Liaocheng University, Liaocheng, China.
Shandong Jianzhu University, Jinan, China.
PeerJ. 2023 Mar 20;11:e14835. doi: 10.7717/peerj.14835. eCollection 2023.
Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, a graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of BFNs to train a classifier, GCN requires an additional input-node features. To our best knowledge, however, there is no systematic study to analyze their influence on the performance of GCN-based brain disorder classification. Therefore, in this study, we conduct an empirical study on various node feature measures, including (1) original fMRI signals, (2) one-hot encoding, (3) node statistics, (4) node correlation, and (5) their combination. Experimental results on two benchmark databases show that different node feature inputs to GCN significantly affect the brain disease classification performance, and node correlation usually contributes higher accuracy compared to original signals and manually extracted statistical features.
脑功能网络（BFN）分析已成为识别神经/精神疾病的一种流行技术。由于 BFN 是一个图，因此可以将图卷积网络（GCN）自然地用于 BFN 的分类。与直接使用 BFN 的邻接矩阵来训练分类器的传统方法不同，GCN 需要额外的输入节点特征。然而，据我们所知，目前还没有系统的研究来分析它们对基于 GCN 的脑疾病分类性能的影响。因此，在这项研究中，我们对各种节点特征度量进行了实证研究，包括（1）原始 fMRI 信号，（2）独热编码，（3）节点统计，（4）节点相关性，以及（5）它们的组合。在两个基准数据库上的实验结果表明，不同的节点特征输入到 GCN 会显著影响脑疾病分类性能，与原始信号和手动提取的统计特征相比，节点相关性通常会贡献更高的准确性。
