《计算机应用研究》|Application Research of Computers

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

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作者 闫彤,杨剑,陈书燊,梁佩鹏
机构 1.北京工业大学 电子信息与控制工程学院,北京 100124;2.磁共振成像脑信息学北京市重点实验室,北京 100053;3.首都医科大学 宣武医院 放射科,北京 100053
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文章编号 1001-3695(2015)04-1028-04
DOI 10.3969/j.issn.1001-3695.2015.04.016
摘要 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(sLORETA)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。
关键词 脑电图;β波;独立成分分析;功能连接
基金项目 国家重点基础研究发展计划资助项目(2014CB744603,2014CB744605)
国家自然科学基金资助项目(61105118,61272345)
北京市自然科学基金资助项目(4132023)
国家国际科技合作专项资助项目(2013DFA32180)
北京市科技新星计划资助项目(Z12111000250000,Z131107000413120)
本文URL http://www.arocmag.com/article/01-2015-04-016.html
英文标题 Functional connectivity of EEG beta rhythm in resting state based on ICA and graph theory
作者英文名 YAN Tong, YANG Jian, CHEN Shu-shen, LIANG Pei-peng
机构英文名 1. College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China; 2. Beijing Key Laboratory of Magnetic Resonance Imaging & Brain Informatics, Beijing 100053, China; 3. Dept. of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
英文摘要 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 (sLORETA). 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.
英文关键词 EEG; beta rhythm; independent component analysis(ICA); functional connectivity
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收稿日期 2014/3/30
修回日期 2014/5/19
页码 1028-1031
中图分类号 TP391.4
文献标志码 A