  Developing a new diagnostic models based on the underlying biological
mechanisms rather than subjective symptoms for psychiatric disorders is an
emerging consensus. Recently, machine learning-based classifiers using
functional connectivity (FC) for psychiatric disorders and healthy controls are
developed to identify brain markers. However, existing machine learningbased
diagnostic models are prone to over-fitting (due to insufficient training
samples) and perform poorly in new test environment. Furthermore, it is
difficult to obtain explainable and reliable brain biomarkers elucidating the
underlying diagnostic decisions. These issues hinder their possible clinical
applications. In this work, we propose BrainIB, a new graph neural network
(GNN) framework to analyze functional magnetic resonance images (fMRI), by
leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to
identify the most informative edges in the brain (i.e., subgraph) and
generalizes well to unseen data. We evaluate the performance of BrainIB against
8 popular brain network classification methods on two multi-site, largescale
datasets and observe that our BrainIB always achieves the highest diagnosis
accuracy. It also discovers the subgraph biomarkers which are consistent to
clinical and neuroimaging findings.
