基于脑电微状态特征的正常老年人大脑网络特性研究  

The characteristic of brain dynamics in normal elderly based on EEG microstate characteristics

作　　者：齐晓英 陈学莹 史周晰 独盟盟 王娜 QI Xiaoying;CHEN Xueying;SHI Zhouxi;DU Mengmeng;WANG Na(School of Medicine,Yan’an University,Yan’an 716000;School of Mathematics and Data Science,Shaanxi University of Science and Technology,Xi’an 710021)

机构地区：[1]延安大学医学院,延安716000 [2]陕西科技大学数学与数据科学学院,西安710021

出　　处：《高技术通讯》2024年第10期1110-1117,共8页Chinese High Technology Letters

基　　金：国家自然科学基金(12102240)资助项目。

摘　　要：采用脑电(EEG)微状态方法,分析了94名受试者静息闭眼61通道脑电数据。基于脑电微状态时间序列、微状态转移概率提出微状态变化率与微状态转移熵计算方法,用于评估大脑功能网络动态信息交流特性及其复杂程度。结果显示,2组均得到A、B、C、D这4种经典微状态,相较于青年人,老年人微状态A、B特征及两者之间的转移概率均增加,而微状态C、D特征和微状态变化率以及微状态转移熵均降低。利用线性回归分析发现,脑电微状态特征与大脑内不同节律波能量相关,预示了正常老年人大脑动态特性发生改变,脑网络动态信息交流减弱,可能与老年人大脑内高频信号增加有关。The electroencephalograph(EEG)microstate method is used to analyze the 94 participants underwent 61-channel recording with eyes-closed.To assess the dynamics and complexity of information interaction among brain function networks,microstate variability and microstate transfer entropy methods are proposed based on microstate time series and transition probabilities,respectively.The results reveal that the classic four microstates A,B,C,D are obtained in both groups.Compared with the young,the features of microstate A,B and transition probabilities between them are increased,while microstate C,D characteristics,microstate variability and microstate transfer entropy are decreased.Additionally,it is found that the EEG microstate results are correlated with EEG band power through linear regression analysis.The study suggests that the characteristics of brain dynamics in the elderly have changed,with a decline in the dynamic feature of information interaction,possibly linked to increased high frequency signals.

关 键 词：脑电(EEG)微状态 老年人 大脑动态特性 微状态变化率 微状态转移熵 

分 类 号：R318[医药卫生—生物医学工程]