ASD-FCGCN: A biologically interpretable graph convolutional network for ASD diagnosis from rs-fMRI

Background and Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental condition traditionally diagnosed through subjective behavioral assessments. Resting-state functional MRI (rs-fMRI) provides a potential pathway for objective neuroimaging biomarker discovery; however, existing deep learning methods often struggle to integrate neurobiological priors with data-driven representations. This study aimed to address these limitations by developing a biologically interpretable graph-learning framework for rs-fMRI-based ASD classification.