| 齐晓英*,陈学莹**,史周晰**,独盟盟**,王娜*.基于脑电微状态特征的正常老年人大脑网络特性研究[J].高技术通讯(中文),2024,34(10):1110~1117 |
| 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 |
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| DOI：10. 3772 / j. issn. 1002-0470. 2024. 10. 010 |
| 中文关键词: 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 |
| 英文关键词: electroencephalograph (EEG) microstate, elderly, brain dynamic characteristic, microstate variability, microstate transfer entrop |
| 基金项目: |
| 作者 | 单位 | | 齐晓英* | (* 延安大学医学院 延安 716000)
(** 陕西科技大学数学与数据科学学院 西安 710021) | | 陈学莹** | | | 史周晰** | | | 独盟盟** | | | 王娜* | |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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 en-
tropy 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 fre-
quency signals. |
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