Identifying Robust Biomarkers of Neurological Disorders from Functional MRI Using Graph Neural Networks

Graph neural networks (GNNs) have emerged as a powerful tool for modeling functional magnetic resonance imaging (fMRI) data to identify potential biomarkers of neurological disorders. Recent studies have reported significant improvements in disorder classification performance and highlighted salient features that could serve as potential biomarkers. However, the robustness of these potential biomarkers remains a key challenge.