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Interpretable brain network models for disease prediction are of great valuefor the advancement of neuroscience. GNNs are promising to model complicatednetwork data, but they are prone to overfitting and suffer from poorinterpretability, which prevents their usage in decision-critical scenarioslike healthcare. To bridge this gap, we propose BrainNNExplainer, aninterpretable GNN framework for brain network analysis. It is mainly composedof two jointly learned modules: a backbone prediction model that isspecifically designed for brain networks and an explanation generator thathighlights disease-specific prominent brain network connections. Extensiveexperimental results with visualizations on two challenging disease predictiondatasets demonstrate the unique interpretability and outstanding performance ofBrainNNExplainer.

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis