论文标题

汇总正规图神经网络用于fMRI生物标志物分析

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

论文作者

Li, Xiaoxiao, Zhou, Yuan, Dvornek, Nicha C., Zhang, Muhan, Zhuang, Juntang, Ventola, Pamela, Duncan, James S

论文摘要

了解某些大脑区域与特定神经系统疾病的关系是神经影像研究的重要领域。识别显着区域的一种有希望的方法是使用图神经网络(GNN),可用于分析图形结构化数据,例如通过功能磁共振成像(fMRI)构建的大脑网络。我们提出了一个可解释的GNN框架,该框架具有一种新型的显着区域选择机制,以确定与疾病相关的神经脑生物标志物。具体而言,我们设计了新型的正规合并层,这些层次突出了显着的兴趣区域(ROI),因此我们可以根据基于池层计算出的节点池评分来识别某种疾病对某些疾病很重要。我们提出的框架汇总了正规化gnn(PR-GNN),鼓励合理的ROI选择,并提供了保持个人或组级别模式的灵活性。我们将PR-GNN框架应用于Biopoint自闭症谱系障碍(ASD)fMRI数据集上。我们研究了超参数的不同选择,并表明PR-GNN在分类准确性方面优于基线方法。显着的ROI检测结果表明,与先前的ASD神经影像学生物标志物相对应。

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.

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