IEEE Trans Neural Syst Rehabil Eng. 2019 Apr;27(4):643-651. doi: 10.1109/TNSRE.2019.2901888. Epub 2019 Feb 26.
Dynamic functional connectivity is attracting a growing interest as it has been suggested to be a more accurate representation of functional brain networks compared to traditional functional connectivity. It is believed that the functional connectivity fluctuations result from the transitions among different brain states other than continuous changes in the brain. In this paper, we aim to investigate the spatial-temporal changes in the interactions between different brain regions during a self-paced hand movement with EEG signals. A systematic analysis framework, consisting of connectivity metric calculation, brain state segmentation, temporal representative graph extraction, and spatial community detection, is proposed to analyze the dynamic functional connectivity. First, corrected imaginary coherency is applied to measure the functional connectivity as it is insensitive to EEG volume conduction problem. Second, singular value decomposition (SVD) vector space distance between the connectivity matrices at two adjacent time points is calculated. In addition, the brain states are segmented based on the changes in the time series of SVD vector space distances. Third, one representative graph is summarized within each state segment using the SVD vectors corresponding to the k largest singular values. Finally, spatial patterns on the representative graph are detected with a modularity-based community detection method. Based on the SVD vector space distance using the change point detection method, a series of brain states lasting for hundreds of milliseconds are identified. Moreover, we find that the sudden decrease points in SVD vector space distance coincide with early Bereitschafts potential. In addition, we find that there are several connectivity patterns along the time before the onset of movement. At first, the functional connectivity is relatively dispersed. Gradually, the functional connectivity begins to concentrate and the predominant communities in each dynamic functional network can be observed clearly.
动态功能连接正受到越来越多的关注，因为与传统功能连接相比，它被认为更能准确地表示功能大脑网络。人们相信，功能连接的波动是由于大脑状态的转变而不是大脑的连续变化而产生的。在本文中，我们旨在使用 EEG 信号研究自我节奏手运动过程中不同脑区之间相互作用的时空变化。提出了一种系统分析框架，包括连接度量计算、脑状态分割、时间代表性图提取和空间社区检测，以分析动态功能连接。首先，应用校正虚相干来测量功能连接，因为它对 EEG 容积传导问题不敏感。其次，计算连接矩阵在两个相邻时间点的奇异值分解（SVD）向量空间距离。此外，根据 SVD 向量空间距离的变化对脑状态进行分割。第三，使用对应于 k 个最大奇异值的 SVD 向量对每个状态段中的一个代表性图进行总结。最后，使用基于模块性的社区检测方法检测代表性图上的空间模式。基于 SVD 向量空间距离的变化点检测方法，确定了一系列持续数百毫秒的脑状态。此外，我们发现 SVD 向量空间距离的突然下降点与早期预备电位吻合。此外，我们发现运动开始前的时间线上存在几种连接模式。首先，功能连接相对分散。逐渐地，功能连接开始集中，每个动态功能网络中的主要社区可以清晰地观察到。
