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Developing a new diagnostic models based on the underlying biologicalmechanisms rather than subjective symptoms for psychiatric disorders is anemerging consensus. Recently, machine learning-based classifiers usingfunctional connectivity (FC) for psychiatric disorders and healthy controls aredeveloped to identify brain markers. However, existing machine learningbaseddiagnostic models are prone to over-fitting (due to insufficient trainingsamples) and perform poorly in new test environment. Furthermore, it isdifficult to obtain explainable and reliable brain biomarkers elucidating theunderlying diagnostic decisions. These issues hinder their possible clinicalapplications. In this work, we propose BrainIB, a new graph neural network(GNN) framework to analyze functional magnetic resonance images (fMRI), byleveraging the famed Information Bottleneck (IB) principle. BrainIB is able toidentify the most informative edges in the brain (i.e., subgraph) andgeneralizes well to unseen data. We evaluate the performance of BrainIB against8 popular brain network classification methods on two multi-site, largescaledatasets and observe that our BrainIB always achieves the highest diagnosisaccuracy. It also discovers the subgraph biomarkers which are consistent toclinical and neuroimaging findings.

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck