基于ICA和图论方法的脑电β波静息态功能连接

Functional connectivity of EEG beta rhythm in resting state based on ICA and graph theory

为了探究正常人脑电β波（13-25 Hz）静息态功能连接,提出了一种结合独立成分分析（ICA）、图论、层次聚类、t检验、标准低分辨率电磁断层成像（s LORETA）技术的分析算法。对利用BP Analyzer 64导脑电仪采集的25个健康被试者在闭眼和睁眼静息状态下的高分辨率脑电信号β波（13-25 Hz）进行了功能连接研究,结果表明：（a）β波在闭眼状态下的功能连接明显多于睁眼状态;（b）从闭眼状态到睁眼状态,在右侧大脑顶叶、枕叶、颞叶区域β波功能连接明显减弱,而在双侧额叶连接增强;（c）静息态网络中的默认节点网络、视觉网络、运动感觉网络在闭眼状态下显著。因此,证明该算法适用于研究脑电β波静息态功能连接。

In order to explore normal EEG beta（ 13 - 25 Hz） rhythm functional connectivity in resting state,this paper proposed an analysis algorithm which combined independent component analysis（ ICA）,graph theory,hierarchical cluster analysis,t-test and standardized low-resolution tomography analysis（ s LORETA）. It used brain vision analyzer 64 channels to record high resolution electroencephalography（ EEG） signals of 25 healthy participants under botheyes-closed and eyes-open resting states. Then it used this analysis algorithmto studythe functional connectivity of beta rhythm（ 13 - 25Hz）. The analysis results demonstrate：（ a） functional connectivity ineyes-closed state is more obvious than in eyes-open state;（ b） during the course from eyes-closed to eyes-open state,functional connectivity decreases in parietal,occipital and temporal regions of right hemisphere dominantly and increases in bilateral frontal regions;（ c） default mode network,visual network and sensory-motor are significant in eyes-closed state. So this analysis algorithmis suitable for studying EEG beta rhythm functional connectivity in resting state.