GAE-BEG Model: A novel GNN Framework integrating neuroimaging and behavioral information to understand Adolescent Psychiatric Disorders

Music City Center 

Functional connectivity (FC) provides insights into multiple psychiatric disorders, yet the substantial inter-subject variability of FC hinders its effectiveness in distinguishing between various psychiatric disorders. Therefore, we propose a novel graph learning framework that integrates FC with behavioral characteristics to better differentiate between psychiatric disorders. Additionally, applying Grad-CAM enhances model interpretability by identifying key regions of interest (ROIs) involved in distinguishing individuals with psychiatric disorders from those without. Preliminary experiments using the ABCD dataset revealed two key findings: first, critical regions including the thalamus, putamen, and pallidum, along with nodes from the somatomotor and cingulo-opercular networks, are essential for distinguishing psychiatric disorders. Additionally, visualization of latent representations indicated that individuals with externalizing disorders, specifically Oppositional Defiant Disorder (ODD), are notably distinguishable from healthy controls. These results highlight the potential of our graph learning framework in identifying psychiatric disorders.