  Understanding how certain brain regions relate to a specific neurological
disorder has been an important area of neuroimaging research. A promising
approach to identify the salient regions is using Graph Neural Networks (GNNs),
which can be used to analyze graph structured data, e.g. brain networks
constructed by functional magnetic resonance imaging (fMRI). We propose an
interpretable GNN framework with a novel salient region selection mechanism to
determine neurological brain biomarkers associated with disorders.
Specifically, we design novel regularized pooling layers that highlight salient
regions of interests (ROIs) so that we can infer which ROIs are important to
identify a certain disease based on the node pooling scores calculated by the
pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN),
encourages reasonable ROI-selection and provides flexibility to preserve either
individual- or group-level patterns. We apply the PR-GNN framework on a
Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different
choices of the hyperparameters and show that PR-GNN outperforms baseline
methods in terms of classification accuracy. The salient ROI detection results
show high correspondence with the previous neuroimaging-derived biomarkers for
ASD.
