A Novel GNN Framework Integrating Neuroimaging and Behavioral Information to Understand Adolescent Psychiatric Disorders
Keywords: adolescent physiatric disorder, neurobehavior, graph autoencoder
TL;DR: Graph learning framework integrates functional connectivity and behavioral characteristics
Abstract: Functional connectivity (FC) is widely used to study various psychiatric disorders, but its consistency is often undermined by significant inter-subject variability. While these differences can be reflected by behavioral characteristics, few studies have combined them with FC. To this end, we propose a novel graph learning framework that enhances differentiation of psychiatric disorders by integrating FC with behavioral characteristics. Additionally, we apply Grad-CAM to enhance model interpretability by identifying key regions of interest involved in distinguishing individuals with psychiatric disorders from healthy controls. Experiments with the Adolescent Brain Cognitive Development dataset highlighted two critical insights: the thalamus and specific ROIs within the somatomotor and cingulo-opercular networks are vital for identifying psychiatric disorders. Additionally, visualization of latent representations indicated that individuals with externalizing disorders, specifically Oppositional Defiant Disorder, are distinguishable from healthy controls. These findings highlight the potential of our graph learning framework in discerning psychiatric disorders, offering potential for enhanced diagnostic accuracy.
Primary Subject Area: Integration of Imaging and Clinical Data
Secondary Subject Area: Interpretability and Explainable AI
Paper Type: Both
Registration Requirement: Yes
Visa & Travel: Yes
Submission Number: 110
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