School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China.
J Neural Eng. 2024 Feb 26;21(1). doi: 10.1088/1741-2552/ad27ee.
Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize potential dynamic spatial and temporal fluctuations. Since multi-subject dFNC data from sliding-window analysis are also naturally a higher-order tensor, we propose an innovative sparse and low-rank CPD (SLRCPD) for the three-way dFNC tensor to excavate significant dynamic spatiotemporal aberrant changes in schizophrenia.The proposed SLRCPD approach imposes two constraints. First, the Lregularization on spatial modules is applied to extract sparse but significant dynamic connectivity and avoid overfitting the model. Second, low-rank constraint is added on time-varying weights to enhance the temporal state clustering quality. Shared dynamic spatial modules, group-specific dynamic spatial modules and time-varying weights can be extracted by SLRCPD. The strength of connections within- and between-IC networks and connection contribution are proposed to inspect the spatial modules. K-means clustering and classification are further conducted to explore temporal group difference.82 subject resting-state functional magnetic resonance imaging (fMRI) dataset and opening Center for Biomedical Research Excellence (COBRE) schizophrenia dataset both containing schizophrenia patients (SZs) and healthy controls (HCs) were utilized in our work. Three typical dFNC patterns between different brain functional regions were obtained. Compared to the spatial modules of HCs, the aberrant connections among auditory network, somatomotor, visual, cognitive control and cerebellar networks in 82 subject dataset and COBRE dataset were detected. Four temporal states reveal significant differences between SZs and HCs for these two datasets. Additionally, the accuracy values for SZs and HCs classification based on time-varying weights are larger than 0.96.This study significantly excavates spatio-temporal patterns for schizophrenia disease.
动态功能网络连接（dFNC），基于数据驱动的组独立成分（IC）分析，是研究精神分裂症等某些脑部疾病潜在模式的重要途径。高阶动态功能连接张量的典范多角分解（CPD）可以提供一个创新的时空框架，准确描述潜在的动态空间和时间波动。由于来自滑动窗口分析的多主体 dFNC 数据也是自然的高阶张量，我们提出了一种创新的稀疏和低秩 CPD（SLRCPD）用于三向 dFNC 张量，以挖掘精神分裂症中的显著动态时空异常变化。所提出的 SLRCPD 方法施加了两个约束。首先，在空间模块上施加 L 正则化以提取稀疏但重要的动态连接并避免模型过度拟合。其次，在时变权重上添加低秩约束以增强时间状态聚类质量。可以通过 SLRCPD 提取共享动态空间模块、组特定动态空间模块和时变权重。提出了连接强度在 IC 网络内和之间以及连接贡献，以检查空间模块。进一步进行 K-means 聚类和分类以探索时间组差异。我们的工作使用了包含精神分裂症患者（SZs）和健康对照（HCs）的 82 个主体静息态功能磁共振成像（fMRI）数据集和开放生物医学研究卓越中心（COBRE）精神分裂症数据集。在不同脑功能区域之间获得了三种典型的 dFNC 模式。与 HCs 的空间模块相比，在 82 个主体数据集和 COBRE 数据集中检测到听觉网络、躯体运动、视觉、认知控制和小脑网络之间的异常连接。对于这两个数据集，四个时间状态显示 SZs 和 HCs 之间存在显著差异。此外，基于时变权重的 SZs 和 HCs 分类的准确率大于 0.96。这项研究显著挖掘了精神分裂症疾病的时空模式。
