BioDiag-Net: Dynamic Multisite Graph Convolutional Network for Computer-Aided ASD Diagnosis with Biomarker Visualization

Abstract

Autism spectrum disorder (ASD), characterized by social communication deficits and restricted/repetitive behaviors, faces limitations in conventional behavioral diagnostics. We propose BioDiag-Net, a deep learning-based dynamic graph convolutional network that integrates spatial brain topology with resting-state fMRI (rs-fMRI) functional connectivity. The architecture adaptively fuses whole-brain features with default mode network (DMN) dynamics through mutual information-driven selection, effectively suppressing noise while preserving neurobiologically relevant patterns. Evaluated on the ABIDE I dataset (1,035 multisite rs-fMRI scans), BioDiag-Net achieves 83.48% accuracy in ASD classification, capturing both global network reorganization and localized DMN aberrations. Biomarker visualization enables interpretable neuroimaging diagnosis, revealing neurophysiological substrates, providing interpretable diagnostic evidence while demonstrating transfer potential for cross-disorder neuroimaging analysis. This work bridges computational neuroscience with clinical practice, advancing personalized psychiatric diagnostics.