C. A., Tsatsanis, K., & Volkmar, F. R. (2005). Clinical evaluation in autism spectrum disorders: Psychological assessment within a transdisciplinary framework. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. 2, pp. 772–798). Hoboken: Wiley.
+McGrew, K. S. (2005). The Catell-Horn-Carroll theory of cognitive abilities: past, present, and future. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: theories, testing, and issues. New York: Guilford Press.
+Roid, G. H. (2003). Stanford-Binet intelligence scales (5th ed.). Itasca: Riverside.
+Saulnier, C. A., Quirmbach, L., & Klin, A. (2011). Clinical diagnosis of autism. In E. Hollander, A. Kolevzon, & J. T. Coyle (Eds.), Textbook of autism spectrum disorders (pp. 25–37). Washington, DC: American Psychiatric Publishing.
+Wechsler, D. (2002). The Wechsler preschool and primary scale of intelligence (3rd ed.). San Antonio: Harcourt Assessment.
+Wechsler, D. (2003). The Wechsler intelligence scale for children(4thed.). SanAntonio: ThePsychologicalCorporation.
+Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson III tests of cognitive abilities. Itasca: Riverside.
+
+Differential Reinforcement
+Thomas Zane
+Van Loan School of Graduate and Professional
+Studies, Endicott College, The Institute for
+Behavioral Studies, Beverly, MA, USA
+Department of Applied Behavior Science,
+University of Kansas, Lawrence, KS, USA
+
+Definition
+Differential reinforcement is the process of reinforcing a specific response in a particular context and not reinforcing (i.e., extinguishing) other responses. More specifically, differential reinforcement involves providing either positive or negative reinforcement for a targeted response (or targeted member of a response class) and withholding reinforcement from all other responses (or members of a response class). The withholding of reinforcement is defined as “extinction.” Thus, differential reinforcement is a two-part process – reinforcing the desired response(s) and extinguishing all other responses. For example, a parent might reinforce with praise a young child calling out the mother’s name and ignoring (and thus not reinforcing) the child’s behavior of hitting the parent. Another example would be a teacher reinforcing (with praise and attention) a child raising her hand before being called upon to answer a question and ignoring that child if she were to shout out the answer without raising her hand. The goal of differential reinforcement is to increase the strength of the response being reinforced, while weakening the strength of the other responses not being reinforced.
+
+Current Knowledge
+A basic principle in understanding differential reinforcement and how people learn in most situations is the concept of discrimination. Basically, discrimination is a process for behaving one way in one situation or context, and behaving in a completely different way in a different situation or context. Thus, discrimination is the ability to tell the difference between environmental events (or contexts or cues) and behaving accordingly. Discrimination typically develops as a result of differential reinforcement. Almost all learning occurs due to the concept of discrimination and differential reinforcement. For example, consider learning the letters of the alphabet. When the letter “B” is shown and the learner asked to identify the letter, indicating “B” will be reinforced and naming any other letter will not be. This process of differentially reinforcing the learner’s responses (as correct and incorrect) results in learning of the alphabet. Consider learning to speak. When an infant says “mama” in the presence of the mother, that response will be reinforced with smiles, hugs, and positive attention. If the infant says “mama” in the presence of the father, there will be no reinforcement. Differentially reinforcing a response in one context (i.e., in presence of the mother) and not in another (i.e., in the presence of the father) results in the baby learning what to say in the presence of each parent. Consider the acquisition of social behaviors. Some young children refuse to share their toys. When this occurs, the adult rarely reinforces such selfishness. However, when a child does in fact share her toys, adults provide positive attention and reinforcement. In this case, the adult responds differently to two different behaviors – sharing and not sharing. Through this process, the child learns that sharing is preferred and hoarding toys is not. Thus, virtually all learning is accomplished through the process of learning discriminations via differential reinforcement.
+
+The procedure of differential reinforcement has been used to both increase and decrease the strength (future rate) of specific behaviors. However, even though the goals are different (when considering increasing or decreasing future rates of behaviors), the procedure of differential reinforcement is the same. The basic procedural components of all differential reinforcement programs are these. First, the interventionist must operationally define the target behavior to be changed. That could be an appropriate behavior that must be increased in rate, a behavior deemed inappropriate that must be decreased in rate, or both. The behavior must be operationally defined to allow for both correct recording of its occurrence (so the interventionist can objectively determine if the differential reinforcement procedure is having the desired effect) as well as for accurate implementation of the procedure (i.e., so that the interventionist(s) reinforce (or not reinforce) the correct response). The second step in using differential reinforcement is to determine the actual reinforcement that will be made contingent upon the required response. This, by necessity, will vary across the individual due to the fact that what constitutes a motivating reinforcer is so personalized across individuals. However, most of the time, the interventionist will use some form of positive reinforcement, such as praise, smiles, good grades, tokens, or other forms of tangible reinforcement found desirable by the individual. On occasion, the interventionist might use a form of negative reinforcement, such as allowing the individual to escape a work demand contingent upon displaying the targeted response. For example, in the case where an individual tantrums in order to escape or avoid work, the caregiver might allow that person to take a break from work if the individual asks for a break instead of tantruming. Allowing the individual to briefly escape an unpleasant work demand negatively reinforces asking for a break. However, if the individual continues to tantrum and does not ask appropriately for a break, the caregiver would continue to keep the person in the demand situation by requiring work. The use of formal reinforcement preference assessments is considered best practice to determine the most motivating reward items available. The last step in the procedure is to determine if and how reinforcement can be withheld from the individual when she/he displays a behavior other than the targeted one. In the case of using differential reinforcement to increase the strength of an appropriate behavior, the interventionist must only reinforce the targeted appropriate behavior. In the example of a child shouting out answers instead of raising a hand, the teacher will reinforce hand raising but will have to decide exactly how to respond to the shouting out of answers. The interventionist will need to ensure that no positive reinforcement follows any behavior other than the targeted one. An important question is whether the inappropriate behavior can be ignored. In the case of shouting out an answer, it is probably the case that planned ignoring can be used effectively. However, in other situations, with other behaviors such as self-injury or aggression, planned ignoring may be difficult.
+
+There are many variations of differential reinforcement procedures that have been used. The most common ones are differential reinforcement of alternative behaviors (DRA), differential reinforcement of incompatible behaviors (DRI), differential reinforcement of other behaviors (DRO), and differential reinforcement of low rate behaviors (DRL; see “See Also” section, below).
+
+Differential reinforcement is one of the most widely used procedures to change behavior. The treatment of problem behaviors has evolved to the point that there is a common assumption that reinforcement-based procedures are considered to be best practice and the most ethical strategies to implement. The procedure is a natural one to most interventionists, in which desired behaviors are rewarded and all other behaviors not rewarded. The research has shown that differential reinforcement procedures can be very effective in changing behaviors, and – since they are based on the use of reinforcement (most of the time, positive, as opposed to negative) – many caregivers are comfortable with using such interventions. An advantage of differential reinforcement procedures is that caregivers have a systematic way to implement a technique that focuses on appropriate (positive) behaviors. Another advantage is that such procedures maintain a positive learning atmosphere and allow the instructional (or work activities) to continue in the context in which these procedures are used. A third advantage is that differential reinforcement can be effective without the addition of aversive or unpleasant procedures, such as punishment. Differential reinforcement is also a good procedure to implement when targeting problem behaviors due to the fact that this procedure can be used before and after the administration of functional assessment strategies to determine the function of that behavior. In these cases, differential reinforcement can possibly establish appropriate replacement behaviors, by orienting staff to notice and reinforce desired behaviors. This is important because differential reinforcement procedures do not address the function of challenging behaviors. That is, these procedures are used in an attempt to “override” the reinforcing function of problem behaviors.
+
+References and Reading
+Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River: Pearson.
+Karsten, A. M., & Carr, J. E. (2009). The effects of differential reinforcement of unprompted responding on the skill acquisition of children with autism. Journal of Applied Behavior Analysis, 42, 327–334.
+Lennox, D. B., Miltenberger, R. G., Spengler, P., & Erfanian, N. (1988). Decelerative treatment practices with persons who have mental retardation: A review of five years of the literature. American Journal on Mental Retardation, 92, 492–501.
+Lerman, D., Kelley, M., Vorndran, C., Kuhn, S., & LaRue, R. (2002). Reinforcement magnitude and responding during treatment with differential reinforcement. Journal of Applied Behavior Analysis, 35, 29–48.
+Mayer, G. R., Sulzer-Azaroff, B., & Wallace, M. (2012). Behavior analysis for lasting change (2nd ed.). Cornwall-on-Hudson: Sloan Publishing.
+Patel, M. R., Piazza, C. C., Martinez, C. J., Volkert, V. M., & Santana, C. M. (2002). An evaluation of two differential reinforcement procedures with escape extinction to treat food refusal. Journal of Applied Behavior Analysis, 35, 363–374.
+Ringdahl, J., Kitsukawa, K., Andelman, M., Call, N., Winborn, L., Barretto, A., et al. (2002). Differential reinforcement with and without instructional fading. Journal of Applied Behavior Analysis, 35, 291–294.
+Tiger, J. H., Bouxsein, K. J., & Fisher, W. W. (2007). Treating excessively slow responding by a young man with Asperger syndrome using differential reinforce-ment of short response latencies. Journal of Applied Behavior Analysis, 40, 559–563.
+Vollmer, T. R., & Iwata, B. (1992). Differential reinforcement as treatment for behavior disorders – procedural and functional variations. Journal of Applied Behavior Analysis, 13, 393–417.
+Vollmer, T. R., Iwata, B., Smith, R., & Rodgers, T. (1992). Reduction of multiple aberrant behaviors and concurrent development of self-care skills with differential reinforcement. Research in Developmental Disabilities, 13, 287–299.
+
+Differential Reinforcement of
+Low Rates of Responding (DRL)
+Thomas Zane1,2 and Cheryl Davis3
+1Van Loan School of Graduate and Professional
+Studies, Endicott College, The Institute for
+Behavioral Studies, Beverly, MA, USA
+2Department of Applied Behavior Science,
+University of Kansas, Lawrence, KS, USA
+37 Dimensions Consulting, Worcester, MA, USA
+
+Definition
+Differential reinforcement of low rates of responding (DRL) is a procedure in which the implementer can lower the rate of a response by reinforcing fewer incidents of that response or by reinforcing longer time intervals between incidents of the response. For example, if an individual makes profanity statements an average of 20 times per half hour, an interventionist could provide a positive reinforcer contingent upon that individual making these statements 18 or fewer times per half hour. A related term is differential reinforcement of diminishing rates (DRD). The technical difference between DRL and DRD is that in DRD, reinforcement follows a response that has been preceded by a minimum amount of time since the last response. DRL technically refers to providing reinforcement for fewer and fewer responses exhibited by the individual. However, DRL is the most common term and often refers to both of these procedures.
+
+Historical Background
+The three general areas of concern for persons with autism are social, behavior, and language. Many persons with this diagnosis display behaviors that are deemed inappropriate, such as aggression, self-stimulation, or self-injury that, if left untreated, can greatly interfere with the individual acquiring positive adaptive skills and becoming more independent. Psychologists and educators have long investigated the best treatment for these types of concerns. One approach that has been studied extensively has been the use of restrictive or punitive procedures. These involve either presenting a stimulus that is aversive or unpleasant to the individual following the occurrence of the unwanted behavior or removing a desirable stimulus following the display of the unwanted behavior. Although these procedures have been shown to be effective in eliminating a wide variety of unwanted behaviors, they have been associated with a number of negative side effects, as well as potential ethical problems, including misuse and abuse. Alternatives to punishment have been pursued vigorously in the research over the past few decades. One development has been that of functional assessment procedures, which allow the practitioner to determine the reinforcement maintaining the unwanted behaviors. Research has shown that if the reinforcement maintaining an unwanted behavior can be prevented from occurring, then the unwanted behavior will reduce in strength. Similarly, if an appropriate behavior that will earn the same reinforcement (function) as the unwanted behavior can be taught, then the individual will likely shift to the appropriate replacement behavior and reduce the occurrence of the unwanted behavior. Along with functional assessment, researchers have developed a set of procedures that emphasize the use of positive reinforcement to reduce unwanted behaviors. Among these is the DRL procedure that focuses on reinforcing less occurrences of unwanted behavior and not reinforcing higher occurrences of unwanted behavior. The findings of dozens of studies show that using reinforcement in particular ways can have the same results as punishment in stopping targeted behaviors.
+
+Current Knowledge
+Even though the DRL procedure is used to reduce rates of a problem behavior, the reinforcement is delivered after the occurrence of that behavior, which may seem counterintuitive. This is in contrast to the differential reinforcement alternative behavior (DRA), which reinforces appropriate replacement behaviors; differential reinforcement of incompatible behavior (DRI), which provides reinforcement for appropriate replacement behaviors that are physically incompatible with the targeted unwanted behaviors; or differential reinforcement of other behavior (DRO) procedure, in which the reinforcement is delivered in the absence of the target behavior. When using DRL, reinforcement occurs following an unwanted response that remains below a certain criterion or following an unwanted response that was preceded by progressively longer intervals of time from the previous response.
+
+It is important to point out that the goal of a DRL procedure is to simply reduce the rate of the targeted behavior but not to eliminate it entirely. Some behaviors that might be considered undesirable at higher rates may be acceptably tolerated at lower rates, without needing to reduce them to zero. For example, perhaps it is acceptable for a child to get out of their seat in school a few times a week, but unacceptable and intolerable if it were to occur several times an hour. A child with autism who spontaneously verbalizes movie scripts only a few times per week could be considered more tolerable than engaging in this behavior several times per half-hour period. Thus, the DRL procedure is typically used when considering reducing behavior that is considered acceptable at lower rates but not at higher levels.
+
+There are several variations of the basic DRL procedure. In “full session DRL,” the implementer provides reinforcement at the end of a session or a predetermined amount of time if the number of incidents of the undesired behavior falls at or below a predetermined criterion level. For example, a teacher divides the school day into 12 30-min sessions or time periods. Each half-hour consists of one “session.” A child engages in tantrums on an average of six per half-hour period. The initial rule for delivering reinforcement in this “full session DRL” program would be that the child engages in five or fewer tantrums in a session. As the rate drops to consistently five or fewer, a new rule would be implemented, whereby reinforcement would be made contingent upon four or fewer occurrences in the session. Over time, by gradually reducing the criterion level, the DRL program will eventually bring the rate of behavior to an acceptable level. Note also that in full session DRL, the individual has an opportunity to earn reinforcement numerous times, across the multiple sessions, since each new session signals a new opportunity.
+
+Another type of DRL is the “interval DRL,” which is a procedure for implementing DRL in which the total session is divided into equal intervals and reinforcement is provided at the end of each interval in which the number of responses during the interval is equal to or below a criterion limit. Similar to the full session DRL, this would involve taking the full session and breaking it down into smaller intervals and reinforcement could be delivered during each of those intervals. For example, a teacher divides a 30-min lunch period into three 10-min intervals. A child is out of his or her seat on an average of 21 times during the lunch period. The rule for delivering reinforcement in this “interval DRL” would be that if the child was out of seat six or fewer times in each 10-min period, reinforcement would be provided. A potential advantage of an interval DRL program is that the individual has multiple opportunities within a session to earn reinforcement, as opposed to just one opportunity (at the end of the session).
+
+A third variation of the basic DRL procedure is the “space-responding DRL” (sometimes called DRD). This is a procedure for implementing DRL in which reinforcement follows each occurrence of the target behavior that is separated from the previous response by a minimum inter-response time (IRT). For example, a child correctly answers questions asked by the teacher but answers so quickly that other students have no opportunity to be called on. The teacher makes a rule with this student that to be called on to answer a question, 3 min must have elapsed since the child last answered a question. Thus, the rule for reinforcement is that only responses that have been preceded by a minimum of 3 min from the previous response will receive reinforcement.
+
+The basic procedural components of all DRL procedures are these. First, the interventionist must operationally define the targeted unwanted behavior to be changed. This must be done to allow for both correct recording of its occurrence (so the interventionist can objectively determine if the differential reinforcement procedure is having the desired weakening effect) and accurate implementation of the procedure (i.e., so that the interventionist(s) implement the DRL plan consistently). The second step in using DRL is to determine the current “operant level” of the response. That is, the interventionist must have data showing the current rate of the behavior before implementing DRL. Depending upon the type of DRL procedure used, data might show the total number of responses during a day, the total number of responses (on average) during individual sessions, and/or the average amount of time between occurrences of the targeted undesired behavior. The third step in using DRL procedures is to determine the actual reinforcement that will be made contingent upon the response meeting the rule for earning reinforcement. This, by necessity, will vary across individuals due to the fact that what constitutes a motivating reinforcer is so personalized. However, most of the time, the interventionist will use some form of positive reinforcement, such as praise, smiles, tokens, or other forms of tangible reinforcement desired by the learner. On occasion, the interventionist might use a form of negative reinforcement, such as allowing the individual to escape a work demand contingent upon displaying the targeted response. For example, in the case where a person tantrums in order to escape or avoid work, the caregiver might allow the individual to take a break from work if she/he asks for a break instead of tantruming. In this procedure, asking for a break is negatively reinforced by allowing the individual to briefly escape an unpleasant work demand. However, if the person continues to tantrum and does not ask appropriately for a break, the caregiver would continue to keep the individual in the demand situation and constantly require work. The use of formal reinforcement preference assessments is considered a best practice to determine the most motivating reward items available.
+
+The last step in the procedure is to determine the actual rule for providing reinforcement. Three rules or criteria must be planned. First, the rule for what level of behavior will be required as the initial new criterion must be established. To determine this, the interventionist would set the initial criterion at or a little below the operant level. For example, if the operant level was ten occurrences per session or interval, the initial DRL criterion would be anywhere from eight to ten. The second rule that must be determined is the criterion for changing from the current criterion to a new, lower one. This criterion would specify the number of sessions that must be at the criterion level before changing to a new one. For example, the interventionist may decide that if the criterion was met, or below, for three consecutive sessions, the next lower criterion would be implemented. The final rule that must be established is the ultimate, terminal criterion at which point the interventionist would consider an acceptable level of the behavior and at which point the DRL plan would be discontinued. For example, if the operant level was ten per session, the interventionist may set two per session as the ultimate criterion to discontinue the DRL.
+
+An excellent example of DRL that has been shown to be effective is called the “Good Behavior Game.” This procedure involves dividing a group of individuals (such as students in a classroom) into two or more teams. The goal is to be the team with the fewest occurrences of undesired behaviors. Generally, the interventionist would periodically observe each team and note whether or not undesired behaviors are occurring. After a set period of time (e.g., end of the day, before lunch, etc.), the team with the fewest occurrences of the targeted undesired behavior will earn some type of positive reinforcer.
+
+DRL is a positive procedure in that it utilizes only reinforcement to reduce undesired behaviors. It is also advantageous in that it is more easily tolerated than a behavior-reduction procedure that only provides reinforcement for the absence of the targeted behavior (i.e., DRO). The goal of DRO is the cessation of the unwanted behavior. A complete elimination would generally be considered more difficult to achieve than allowing some (but lower) level of the target behavior. The individual exhibiting the target behavior may more easily tolerate being allowed some amount of unwanted behavior, than attempting to eliminate it altogether. That is why DRL is often successful; it results in a targeted behavior that is inappropriate at higher levels becoming appropriate and tolerated at lower levels. Lastly, DRL procedures have been shown in the literature to be effective procedures with a wide variety of individuals and target problems; thus, it has good generalization evidence.
+
+There are several considerations for using DRL most effectively. Firstly, the interventionist must recognize that the DRL procedure does not produce rapid behavior change; rather, it produces slow and gradual changes. So, one must use DRL to change behaviors that are amenable to gradual change. Secondly, practitioners should not use DRL when targeting behaviors that could be physically harmful to the individual or others (such as self-injury, aggression, etc.). For those categories of behaviors, the interventionist should use procedures that have more of an immediate impact or combine DRL with such strategies. Lastly, DRL does, by its nature, focus on the undesired behavior, rather than reinforcing appropriate replacement behaviors. This suggests that the implementer combines DRL with procedures that target and reinforce appropriate replacement behaviors.
+
+Future Directions
+When developing programs for children with autism, often part of that programming focuses on attempting to reduce problem behaviors. The set of DRL programs could be useful in that regard, depending upon the characteristics of the undesired behavior. Future directions could include clarifying the specific behavioral or contextual variables that would suggest a particular DRL program be used over another type of program, such as DRA, DRI, DRO, or more restrictive techniques. In addition, rules for determining the combination of DRL with other programs to specifically teach, model, and reinforce appropriate incompatible behaviors (to the undesired ones) would be useful for practitioners.
+
+See Also
+▶Differential Reinforcement
+
+References and Reading
+Anglesea, M. M., Hoch, H., & Taylor, B. A. (2008). Reducing rapid eating in teenagers with autism: Use of a page prompt. Journal of Applied Behavior Analysis, 41(1), 107–111.
+Barrish, H. H., Saunders, M., & Wolf, M. M. (1969). Good behavior game: Effects of individual contingencies for group consequences on disruptive behavior in a classroom. Journal of Applied Behavior Analysis, 2, 119–124.
+Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis (3nd ed.). Upper Saddle River: Pearson.
+Deitz, S. M., & Repp, A. C. (1973). Decreasing classroom misbehavior through the use of DRL schedules of reinforcement. Journal of Applied Behavior Analysis, 6, 457–463.
+Hagopian, L. P., & Kuhn, D. E. (2009). Targeting social skills deficits in an adolescent with pervasive developmental disorder. Journal of Applied Behavior Analysis, 42(4), 909–911.
+Lennox, D. B., Miltenberger, R. G., Spengler, P., & Erfanian, N. (1988). Decelerative treatment practices with persons who have mental retardation: A review of five years of the literature. American Journal on Mental Retardation, 92, 492–501.
+Mayer, G. R., Sulzer-Azaroff, B., & Wallace, M. (2019). Behavior analysis for lasting change (4th ed.). Cornwall-on-Hudson: Sloan Publishing.
+Rolider, A., & van Houten, R. (1990). The role of reinforcement in reducing inappropriate behavior: Some myths and misconceptions. In A. C. Repp & N. N. Singh (Eds.), Perspectives on the use of nonaversive and aversive interventions for persons with developmental disabilities (pp. 119–127). Sycamore: Sycamore.
+Shaw, R., & Simms, T. (2009). Reducing attention-maintained behavior through the use of positive punishment, differential reinforcement of low rates, and response marking. Behavioral Interventions, 24, 249–263.
+Wright, C. S., & Vollmer, T. R. (2002). Evaluation of a treatment package to reduce rapid eating. Journal of Applied Behavior Analysis, 35(1), 89–93.
+
+Differential Reinforcement
+Procedures of Alternative
+Behavior (DRA/DRAlt) of
+Incompatible Behavior (DRI)
+Thomas Zane
+Van Loan School of Graduate and Professional
+Studies, Endicott College, The Institute for
+Behavioral Studies, Beverly, MA, USA
+Department of Applied Behavior Science,
+University of Kansas, Lawrence, KS, USA
+
+Definition
+Differential reinforcement of alternative behaviors (DRA) and differential reinforcement of incompatible behaviors (DRI) are both procedures designed to decrease the rate of targeted unwanted behaviors. Targeted behaviors decrease due to two mechanisms – reinforcement of appropriate behaviors that will replace the unwanted behavior and the withholding of reinforcement that historically followed the unwanted behavior. DRI is often considered a type of DRA procedure. With both of these procedures, reinforcement is contingent upon specific behaviors that will replace the unwanted behavior. The nature of the replacement behaviors marks the difference between DRA and DRI. In DRI, the replacement behaviors are physically incompatible with the unwanted behavior. They both cannot be done at the same time. For example, if the unwanted behavior were out of seat, a physically incompatible behavior would be staying in seat. If the unwanted behavior were putting fingers in the mouth, a physically incompatible behavior would be putting hands in pants pockets. Thus, with a DRI procedure, the replacement behavior is both an appropriate one, as well as physically incompatible with the unwanted behavior. In DRA, there is no concern about the replacement behaviors being physically incompatible; it is simply an appropriate behavior that could fulfill the same function as the unwanted behavior. For example, if the unwanted behavior were screaming (to indicate a need to escape a work demand), an interventionist might use a DRA procedure and select an appropriate replacement behavior such as pointing to a card to signal a need to break from work. Another example would be that if the unwanted behavior were a child inappropriately calling out answers in class (without waiting for the teacher to call on her), an appropriate replacement behavior would be to raise her hand to be called on. Note that in both of these examples, the appropriate replacement behavior is not physically incompatible with the targeted unwanted behavior; both the unwanted behavior and the replacement behavior could be displayed simultaneously. In both procedures, reinforcement is delivered for the alternative or incompatible behavior, and reinforcement is withheld (extinguished) from the targeted unwanted behavior. Both procedures result in a decrease in rate of the unwanted behavior. The strength of these procedures lies in the discrimination between the two – the alternative or incompatible behaviors are reinforced, while the unwanted behaviors are not.
+
+Historical Background
+The three general areas of concern for persons with autism are social, behavior, and language. Many persons with this diagnosis display behaviors that are deemed inappropriate, such as aggression, self-stimulation, or self-injury and that greatly interfere with the person learning positive adaptive skills and increasing their independence in life. Psychologists and educators have long investigated the best treatment for these types of concerns. One approach that has been studied extensively has been the use of restrictive or punitive procedures. These involve either presenting a stimulus that is aversive or unpleasant to the individual following the occurrence of the unwanted behavior or by removing a desirable stimulus following the display of the unwanted behavior. Although these procedures have been shown to be effective in eliminating a wide variety of unwanted behaviors, they have been associated with a number of negative side effects, as well as potential ethical problems, including misuse and abuse. Alternatives to punishment have been pursued vigorously in the research over the past few decades. One development has been that of functional assessment procedures, which allow the practitioner to determine the reinforcement maintaining the unwanted behaviors. Research has shown that if the reinforcement maintaining an unwanted behavior can be prevented from occurring, then the unwanted behavior will reduce in strength. Similarly, if an appropriate behavior that will earn the same reinforcement (function) as the unwanted behavior can be taught, then the individual will likely shift to the appropriate replacement behavior and reduce the occurrence of the unwanted behavior. Along with functional assessment, researchers have developed a set of procedures that emphasize the use of positive reinforcement to reduce unwanted behaviors. Among these are the DRA and DRI procedures that focus on simultaneously reinforcing behaviors that can replace the unwanted behavior and removing the reinforcement that has maintained the targeted unwanted behavior. Findings of dozens of studies show that using reinforcement in particular ways can have the same results as punishment in stopping targeted behaviors.
+
+Current Knowledge
+Differential reinforcement procedures have been found to be some of the most frequently used procedures to reduce and eliminate unwanted behaviors, across educational, social, and vocational contexts. DRA is useful for behaviors that may occur at high or low rates, as this procedure involves teaching the individual to engage in a more appropriate behavior than the behavior targeted for reduction. Often, DRA is combined with DRI. DRI is preferable, as the student cannot engage in the targeted behavior for reduction since the reinforced response is physically incompatible with the unwanted behavior.
+
+The procedural steps for both DRA and DRI are similar. First, the implementer must operationally define the targeted unwanted behavior to be reduced or eliminated so that the implementer(s) will not deliver reinforcement after its occurrence and that there will be increased accuracy in data collection, to confirm (or not) if the differential reinforcement procedure is having the desired effect. With both of these procedures, the implementer must track the occurrence of both the targeted unwanted behaviors, as well as the alternative and incompatible ones.
+
+Second, the interventionist should determine the function of the unwanted behavior. This information is helpful when deciding the procedures to use to prevent the reinforcement of the unwanted behavior (see below), as well as in guiding the selection of the appropriate replacement behaviors, which is the third step. The implementer must operationally define one or more behaviors that will be (a) desirable alternatives to the unwanted behaviors, (b) fulfill the same function as the unwanted behaviors, and (c) preferably be physically incompatible or compete with the unwanted behaviors. For example, if the unwanted behavior is swearing when frustrated, then an alternative behavior to strengthen could be having the individual write down what is frustrating. When planning on using a DRI procedure, the implementer must define an appropriate behavior that is physically incompatible with the targeted inappropriate one. In the example of an individual swearing, an incompatible behavior to reinforce could be saying, “oh, I am so frustrated, I need help!” instead of swearing. Note that expressing frustrating using that phrase is physically incompatible with swearing.
+
+The fourth step in using DRA or DRI is to determine the actual reinforcement that will be made contingent upon the required alternative or incompatible response. The implementer will be guided by two considerations here – the function of the unwanted behavior (determined through a functional assessment) and the preferences of the individual. Reinforcement needs to be determined based upon the particular individual with whom the implementer is working, since reinforcement is so individualized. Most of the time, the implementer will use some form of positive reinforcement, such as praise, smiles, good grades, tokens, or other forms of tangible reinforcement desired by the person. On occasion, the implementer might use a form of negative reinforcement, such as allowing the individual to escape a work demand contingent upon displaying the targeted response. These procedures are referred to as differential negative reinforcement of alternative behaviors (DNRA) and differential negative reinforcement of incompatible behaviors (DNRI). For example, in the case where a person tantrums in order to escape or avoid work, the caregiver might allow the individual to take a break from work if she/he asks for a break instead of tantruming. Thus, in this procedure, asking for a break is negatively reinforced by allowing the person to briefly escape an unpleasant work demand. However, if the individual continues to tantrum and does not ask appropriately for a break, the caregiver would keep the individual in the demand situation and continue to present work demands. The use of formal reinforcement preference assessments is considered best practice to determine the most motivating reward items available.
+
+The last step in the procedure is to identify the extinction procedures to implement contingent upon the occurrence of the targeted unwanted behavior. The results of the functional assessment are critically important here. Once the reinforcement for the unwanted behavior has been determined, the interventionist must plan on how to prevent that reinforcement from occurring when the unwanted behavior is emitted. In the case of using DRA and DRI, the implementer will need to ensure that no reinforcement follows the unwanted behavior. For example, when an individual swears, how will the implementer react? In DRA and DRI, the implementer must ignore the swearing and not comment or react to it and focus on reinforcing the occurrence of the alternative or incompatible behavior.
+
+There are several advantages to DRA procedures. Of particular importance is the focus on appropriate behavior. These procedures require specification of appropriate and positive behaviors to strengthen in the individual, which will contribute to the individuals’ overall level of reinforcement. They learn what to do, not just what not to do. A second advantage of this group of procedures is that they are associated with few or no negative side effects, unlike more restrictive procedures, such as time-out, overcorrection, and other forms of punishment. Since DRA/DRI is associated with reinforcement for appropriate responding, the individual receiving the reinforcement will likely show positive affect, demonstrate generalized responding, and develop a positive relationship with the interventionist.
+
+A third and equally important advantage is that practitioners view these procedures quite positively, much more so than punitive or restrictive ones. Caregivers, thus, are more likely to carry out these procedures with greater willingness and fidelity. A final advantage is that DRA procedures are associated with long-term positive change. As the unwanted behavior decreases in strength, and the appropriate behaviors increase, there should be continued suppression and elimination of the unwanted behavior.
+
+When considering the use of this group of procedures, it has been shown that the effect on the targeted replacement behavior may take some time. Reinforcement does result in behavior change, but the change may not be that rapid. To increase the speed of behavior change, it is recommended selecting powerful reinforcers. Another way to further increase the speed of further progress, one should select alternative or incompatible behaviors that already exist in the individual’s repertoire. These appropriate behaviors should already be occurring at some level so that the implementer has opportunities to reinforce them when they occur. Although interventionists could teach a new skill or behavior as the replacement behavior, this simply complicates the effort involved. As with most behaviors that are targeted for increase, it would be important to select these appropriate behaviors that will likely be naturally reinforced in the individual’s daily environment. It is also good practice to select these alternative and incompatible behaviors that will be less effort to emit than the targeted unwanted one. As noted earlier, of equal importance is to select replacement behaviors that are incompatible with the unwanted behavior.
+
+In addition, there is a potential danger of a DRA procedure that focuses on a limited group of replacement behaviors of reducing the strength of other, equally appropriate replacement behaviors. For example, consider an unwanted behavior of screaming to escape or avoid a work situation. If the interventionist selected one appropriate replacement behavior, that of pointing to a break card, the individual may learn to use that card when a break is desired but at the same time, no longer asks for a break using words or a communication device. To avoid this potential result, the interventionist should select all replacement behaviors that could serve the same function as the targeted unwanted behavior.
+
+Lastly, the implementer must consistently reinforce the alternative and incompatible behaviors and consistently extinguish the unwanted behavior. The procedures are less effective when some instances of the alternative or incompatible behaviors are not reinforced and some instances of the unwanted behavior continue to achieve reinforcement. Extinction of the unwanted behavior seems to be important in the success of DRA/DRI. Research has shown that these procedures will be less effective if the unwanted behavior continues to result in reinforcement.
+
+Future Directions
+Differential reinforcement of alternative/incompatible behaviors should be seriously considered when planning on addressing unwanted behaviors. To use these procedures effectively, the practitioner must carefully determine the reinforcement for the unwanted behavior, plan powerful reinforcement to strengthen the appropriate behavior, and develop procedures for preventing the unwanted behavior from being rewarded. When working with individuals who display unwanted behaviors in which it may be difficult to prevent the reinforcement for those behaviors, caregivers will need to determine how to manipulate the reinforcement for the replacement behaviors in a way to promote their increase, regardless of the reinforcement for the unwanted behaviors. For example, the use of intermittent reinforcement, increased duration of reinforcement, or a greater magnitude of reinforcement for the appropriate replacement behaviors could be considered.
+
+See Also
+▶Differential Reinforcement
+
+References and Reading
+Athens, E. S., & Vollmer, T. R. (2010). An investigation of differential reinforcement of alternative behavior without extinction. Journal of Applied Behavior Analysis, 43, 569–589.
+Beare, P. L., Severson, S., & Brandt, P. (2004). The use of a positive procedure to increase engagement on-task and decrease challenging behavior. Behavior Modification, 28, 28–44.
+Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River: Pearson.
+Heinicke, M. R., Carr, J. E., & Mozzoni, M. P. (2009). Using differential reinforcement to decrease academic response latencies of an adolescent with acquired brain injury. Journal of Applied Behavior Analysis, 42, 861–865.
+Mayer, G. R., Sulzer-Azaroff, B., & Wallace, M. (2012). Behavior analysis for lasting change (2nd ed.). Cornwall-on-Hudson: Sloan Publishing.
+Petscher, E. S., & Bailey, J. (2008). Comparing main and collateral effects of extinction and differential reinforcement of alternative behavior. Behavior Modification, 32(4), 468–488.
+Petscher, E. S., Rey, C., & Bailey, J. S. (2009). A review of empirical support for differential reinforcement of alternative behavior. Research in Developmental Disabilities, 30, 409–425.
+Pipkin, C. S. P., Vollmer, T. R., & Sloman, K. N. (2010). Effects of treatment integrity failures during differential reinforcement of alternative behavior: A translational model. Journal of Applied Behavior Analysis, 43, 47–70.
+Tiger, J. H., Bouxsein, K. J., & Fisher, W. W. (2007). Treating excessively slow responding of a young man with Asperger syndrome using differential reinforce-ment of short response latencies. Journal of Applied Behavior Analysis, 40, 559–563.
+Vollmer, T. R., Roane, H. S., Ringdahl, J. E., & Marcus, B. A. (1999). Evaluating treatment challenges with differential reinforcement of alternative behavior. Journal of Applied Behavior Analysis, 32, 9–23.
+
+Differential Reinforcement
+Procedures of Other Behavior
+(DRO)
+Thomas Zane1,2 and Cheryl Davis3
+1Van Loan School of Graduate and Professional
+Studies, Endicott College, The Institute for
+Behavioral Studies, Beverly, MA, USA
+2Department of Applied Behavior Science,
+University of Kansas, Lawrence, KS, USA
+37 Dimensions Consulting, Worcester, MA, USA
+
+Definition
+Differential reinforcement of other behaviors (DRO) is a reinforcement procedure in which reinforcement is delivered for any response other than a specific target behavior. This procedure results in a decrease in that specific target behavior because that behavior is never followed by reinforcement; thus, it weakens in future rate. For example, if a child with autism displays self-stimulatory behavior in the form of waving both hands in front of his face, a DRO procedure would be to provide a positive reinforcement for a 10-s period during which his hands were not waving in front of his face. Other names for this procedure include differential reinforcement of zero occurrences or omission training.
+
+Historical Background
+When considering interventions for undesirable behaviors, interventionists initially found punishment procedures to be effective. Although such procedures as overcorrection, time-out, and response cost do indeed reduce unwanted behavior, there are often negative side effects for the individual being exposed to those procedures, and historically, there have been abuses using aversive techniques. As more research was conducted on dealing with unwanted behaviors, professionals learned that using positive procedures could be an effective tool in obtaining reductions in these behaviors. Generally speaking, differential reinforcement has been shown to both increase appropriate behaviors and reduce the strength of unwanted responses. One form of differential reinforcement that has been shown in the research to be quite effective in weakening problem behaviors is DRO. This technique has been shown to be effective across a wide variety of unwanted behaviors exhibited by a variety of individuals.
+
+Current Knowledge
+Differential reinforcement of other behaviors (DRO) is a procedure for decreasing problem behavior in which reinforcement is contingent on the absence of the problem behavior during or at specific times. DRO is perhaps the simplest of all behavior reduction procedures as it involves the simple rule of providing reinforcement whenever the specific undesirable behavior is not displayed. DRO differs from differential reinforcement of alternative behaviors (DRA) and differential reinforcement of incompatible behaviors (DRI) in that with those two procedures, reinforcement follows specific appropriate responses. In DRO, reinforcement is provided contingent upon passage of time in which the targeted undesired behavior does not occur. Note that reinforcement does not follow any specific response; it can follow any response as long as that response is not the targeted undesirable behavior. Because the “other” behaviors are not defined, no one behavior is reinforced so much that it is likely to increase in strength. But what does happen is that the targeted undesirable behavior is never reinforced, so over time, it reduces in rate.
+
+There are basically two types of DRO, whole-interval and momentary-interval. The whole-interval DRO is a procedure in which reinforcement is available at the end of a fixed interval of duration if the targeted unwanted behavior did not occur at any time throughout that interval. For example, a child with autism is often out of her seat during independent work time. A whole-interval DRO procedure could involve dividing the independent work time period into six 5-min periods. During each 5-min period, the teacher observes the child, and if the child does not get out of seat at all during a 5-min period, the teacher delivers reinforcement. However, if the child did get out of seat during a 5-min period, no reinforcement will be provided; the child will have another opportunity at the beginning of the next 5-min interval. Since the out-of-seat behavior is not reinforced by the teacher, and other behaviors are, the out-of-seat behavior should begin to diminish in rate. This procedure requires constant vigilance and observation on the part of the interventionist throughout the interval, so as to observe any occurrence of the target behavior. This DRO is appropriate for high or low rates of challenging behaviors, as the interval can be set according to the rates of challenging behaviors. Typically, one sets the interval just below the pre-intervention IRT duration of the problem behavior (see below).
+
+The momentary DRO is a procedure whereby reinforcement is available at specific moments of time and delivered contingent on the problem behavior not occurring at that those precise moments. For example, a child with autism often whines while playing at home. A caregiver could make a rule that every 2 min (and exactly at the 2-min mark), the child will be provided a reinforcer if, at that very moment of observation, there is no whining being emitted. Thus, reinforcement is delivered at the moment of observation if the individual is doing anything other than whining. Note that this DRO procedure does not demand constant vigilance and attention on the part of the interventionist as does the whole-interval DRO. Using a momentary DRO allows the interventionist to be attentive to the individual only at the precise moment specified by the DRO schedule. Whether reinforcement is delivered is not dependent upon whether the targeted behavior was present or absent before or after the moment of observation; reinforcement is entirely dependent upon whether it is occurring at the precise observational moment.
+
+There are two variations of the whole- and momentary-interval DRO procedures. The intervals can be a fixed or variable duration. Thus, a fixed-whole-interval DRO consists of the interval size being standard across all intervals. However, a variable-whole-interval DRO consists of the interval duration varying per interval. For example, the intervals could range from 5, 10, 35, 3, and so forth but varying around a set mean. Momentary-interval DRO programs can be either fixed or variable. A fixed-momentary-interval DRO consists of the interval size being standard across all intervals; a variable-momentary-interval DRO plan allows each interval to vary around some average duration. The advantage of the variable DRO is that individuals cannot predict when the interval will end and reinforcement is available. All DRO procedures target the reduction of targeted inappropriate behavior. The research on which DRO procedure to use shows mixed results; both types of DRO plans can be effective in reducing the targeted undesired behavior.
+
+The basic procedural components of all DRO procedures are these. First, the interventionist must operationally define the target behavior to be changed. That requires carefully specifying the targeted unwanted behavior to allow for both correct recording of its occurrence (so the interventionist can objectively determine if the differential reinforcement procedure is having the desired weakening effect) and accurate implementation of the procedure (i.e., so that the interventionist(s) know exactly when reinforcement should or should not be provided). The second step in using DRO is to determine the actual reinforcement that will be made contingent upon the absence of the unwanted response and how it will be delivered. This, by necessity, will vary across individuals due to the fact that what constitutes a motivating reinforcer is so personalized. However, most of the time, the interventionist will use some form of positive reinforcement, such as praise, smiles, tokens, or other forms of tangible reinforcement desired by the learner. On occasion, the interventionist might use a form of negative reinforcement, (termed differential negative reinforcement of other behavior, or DNRO) such as allowing the learner to escape a work demand contingent upon displaying the targeted response. For example, in the case where an individual tantrums in order to escape or avoid work, the caregiver might allow the person to take a break from work if she/he asks for a break instead of tantruming. In this procedure, asking for a break is negatively reinforced by allowing the person to briefly escape an unpleasant work demand. However, if the individual continues to tantrum and does not ask appropriately for a break, the caregiver would continue to keep the person in the demand situation and constantly require work. The use of formal reinforcement preference assessments is considered best practice to determine the most motivating reward items available.
+
+The third step in implementing a DRO procedure is to determine which type of DRO will be used, interval or momentary, and the criteria for establishing the initial interval size and increasing the interval size as the behavior begins to weaken. Once the type of DRO program is decided, the interventionist must determine the interval size to use to begin the procedure. Research has shown the most success is seen when the initial interval size is set small and gradually lengthened over time, as the targeted behavior reduces in rate. This should be based upon pre-intervention levels of the problem behavior and the average inter-response time (IRT) duration historically observed. The formula for calculating IRT is to divide the total number of responses observed during a certain time interval by the total amount of time of that interval. For example, if during pre-intervention conditions, the individual exhibits the target behavior, on average, ten times every hour, the mean interval between occurrences is 6 min (ten occurrences of the behavior divided by 60 min). That information can then be used to establish the initial interval size for the DRO procedure. Next, the interventionist must develop a criterion for increasing the interval duration as the DRO program demonstrates success. For example, if the practitioner begins with an interval size of 6 min and over 90% of the intervals shows no targeted problem behavior over 3 consecutive days, then the interval size could be increased to 7 min. Such a mastery criterion if developed, in advance, will result in both increased progress in decreasing the problem behavior and a procedure gradually easier to implement.
+
+The last step in the procedure is to determine exactly how to respond to the display of the targeted undesired behavior. The rule in DRO is to not provide any reinforcement (regardless of function) for its occurrence. So, the interventionist must be careful not to react in any way that could possibly provide any source of reinforcement for its occurrence. An important question is whether the inappropriate behavior can be ignored or if it is such a serious behavior that some sort of intervention must apply. In the case of shouting out an answer, it is probably the case that ignoring it can be done effectively. However, in other situations, with other behaviors such as self-injury or aggression, not reacting may be difficult, due to potential safety issues. In those cases, DRO may not be the method of choice.
+
+There are several advantages to DRO such as the procedure is positive, is easy to implement, and focuses solely on the use of reinforcement to decrease undesired behaviors. Reinforcers are not removed from the individual, and few to no negative side effects are reported. Interventionists appreciate and are more willing to use positive procedures as opposed to more aversive or unpleasant interventions. Since these procedures are generally effective and positive, they are more ethically appropriate as a treatment choice. DRO is easy for teachers to use in most classrooms and school settings and has been shown to work across a wide variety of populations and contexts. The effect of such procedures is more rapid than simply extinguishing the targeted undesired behavior; although extinction can work, the application of DRO produces quicker change. Additionally, the effects of DRO have been shown to be long-lasting, producing durable response suppression. A particular advantage of a momentary DRO is that it does not require such continuous attention, and for a busy teacher or parent, that can be a useful feature. With this procedure, at the moment of observation, the interventionist can interrupt what she/he is doing, observe whether or not the targeted undesirable behavior is occurring, and deliver (or not deliver) the reinforcement based upon that immediate observation.
+
+However, there are several potential disadvantages to DRO procedures. One is that such procedures are not designed to teach and/or increase any particular appropriate behavior. Its inherent characteristic is to focus on the absence of the targeted behavior, and there is no attempt to operationally define and strengthen an appropriate replacement behavior. Another potential limitation of this procedure is that it focuses the attention of the interventionist on the negative or undesired behavior. Since its occurrence triggers whether or not reinforcement is delivered, the interventionist is paying attention primarily to whether or not the problem behavior occurs. This may result in the individual inadvertently getting attention for the problem behavior. Thus, caregivers need to be aware of any potential reaction being given to the individual following the occurrence of the targeted unwanted behavior. Another potential disadvantage of the DRO procedures is that since reinforcement is delivered for any response other than the targeted undesired behavior, there is a risk that other behaviors equally undesirable may inadvertently be reinforced and thus strengthened. For example, consider a DRO procedure used to reduce the self-stimulatory behavior of jumping up and down repeatedly. With DRO, reinforcement is given whenever jumping is not occurring. However, if the individual is not jumping but instead waving fingers in front of the face, reinforcement would be allowed (since the rule is to provide reinforcement for any response other than jumping). This potential disadvantage is possible when working with an individual who displays a large number of undesired behaviors. If this potential exists, a recommendation would be to provide the reinforcement only when none of the undesired behaviors are occurring or to use a procedure other than DRO (such as DRA or DRI).
+
+Future Directions
+DRO procedures are effective, show long-lasting results, are relatively easy to implement, and are preferred by interventionists due to their positive nature. Further clarification of the behavioral characteristics of when to use which type of DRO would enhance its use and effectiveness. Guidelines for establishing initial interval size and criterion for increasing the interval duration would be helpful as well.
+
+See Also
+▶Differential Reinforcement
+
+References and Reading
+Conyers, C., Miltenberger, R., Romaniuk, C., Kopp, B., & Himle, M. (2003). Evaluation of DRO schedules to reduce disruptive behavior in a preschool classroom. Child and Family Behavior Therapy, 25(3), 1–6.
+Conyers, C., Miltenberger, R., Maki, A., Barenz, R., Jurgens, M., Sailer, A., et al. (2004). A comparison of response cost and differential reinforcement of other behavior to reduce disruptive behavior in a preschool classroom. Journal of Applied Behavior Analysis, 37, 411–415.
+Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis (3rd ed.). Upper Saddle River: Pearson.
+Cowdery, G., Iwata, B., & Pace, G. (1990). Effects and side effects of DRO as treatment for self-injurious behavior. Journal of Applied Behavior Analysis, 23(4), 497–506.
+Daddario, R., Anhalt, K., & Barton, L. (2007). Differential reinforcement of other behavior applied classwide in a child care setting. International Journal of Behavioral Consultation and Therapy, 3(3), 342–348.
+Hegel, M. T., & Ferguson, R. J. (2000). Differential reinforcement of other behavior (DRO) to reduce aggressive behavior following traumatic brain injury. Behavior Modification, 24(1), 94–101.
+Homer, A. L., & Peterson, L. (1980). Differential reinforcement of other behavior: A preferred response elimination procedure. Behavior Therapy, 11, 449–471.
+Kodak, T., Miltennberger, R. G., & Romaniuk, C. (2003). The effects of differential negative reinforcement of other behavior and noncontingent escape on compliance. Journal of Applied Behavior Analysis, 36, 379–382.
+Mayer, G. R., Sulzer-Azaroff, B., & Wallace, M. (2019). Behavior analysis for lasting change (4th ed.). Cornwall-on-Hudson: Sloan Publishing.
+Miltenberger, R. G. (2016). Behavior modification: Principles and procedures (6th ed.). Boston: Cengage Learning.
+
+Diffusion Tensor Magnetic
+Resonance Imaging
+Roger J. Jou1 and Lawrence H. Staib2
+1Child Study Center, Yale University School of
+Medicine, New Haven, CT, USA
+2Department of Diagnostic Radiology, Yale
+University School of Medicine, New Haven, CT,
+USA
+
+Definition
+Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) modality used in brain imaging which measures characteristics of water diffusion in vivo to make inferences on the underlying neuroanatomy, such as the structural integrity of white matter. White matter structures probed include major neuronal fiber tracts such as association (e.g., superior longitudinal fasciculus), commissure (e.g., corpus callosum), and projection (e.g., corticospinal tract) fibers. Water diffusion can be characterized at each anatomical location by the diffusion tensor, a second-order model which provides the direction and the degree of anisotropy (i.e., directionality). The diffusion tensor can be visualized as an ellipsoid and generally aligns with the underlying white matter fibers. Diffusion properties in tissue can then be captured using various numeric metrics computed from the tensor and commonly include fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), mean diffusivity (MD), and apparent diffusion coefficient (ADC), with FA being the most widely utilized in neuropsychiatric research.
+
+There are some general relationships between the aforementioned metrics and biological features of tissue. Thus, knowledge of these inferences can guide the interpretation of research findings using DTI. Each of these measures aims to characterize the restriction of water diffusion due to physical barriers such as membranes and myelin; therefore, they are used as surrogates for white matter structural integrity in DTI studies. RD is a measure of the inhibited water diffusion occurring across or perpendicular to nerve fibers. Causes of increased restriction perpendicular to the fiber (a low RD) include thicker myelin, a more water-impermeable myelin, denser packing of fibers, and/or smaller fiber diameters. Less restriction (a high RD) could be due to delayed myelination, loss of myelin, more water-permeable myelin, loss of axonal membrane integrity, looser fiber packing, disorganized fiber packing, and/or larger-diameter fibers. On the other hand, AD is a measure of water diffusion occurring along or parallel to nerve fibers. A higher AD value indicates less hindrance to water movement along axons and could be due to axonal loss and/or less-dense fiber packing. MD and ADC measure average water diffusion in all directions. Finally, FA ranges from zero to one and describes the degree to which water diffusion is directionally dependent. A value of zero means that water diffusion is isotropic; it is equally restricted in all directions such that the pattern of diffusion resembles a sphere. A value of one means that water diffusion is completely restricted to a single direction. FA is most frequently used to characterize white matter integrity. Regardless of the specific measure, however, each of these parameters provides different information about the underlying white matter architecture. Because of this, considering multiple measures has become a common approach in DTI studies.
+
+Moreover, DTI can also be used to reconstruct the 3D structure of white matter fiber tracts using a technique called tractography or “fiber tracking.” Algorithms are used to determine 3D curves which trace fibers by following the orientation of maximum water diffusion. These fibers can then be visualized resulting in spectacular images of multiple fiber pathways. Tractography is now being used regularly in the study of neuropsychiatric disorders such as autism. Previously, such white matter anatomy could only be studied by postmortem dissection or invasive tracing studies in nonhuman animals. Because of this, tractography has been referred to as “virtual dissection.”
+
+DTI has revolutionized the study of structural brain connectivity in humans and is extensively used in the field of autism research to study alterations in neural connectivity. In the past 5 years, there has been a surge in the number of DTI studies published in autism research with the overall consensus being that some level of impairment exists in structural brain connectivity likely in the direction of underconnectivity. The focus of this chapter is the application of DTI in the study of the neurobiology of autism spectrum disorders (ASD). While a brief description of the technical aspects of DTI has been provided, the reader is referred to other reading which covers the technical details of DTI in much greater detail (Mori 2007; Mori and Zhang 2006).
+
+Historical Background
+Interestingly, diffusion MRI had been known for many decades as a source of obtaining tissue contrast. Early work in the 1950s by Hahn (1950), Carr and Purcell (1954), and Torrey (1956) laid the groundwork for diffusion measurements from magnetic resonance, providing an understanding of the change in the magnetic resonance signal in the presence of water diffusion. Stejskal and Tanner (1965) advanced this formulation and incorporated the diffusion tensor. Finally, Basser and colleagues (1994) developed the acquisition strategy that allowed computation of the diffusion tensor. Using multiple acquisitions, each sensitive to diffusion in a specified direction, the diffusion tensor can be reconstructed at each location in the brain image.
+
+Current Knowledge
+At the time of this writing, there are over 30 studies using diffusion imaging, investigating the neurobiology of ASD since the first study was published in 2004 (Barnea-Goraly et al. 2004). In recent years, the number of studies has increased sharply: one study in 2004, five studies in 2007, eight studies in 2009, and 13 studies in 2010 (all reviewed below). The methods implemented in these studies are diverse, ranging from voxel-wise comparisons to tractography-based studies. Some studies use a combination of methods or other MRI modalities such as structural and/or functional MRI. The DTI studies reviewed in this chapter are presented according to their methodology which will include voxel-wise, region of interest (ROI), tractography, combination DTI, and multimodal MRI studies. White matter properties vary with age through development. Thus, in order to better appreciate the developmental aspects of ASD, these DTI studies are further subdivided into child (age <13 years), adolescent (13–20 years), and adult (age ≥ 21 years) categories based mainly on the average age of ASD participants.
+
+Voxel-Wise
+The first DTI study published in the autism research literature was a voxel-wise study. Thus, it comes as no surprise that voxel-wise studies are the most common of the DTI studies in ASD. In general, image volumes are warped to a common space, and then, groups are compared on a voxel-by-voxel basis within the white matter. A variety of statistical procedures are used to identify significant differences and control for false positives which can result from the large numbers of comparisons made (each brain contains thousands of voxels). At the time of this writing, there are 10 DTI studies which utilize a voxel-wise approach in the study of ASD. Overall, these studies demonstrate diffuse abnormalities in white matter using the previously mentioned metrics, though the most commonly reported abnormality is a reduction in FA.
+
+There are four studies which implement a voxel-wise analysis studying children with ASD. Cheung and colleagues (2009) reported on a comparison of 13 children with autism (9.3 ± 2.6 years) and 14 controls (9.9 ± 2.5 years) where FA in the autism group was significantly lower than controls in bilateral prefrontal and temporal regions, particularly in the right ventral temporal lobe adjacent to the fusiform gyrus. Additionally, FA was greater in the right inferior frontal gyrus and left occipital lobe. Barnea-Goraly and colleagues (2010) reported on a comparison of 13 children with autism (10.5 ± 2.0 years), 13 of their unaffected siblings (8.9 ± 1.9 years), and 11 controls (9.6 ± 2.1 years). Both the autism and unaffected sibling groups had widespread FA reductions in the frontal, parietal, and temporal lobes, including regions known to be important for social cognition. Within regions of reduced FA, reductions in AD with preserved RD were observed. There were no differences in white matter structure between autism and unaffected sibling groups. Sahyoun and colleagues (2010a) reported on a comparison of nine children with autism (12.8 ± 1.5 years) and 12 controls (13.3 ± 2.45 years). Controls showed increased FA within frontal white matter and the superior longitudinal fasciculus. The autism group showed increased FA within peripheral white matter, including the ventral temporal lobe. Shukla and colleagues (2011) reported on a comparison of 26 children with ASD (12.8 ± 0.6 years) and 24 controls (13.0 ± 0.6 years). The ASD group demonstrated decreased FA and increased MD and RD in numerous white matter structures: corpus callosum, anterior and posterior limbs of the internal capsule, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, cingulum, anterior thalamic radiation, and corticospinal tract. There were no areas of increased FA, reduced MD, or RD in the ASD group.
+
+There are four studies which implement a voxel-wise analysis studying adolescents with ASD. In the first published DTI study in ASD, Barnea-Goraly and colleagues (2004) reported on a comparison of seven adolescents with autism (14.6 ± 3.4 years) and nine controls (13.4 ± 2.8 years). The autism group demonstrated reduced FA in white matter adjacent to the ventromedial prefrontal cortices, anterior cingulate gyri, and temporoparietal junctions. FA reductions were also seen adjacent to the superior temporal sulcus bilaterally, temporal lobes approaching the amygdala bilaterally, occipitotemporal tracts, and corpus callosum. Cheng and colleagues (2010) compared 25 adolescents with ASD (13.71 ± 2.54 years) and 25 controls (13.51 ± 2.20 years), reporting reduced FA in the right posterior limb of internal capsule with increased RD distally and reduced AD centrally. ASD adolescents also demonstrated greater FA with reduced RD in the frontal lobe, greater FA with reduced RD in the right cingulate gyrus, greater FA with reduced RA with increased AD in the bilateral insula, greater FA with reduced RD in the right superior temporal gyrus, and greater FA with reduced RD in the bilateral middle cerebellar peduncle. Noriuchi and colleagues (2010) reported on a comparison of seven adolescents with ASD (13.96 ± 2.68 years) and seven controls (13.36 ± 2.74 years). For the ASD group, FA and AD were lower in the white matter around left dorsolateral prefrontal cortex, posterior superior temporal sulcus/temporoparietal junction, right temporal pole, amygdala, superior longitudinal fasciculus, occipitofrontal fasciculus, mid- and left anterior corpus callosum, and mid- and right anterior cingulate cortex. Higher AD values were observed in the cerebellar vermis lobules in the ASD group. Groen and colleagues (2011) reported on a comparison of 17 adolescents with autism (14.4 ± 1.6 years) and 25 controls (15.5 ± 1.8 years). Participants with autism had lower FA in the left and right superior and inferior longitudinal fasciculi which lost significance after controlling for age and IQ. MD levels were markedly increased in the autism group throughout the brain.
+
+In the two remaining voxel-wise studies, one examined adults only, and the other included subjects from the entire age range from children to adults. Bloemen and colleagues (2010) reported on a comparison of 13 adults with Asperger syndrome (39.0 ± 9.8 years) and 13 controls (37.0 ± 9.6 years). Adults with Asperger syndrome had lower FA than controls in 13 clusters which were largely bilateral and included white matter in the internal capsule; frontal, temporal, parietal, and occipital lobes; cingulum; and corpus callosum. Keller and colleagues (2007) reported on a comparison of 34 children, adolescents, and adults with ASD (18.9 ± 7.3 years) and 31 controls (18.9 ± 6.2 years). Participants with ASD had lower FA in areas within and near the corpus callosum and in the right retrolenticular portion of the internal capsule.
+
+Region of Interest (ROI)
+In using the ROI method, anatomical area(s) which are to be studied are traced for each individual participant, usually by hand and without knowledge of group membership, in order to obtain averaged measures (e.g., FA, RD) within the ROI that characterize the selected region for a particular participant. Comparisons can then be made testing for significant group differences. ROI studies are particularly useful when particular brain structures, which can be readily defined, are suspected to be abnormal. By focusing on hypothesized regions, the problem of multiple comparisons is greatly reduced. At the time of this writing, there are seven DTI studies which use an ROI approach in the study of ASD. Overall, these studies demonstrate various diffusion abnormalities in most areas studied with the most common abnormality being a reduction in FA.
+
+There are four studies which implement an ROI approach studying children with ASD. Ben Bashat and colleagues (2007) reported on a comparison of seven toddlers with autism with ages ranging from 1.8 to 3.3 years. ROI measurements in different anatomical regions revealed an increase in FA with dominance in the left hemisphere and frontal lobe. Sivaswamy and colleagues (2010) reported on a comparison of 27 children with ASD (mean age 5.0 years) and 16 controls (mean age 5.9 years) where ROIs were placed in the cerebellar peduncles. In the ASD group, there was an increase in the MD of bilateral superior cerebellar peduncles and reversal of asymmetry in FA of the middle cerebellar peduncle and inferior cerebellar peduncle. Brito and colleagues (2009) compared eight children with ASD (9.53 ± 1.83 years) and eight controls (9.57 ± 1.36 years). In the ASD group, they reported reduced FA in ROIs corresponding to the anterior corpus callosum, right corticospinal tract, posterior limb of right and left internal capsules, left superior cerebellar peduncle, and right and left middle cerebellar peduncles. Shukla and colleagues (2010) reported on a comparison of 26 children with ASD (12.7 ± 0.6 years) and 24 controls (13.0 ± 0.6 years). ASD children demonstrated reduced FA and increased RD for whole-brain white matter and ROIs corresponding to the corpus callosum and internal capsule. Additionally, there was increased MD for whole-brain white matter and ROIs corresponding to the anterior and posterior limbs of the internal capsule. Finally, reduced AD was reported for the ROI of the body of the corpus callosum, and reduced FA was also found for the ROI of the middle cerebellar peduncle.
+
+In the three remaining studies, analyses included subjects across the entire age range including children, adolescents, and adults. Lee and colleagues (2007) reported on a comparison of 43 individuals with ASD (16.2 ± 6.7 years) and 34 controls (16.4 ± 6.0 years) with ROIs capturing the superior temporal gyrus and temporal stem. In all examined regions, the ASD group demonstrated decreased FA and increased MD and RD. Lange and colleagues (2010) reported on a comparison of 30 individuals with autism (15.78 ± 5.6 years) and 30 controls (15.79 ± 5.5 years) with ROIs including superior temporal gyrus and temporal stem. Tensor skew, a measure of tensor shape, was used in addition to the more common metrics. In the superior temporal gyrus, reversed hemispheric asymmetry was reported for the autism group: tensor skew was greater on the right, and FAwas decreased on the left. Moreover, there was also increased AD bilaterally. In the right temporal stem (but not the left), increases in MD, AD, and RD were exhibited in the autism group. Alexander and colleagues (2007) reported on a comparison of 43 individuals with ASD (16.23 ± 6.70 years) and 34 controls (16.44 ± 5.97 years) using a corpus callosum ROI. There were significant group differences in white matter volume, FA, MD, and RD which appeared to be driven by an autism subgroup with small corpus callosum volumes, high MD, low FA, and increased RD. Compared to other individuals with autism or the controls, this subgroup had lower performance IQ measures.
+
+Tractography
+Tractography studies have similarities to ROI studies, except the area of interest is defined using tractography. The results of tractography are very sensitive to the method and parameters used in creating these tract volumes; thus, great care must be taken to ensure reliability and blindness. In a manner analogous to ROI studies, diffusion metrics captured within the tract volume are analyzed. In addition, geometric properties of the tracts can also be obtained (e.g., lengths, volumes). Comparisons can be made by averaging these measures and comparing means between groups. At the time of this writing, there are six DTI studies which utilize a tractography approach in the study of the neurobiology of ASD. Overall, studies using tractography demonstrate diffusion abnormalities in many fiber tracts, again with the most common abnormality being a reduction in FA.
+
+There are two studies which implement the tractography approach studying children and adolescents with ASD. Sundaram and colleagues (2008) reported on a comparison of 50 children with ASD (4.79 ± 2.43 years) and 16 controls (6.84 ± 3.45 years). Tractography was performed on frontal lobe long- and short-range pathways. The ADC was significantly higher for whole frontal lobe, long- and short-range association fibers in the ASD group. FA was significantly lower in the ASD group for short-range fibers but not for long-range fibers. There was no between-group difference in the number of frontal lobe fibers (short and long); however, the long-range association fibers of frontal lobe were significantly longer in ASD group. Fletcher and colleagues (2010) reported on a comparison of 10 adolescents with autism (14.25 ± 1.92 years) and 10 controls (13.36 ± 1.34 years), performing tractography of the arcuate fasciculus (superior longitudinal fasciculus). The results showed an increase in MD in the autism group, due mostly to an increase in the RD. Both MD and FA were less lateralized in the autism group.
+
+The remaining four tractography studies include adults with one study including participants across the entire age range. Catani and colleagues (2008) reported on a comparison of 15 adults with Asperger syndrome (31 ± 9 years) and 16 controls (35 ± 11 years). Tractography was performed on short intracerebellar connections, long-range afferent (i.e., corticopontocerebellar and spinocerebellar tracts) and efferent (i.e., superior cerebellar tracts) connections. The Asperger group had significantly lower FA in the short intracerebellar fibers and right superior cerebellar peduncles, but no difference in the afferent tracts. Conturo and colleagues (2008) reported on a comparison of 17 adults with autism (26.46 ± 2.73 years) and 17 controls (26.08 ± 2.69 years), performing tractography of hippocampo-fusiform and amygdalo-fusiform pathways. While these pathways had normal size and shape, the right hippocampo-fusiform had reduced RD compared with controls, opposite to the whole-brain effect of increased RD. In contrast, left hippocampo-fusiform, right arcuate fasciculus, and left arcuate fasciculus had increased RD and increased AD in autism. There was a general loss of lateralization compared with controls. Thomas and colleagues (2011) reported on a comparison of 12 adults with autism (28.5 ± 9.7 years) and 18 controls (22.4 ± 4.1 years), performing tractography on callosal and visual-association pathways. Compared with the control group, the autism group demonstrated an increase in the volume of the intra-hemispheric fibers, particularly in the left hemisphere, and a reduction in the volume of the forceps minor and the body of the corpus callosum. Finally, Pugliese and colleagues (2009) compared 24 children, adolescents, and adults with Asperger syndrome (23.3 ± 12.4 years) and 42 controls (25.3 ± 10.3 years), performing tractography on the following limbic pathways: inferior longitudinal fasciculus, inferior frontal occipital fasciculus, uncinate, cingulum, and fornix. There were no significant between-group differences in FA and MD. However, the Asperger group had a significantly higher number of streamlines in the right and left cingulum and in the right and left inferior longitudinal fasciculus. In contrast, the group with Asperger syndrome had a significantly lower number of streamlines in the right uncinate.
+
+Combination DTI
+While each of the DTI methods described above has limitations when used alone, these can be overcome by using the methods in combination with one another, ideally in a synergistic manner. Kumar and colleagues (2010) reported on a comparison of 32 children with ASD (mean age 5.0 years), 12 developmentally impaired children without ASD (mean age 4.6 years), and 16 controls (mean age 5.5 years). They essentially performed two separate analyses on the same group of participants: voxel-wise and tractography study. In the voxel-wise portion of the study, when the ASD and developmentally impaired children were compared with controls, FA was lower in the right uncinate fasciculus, right cingulum, and corpus callosum in both affected groups. There was also reduced FA in right arcuate fasciculus when ASD children were compared with controls and reduced FA in the bilateral inferior fronto-occipital fasciculus when developmentally impaired children were compared with controls. ADC was increased in right arcuate fasciculus in both ASD and developmentally impaired children. In the tractography portion of the study, the ASD group showed shorter length of the left uncinate fasciculus and increased length, volume, and density of the right uncinate fasciculus; increased length and density of the corpus callosum; and higher density of the left cingulum compared with the control group. Compared with the developmentally impaired group, the ASD group had increased length, volume, and density of the right uncinate fasciculus; higher volume of the left uncinate fasciculus; and increased length of the right arcuate fasciculus and corpus callosum. Jou and colleagues (2011) reported on a comparison of 10 ASD adolescents (13.06 ± 3.85 years) and 10 controls (13.94 ± 4.23 years). DTI data was analyzed in a synergistic manner by performing a voxel-wise comparison with follow-up tractography to identify underlying affected white matter structures. The regions of lower FA, as confirmed by tractography, involved the inferior longitudinal fasciculus/inferior fronto-occipital fasciculus, superior longitudinal fasciculus, and corpus callosum/cingulum. Notably, some volumes of interest were adjacent to the fusiform face area, bilaterally, corresponding to involvement of the inferior longitudinal fasciculus. The largest effect sizes were noted for volumes of interest in the right anterior radiation of the corpus callosum/cingulum and the right fusiform face area (inferior longitudinal fasciculus). Finally, Pardini and colleagues (2009) reported on a comparison of 10 adults with autism (19.7 ± 2.83 years) and 10 controls (19.9 ± 2.64 years). They compared FAwithin orbitofrontal cortex volumes defined by tractography in addition to voxel-wise comparison of FA. The low-functioning group with autism demonstrated reduced tract volume and lower mean FA values in the left orbitofrontal cortex network compared with controls.
+
+Multimodal MRI
+While an extremely powerful technology, DTI remains an indirect probe of white matter integrity based on measuring properties of restricted water diffusion. One strategy to augment this data is to use multiple modalities in search for converging evidence supporting a particular neurobiological hypothesis. At the time of this writing, there are a total of five published studies taking a multimodal MRI approach: two combining with structural MRI, two combining with functional MRI, and one combining with both structural and functional MRI.
+
+Ke and colleagues (2009) reported on a comparison of 12 children with autism (8.75 ± 2.26 years) and 10 controls (9.40 ± 2.07 years) using voxel-wise comparison of both white matter density (structural MRI) and FA (DTI). In the autism group, there was a decrease of the white matter density in the right frontal lobe, left parietal lobe, and right anterior cingulate. Moreover, there was an increase of the white matter density in the right frontal lobe, left parietal lobe, and left cingulate gyrus. The autism group also exhibited reductions of FA in the frontal lobe and left temporal lobe. Mengotti and colleagues (2011) reported on a comparison of 20 children with autism (7.00 ± 2.75 years) and 22 controls (7.68 ± 2.03 years) using a combination of voxel-wise comparison in gray/white matter and ROIs (corpus callosum, frontal, temporal, parietal, and occipital lobes) comparing ADC. Compared to controls, the autism group exhibited increased white matter volumes in the right inferior frontal gyrus, right fusiform gyrus, left precentral and supplementary motor areas, and left hippocampus. Moreover, there were increased gray matter volumes in the inferior temporal gyri bilaterally, right inferior parietal cortex, right superior occipital lobe, and left superior parietal lobule. Additionally, there were decreased gray matter volumes in the right inferior frontal gyrus and left supplementary motor area. Finally, the autism group exhibited abnormally increased ADC in the bilateral frontal cortex and left genu of the corpus callosum.
+
+Using a combination of DTI and functional MRI, Sahyoun and colleagues (2010b) reported on a comparison of 12 adolescents with autism (13.3 ± 2.1 years) and 12 controls (13.3 ± 2.5 years). DTI analysis included a tractography approach in which fiber tracking was aided by functional MRI. FA was captured within these tracts, and mean FA was compared between groups. The functional MRI included response time on pictorial problem-solving task. Autism and control groups showed similar networks: linguistic processing activated inferior frontal, superior and middle temporal, ventral visual, and temporoparietal areas, whereas visuospatial processing activated occipital and inferior parietal areas. However, the autism group activated occipitoparietal and ventral temporal areas, whereas controls activated frontal and temporal language regions. The autism group relied more heavily on visuospatial abilities as evidenced by intact connections between the inferior parietal and ventral temporal ROIs. There was impaired activation of frontal language areas in the autism group as evidenced by reduced connectivity of the inferior frontal region to the ventral temporal/middle temporal regions. In another combination DTI and functional MRI study, Thakkar and colleagues (2008) reported on a comparison of 12 ASD adults (30 ± 11 years) and 14 controls (27 ± 8 years). DTI analysis included a comparison of FA performed 2 mm below the white/gray matter boundary. Functional MRI included a saccadic paradigm where activation was compared in error versus correct antisaccades, and in both correct and error antisaccades versus fixation, both within and between groups using a random effects model. Relative to controls, the ASD group made more antisaccade errors and responded more quickly on correct trials. The ASD group also showed reduced discrimination between error and correct responses in rostral anterior cingulate cortex and reduced FA in white matter underlying anterior cingulate cortex. Finally, in the ASD group, there was increased activation on correct trials and reduced FA in rostral anterior cingulate, both of which were related to repetitive behavior. Using a combination of DTI and structural and functional MRI, Knaus and colleagues (2010) reported on a comparison of 14 ASD adolescents (age range 11–19 years) and 20 controls (age range 11–19 years). Structural MRI analysis included volumetric measurements of language areas. DTI analysis included tractography to delineate a pathway between temporal and frontal language areas to compare mean FA. Functional MRI was used to divide participants into typical (leftward) and atypical (rightward) language laterality groups. Participants with typical left-lateralized language activation had smaller frontal language region volume and higher FA of the arcuate fasciculus compared to the group with atypical language laterality, across both ASD and controls. The group with typical language asymmetry included the most right-handed controls and fewest left-handers with ASD. Atypical language laterality was more prevalent in the ASD than in controls.
+
+Future Directions
+Future directions include further refinement of DTI techniques, sophistication in the integration of multiple imaging modalities, and multi-dimensional longitudinal designs. Improvements in technology include higher scan resolution, improving signal-to-noise ratio while maintaining tolerability, and developing novel metrics with higher pathological specificity. Other improvements go beyond the tensor model to examine the directional variation of diffusion in more detail (Lo et al. 2011). Tractography faces challenges in its ability to resolve multiple fiber populations in a single voxel (e.g., crossing and kissing fibers), growing usage as a more quantitative measure, and lack of standardized technique supported by gold-standard postmortem studies. While several multimodal studies have been published, there could be tighter integration of more modalities (MRI and beyond) to create novel study designs with higher synergy. The studies reviewed in this chapter are all cross-sectional; thus, longitudinal studies would be optimal to fill in the gaps in current knowledge. In addition to longitudinal imaging across the life span, there should be longitudinal clinical assessments designed to give further meaning to imaging data.
+
+See Also
+▶Functional Connectivity
+▶Magnetic Resonance Imaging
+
+References and Reading
+Alexander, A. L., Lee, J. E., Lazar, M., Boudos, R., Dubray, M. B., Oakes, T. R., et al. (2007). Diffusion tensor imaging of the corpus callosum in Autism. NeuroImage, 34(1), 61–73.
+Barnea-Goraly, N., Kwon, H., Menon, V., Eliez, S., Lotspeich, L., & Reiss, A. L. (2004). White matter structure in autism: Preliminary evidence from diffusion tensor imaging. Biological Psychiatry, 55(3), 323–326.
+Barnea-Goraly, N., Lotspeich, L. J., & Reiss, A. L. (2010). Similar white matter aberrations in children with autism and their unaffected siblings: A diffusion tensor imaging study using tract-based spatial statistics. Archives of General Psychiatry, 67(10), 1052–1060.
+Basser, P. J., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259–267.
+Ben Bashat, D., Kronfeld-Duenias, V., Zachor, D. A., Ekstein, P. M., Hendler, T., Tarrasch, R., et al. (2007). Accelerated maturation of white matter in young children with autism: A high b value DWI study. NeuroImage, 37(1), 40–47.
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+
+DiGeorge Syndrome
+▶CATCH 22 (Chromosome 22q11 Deletion Syndrome)
+
+Digital, Block-Based Coding of
+Robots for Students with
+Autism Spectrum Disorder
+Victoria Knight1 and John Wright2
+1Faculty of Education, University of British
+Columbia, Vancouver, BC, Canada
+2School of Social and Behavioral Sciences, Marist
+College, Poughkeepsie, NY, USA
+
+Definition
+Background. Robotics and computer programming (i.e., coding) are becoming universal in K-12 STEM classrooms. Some educators and experts are calling coding the “new literacy” because every child will need to be literate in coding in order to excel in the twenty-first century (Burke et al. 2016). Participation in robotics and computer coding/programming have shown a wide range of benefits for typically developing children, including increasing student knowledge and participation in the practices of science, engineering, and in problem-solving and team building skills (e.g., Karp and Maloney 2013). Coding (i.e., programming or developing) is the process of instructing a computer, app, phone, or website. Robotics is the field of computer science dedicated to engineering robots. Students might be especially engaged when coding robots because they can use computer programming to interact with physical robots. To code a robot, students must determine the task they want the robot to complete, design the code to complete the task, and then ensure the robot receives the message in code and completes the desired task. Block-based coding is a type of coding used primarily to teach introductory coding skills to children and includes predesigned sets of codes the child “drags and drops” from a computer program in order to create a set of instructions. These instructions can be used to run a computer program, control a robot, or manipulate a variety of technologies. One type of block-based coding editor is Ozo-blocky, which allows for students to increase the complexity of their coding skills over time, leading to learning of functions and even text-based coding. Learning these block-based coding skills could be a precursor to understanding more advanced skills, such as learning a computer programming language in which text-based coding is used (e.g., JAVA).
+
+Research. Preliminary evidence suggests youth with autism spectrum disorder (ASD) and challenging behavior can learn how to manipulate a robot’s actions after being explicitly taught how to drag and drop code using block-based code (Knight et al. 2019a, b). High school students in the Knight et al. (2019a, b) study also created novel codes and generalized the skills in a self-directed manner by determining the steps they wanted the robot to follow, programming for those steps, and then assessing whether the robots followed the desired sequence (problem-solving if the robots did not). In a similar study, an elementary-aged student with ASD and challenging behavior was explicitly taught how to program robots by creating color sequences of code using paper and markers for the robots to follow (Knight et al. 2019a, b). In this study, the student learned how to calibrate, draw various tracks for the robot to follow, and draw a three-color code which programmed the robot to move at nitro speed. This student also generalized the coding skill to novel codes and without prompting, created his own three-dimensional paper robot, complete with numerical formulas the robot used to follow directions. None of the children in either study exhibited challenging behavior at any time throughout the study, even when asked questions for which they did not know the answers (e.g., “Code the robot to go fast” during baseline sessions).
+
+Why teach coding of robotics to children with ASD? Including students with ASD in coding and robotics opportunities throughout their K-12 education could lead to future hobbies, career goals, or an interest in pursuing science, technology, engineering, and math (STEM) college courses or majors. Wei et al. (2013) found individuals with ASD less likely than their typically developing peers and peers with other disabilities to enter college; however, individuals with ASD were enrolled in STEM majors at a disproportionally higher rate than their peers. Increasingly, the tech sector (e.g., Microsoft) is examining ways to hire the untapped talent pool of employees with ASD, including allowing performance-based interviews rather than traditional face to face ones. Many of these companies report “finding a return on their investment” in terms of hiring employees with ASD (Eng 2018). Longitudinal studies should examine whether students with ASD who are exposed to STEM skills in a similar way to the students in the current study are more likely to be successful in STEM courses, fields, and careers in their futures.
+
+References and Reading
+Burke, Q., O’Byrne, W. I., & Kafai, Y. B. (2016). Computational participation: Understanding coding as an extension of literacy instruction. Journal of Adolescent & Adult Literacy, 59(4), 371–375.
+Eng, D. (2018, July 24). Where autistic workers thrive. Fortune Media IP Limited. Retrieved from https://fortune.com/2018/06/24/where-autistic-workers-thrive/
+Geist, E. (2016). Robots, programming and coding, oh my! Childhood Education, 92, 547–554.
+Gülbahar, Y., & Kalelioglu, F. (2014). The effects of teaching programming via scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13, 33–50.
+Karp, T., & Maloney, P. (2013). Exciting young students in grades K-8 about STEM through an afterschool robotics challenge. American Journal of Engineering Education, 4, 39–54.
+Knight, V. F., Wright, J., Wilson, K., & Hooper, A. (2019a). Teaching digital, block-based coding of robots to high school students with autism spectrum disorder and challenging behavior. Journal of Autism and Developmental Disorders, 49, 3113–3126. https://doi.org/10.1007/s10803-019-04033-w.
+Knight, V. F., Wright, J., & DeFreese, A. (2019b). Teaching robotics coding to a student with ASD and severe problem behavior. Journal of Autism and Developmental Disorders, 49, 2632–2636. https://doi.org/10.1007/s10803-019-03888-3.
+National Science Foundation. (2017). National Center for Science and Engineering Statistics. Women, Minorities, and Persons with Disabilities in Science and Engineering: 2017. Special Report NSF 17-310. Arlington.
+Sullivan, F. R., & Heffernan, J. (2016). Robotic construction kits as computational manipulatives for learning in the STEM disciplines. Journal of Research on Technology in Education, 48, 105–128. https://doi.org/10.1080/15391523.2016.1146563.
+Taylor, M. S. (2018). Computer programming with pre-K through first-grade students with intellectual disabilities. The Journal of Special Education, 52, 78–88. https://doi.org/10.1177/0022466918761120.
+Taylor, M. S., Vasquez, E., & Donehower, C. (2017). Computer programming with early elementary students with down syndrome. Journal of Special Education Technology, 32, 149–159. https://doi.org/10.1177/0162643417704439.
+Wei, X., Yu, J. W., Shattuck, P., McCracken, M., & Blackorby, J. (2013). Science, technology, engineering, and mathematics (STEM) participation among college students with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 43, 1539–1546. https://doi.org/10.1007/s10803-012-1700-z.
+
+Digitigrade Gait
+▶Toe Walking
+
+Dimensional Versus
+Categorical Classification
+Andrew Pickles
+School of Epidemiology and Health Science,
+University of Manchester, Manchester, UK
+
+Synonyms
+Class versus variable; Discrete versus continuous
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