Wang Lebo, Li Kaiming, Hu Xiaoping P
Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, USA.
Department of Bioengineering, University of California, Riverside, Riverside, CA, USA.
Netw Neurosci. 2021 Feb 1;5(1):83-95. doi: 10.1162/netn_a_00171. eCollection 2021.
There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.
深度学习已成功应用于功能磁共振成像（fMRI），其中fMRI数据大多被视为结构化网格，并且来自欧几里得邻域的空间特征通常由计算机视觉领域的卷积神经网络（CNN）提取。最近，CNN已扩展到图数据并展现出卓越性能。在此，我们基于功能连接定义图，并提出一种用于fMRI分析的基于连接性的图卷积网络（cGCN）架构。这种方法使我们能够从连接组邻域而非欧几里得邻域提取空间特征，这与大脑的功能组织一致。为了评估cGCN的性能，我们将其应用于两个静息态fMRI数据场景。一个是健康参与者的个体识别，另一个是自闭症患者与正常对照的分类。我们的结果表明，cGCN能够在fMRI分析中有效捕获功能连接特征以用于相关应用。
