Abstract
This study employed Graph Neural Networks (GNNs), specifically an unsupervised GNN, to extract node embeddings from brain regions in both Autism Spectrum Disorder (ASD) and control groups. The objective was to identify potential biomarkers by analyzing node embeddings extracted from a graph model based on functional Magnetic Resonance Imaging (fMRI) data. Permutation tests were conducted to identify regions with significant differences in their embeddings between the two groups. Our results revealed several regions exhibiting significant differences, including the cerebellum, temporal lobe, and occipital lobe. These findings align with previous studies on ASD. Moreover, novel regions such as Vermis_3, Vermis_4_5, Fusiform areas, Parietal, and Cuneus were identified, emphasizing the need for further investigation. This study underscores the potential of GNNs in analyzing brain networks for ASD biomarker discovery. The identified regions warrant additional validation and exploration to understand their association with specific domains of ASD symptoms. Our approach presents a promising avenue to advance the diagnosis of ASD and to improve our understanding of its underlying neural basis.