  Recently, the application of deep learning models to diagnose
neuropsychiatric diseases from brain imaging data has received more and more
attention. However, in practice, exploring interactions in brain functional
connectivity based on operational magnetic resonance imaging data is critical
for studying mental illness. Since Attention-Deficit and Hyperactivity Disorder
(ADHD) is a type of chronic disease that is very difficult to diagnose in the
early stages, it is necessary to improve the diagnosis accuracy of such illness
using machine learning models treating patients before the critical condition.
In this study, we utilize the dynamics of brain functional connectivity to
model features from medical imaging data, which can extract the differences in
brain function interactions between Normal Control (NC) and ADHD. To meet that
requirement, we employ the Bayesian connectivity change-point model to detect
brain dynamics using the local binary encoding approach and kernel hierarchical
extreme learning machine for classifying features. To verify our model, we
experimented with it on several real-world children's datasets, and our results
achieved superior classification rates compared to the state-of-the-art models.
