Temporal–Spatial Patterns in Dynamic Functional Brain Network for Self-Paced Hand Movement

摘要

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.