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.

研究使用图神经网络分析脑网络结构数据中与某些神经系统疾病相关的生物标志物。提出了一种可解释的 GNN 框架，其中包括一种新颖的显著区域选择机制和设计新颖的规则化池化层，以突显感兴趣区域（ROI），从而推断哪些 ROI 重要于识别某些疾病。在自闭症神经影像数据集上应用 PR-GNN 框架并展示其在分类准确性方面的优越性及与以往自闭症神经影像现出的生物标志物结果的高度一致性。