SD-CNN: A static-dynamic convolutional neural network for functional brain networks
SD-CNN: A static-dynamic convolutional neural network for functional brain networks
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DOI：
10.1016/j.media.2022.102679
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发表时间：
2022-11-21
影响因子：
10.9
通讯作者：
Zhang, Daoqiang
中科院分区：
文献类型：
--
作者：
Huang, Jiashuang;Wang, Mingliang;Zhang, Daoqiang
静态功能连接（sFC）和动态功能连接（dFC）已广泛应用于静息态功能磁共振成像（rs-fMRI）分析。基于整个rs-fMRI扫描计算的sFC可以准确地描述大脑网络的静态拓扑结构。通过将rs-fMRI扫描分成一系列短滑动窗口来估计dFC，用于揭示FC模式的时变变化。目前，如何在深度学习框架下联合使用sFC和dFC来识别脑疾病仍然是一个热点问题。为此，我们提出了一种用于功能性大脑网络的静态-动态卷积神经网络，该网络涉及静态路径和动态路径，以充分利用sFC和dFC。具体地，静态路径，使用高分辨率卷积滤波器（即，在sFC的单个邻接矩阵处执行卷积滤波器（具有大量通道的卷积滤波器）以捕获静态FC图案。在dFC的每个邻接矩阵处使用低分辨率卷积滤波器的动态路径被执行以捕获时变FC模式。在该模型中使用了两种类型的扩散连接，以鼓励静态路径和动态路径之间的信息传递，这可以使学习到的特征更具区分性。此外，一个静态和动态的组合分类器被引入到联合收割机的特征，从两个路径识别脑疾病。在两个真实的数据集上的实验证明了该方法的有效性和优越性。
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time -varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.