Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer’s disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

中文翻译：

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通过时空图卷积模型探索大脑有效连接网络


近年来，从功能磁共振成像（fMRI）数据中学习大脑有效连接网络（ECN）引起了广泛关注。随着深度学习在众多领域的成功应用，文献报道了多种基于深度学习的脑ECN学习方法。然而，当前的方法忽略了功能磁共振成像数据的深层时间特征，未能充分利用大脑区域之间的空间拓扑关系。在本文中，我们提出了一种基于时空图卷积模型（STGCM）的学习脑ECN的新方法，名为STGCMEC，其中我们首先采用时间卷积网络来提取fMRI数据的深层时间特征，并利用图卷积网络通过聚合邻近区域的信息来更新每个大脑区域的空间特征，这使得大脑区域的特征更具辨别力。然后，基于大脑区域的这些特征，我们设计了一个联合损失函数来指导 STGCMEC 学习大脑 ECN，其中包括任务预测损失和图正则化损失。模拟数据集和真实阿尔茨海默病神经影像倡议（ADNI）数据集上的实验结果表明，与一些最先进的方法相比，所提出的 STGCMEC 能够更好地学习大脑 ECN。