论文标题
了解用于RS-FMRI功能连接分析的图形同构网络
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
论文作者
论文摘要
图形神经网络（GNN）依赖于图形操作，其中包括各种图形相关任务的神经网络培训。最近，已经尝试将GNN应用于功能磁共振图像（fMRI）数据。尽管最近进展，但一个普遍的局限性很难以一种可解释的方式来解释分类结果。在这里，我们开发了一个使用图同构网络（GIN）分析fMRI数据的框架，该数据最近被提议作为用于图形分类的强大GNN。本文的重要贡献之一是观察到，杜松子是在图形空间中使用邻接矩阵定义的偏移操作中卷积神经网络（CNN）的双重表示。这种理解使我们能够为GNN利用基于CNN的显着性图技术，我们通过一速编码为拟议的杜松子酒量身定制，以可视化大脑的重要区域。我们使用大型休息状态fMRI（RS-FMRI）数据来验证我们提出的框架，以根据大脑的图形结构对受试者的性别进行分类。该实验与我们的期望一致，因此获得的显着性图与以前与性别差异相关的神经成像证据表现出很高的对应关系。
Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix. This understanding enables us to exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding, to visualize the important regions of the brain. We validate our proposed framework using large-scale resting-state fMRI (rs-fMRI) data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences.
