In recent years, numerous studies have adopted rs-fMRI to construct dynamic functional connectivity networks (DFCNs) and applied them to the diagnosis of brain diseases, such as epilepsy and schizophrenia. Compared with the static brain networks, the DFCNs have a natural advantage in reflecting the process of brain activity due to the time information contained in it. However, most of the current methods for constructing DFCNs fail to aggregate the brain topology structure and temporal variation of the functional architecture associated with brain regions, and often ignore the inherent multi-dimensional feature representation of DFCNs for classification. In order to address these issues, we propose a novel DFCNs construction and representation method and apply it to brain disease diagnosis. Specifically, we fuse the blood oxygen level dependent (BOLD) signal and interactions between brain regions to distinguish the brain topology within each time domain and across different time domains, by embedding block structure in the adjacency matrix. After that, a sparse tensor decomposition method with sparse local structure preserving regularization is developed to extract DFCNs features from a multi-dimensional perspective. Finally, the kernel discriminant analysis is employed to provide the decision result. We validate the proposed method on epilepsy and schizophrenia identification tasks, respectively. The experimental results show that the proposed method outperforms several state-of-the-art methods in the diagnosis of brain diseases.

中文翻译：

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堆叠拓扑保持动态脑网络表示和分类

近年来，大量研究采用rs-fMRI构建动态功能连接网络（DFCNs），并将其应用于癫痫和精神分裂症等脑部疾病的诊断。与静态脑网络相比，DFCNs由于其中包含的时间信息，在反映大脑活动过程方面具有天然优势。然而，目前大多数构建 DFCN 的方法都未能聚合大脑拓扑结构和与大脑区域相关的功能架构的时间变化，并且往往忽略了 DFCN 固有的多维特征表示进行分类。为了解决这些问题，我们提出了一种新的 DFCNs 构建和表示方法，并将其应用于脑部疾病诊断。具体来说，通过在邻接矩阵中嵌入块结构，我们融合了血氧水平依赖性 (BOLD) 信号和大脑区域之间的相互作用，以区分每个时域内和不同时域内的大脑拓扑结构。之后，开发了一种具有稀疏局部结构保留正则化的稀疏张量分解方法，以从多维角度提取 DFCN 特征。最后，核判别分析被用来提供决策结果。我们分别在癫痫和精神分裂症识别任务上验证了所提出的方法。实验结果表明，所提出的方法在脑部疾病的诊断方面优于几种最先进的方法。