在全身麻醉期间,脑电图复杂性,信息整合和大脑网络在最小意识状态的患者中发生变化
Zhenhu Liang1,2, Zhilei Lan1,2, Yong Wang3
1Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China.
Journal of neural engineering
|December 6, 2023
概括
全身麻醉改变电脑电图 (EEG) 复杂性和脑网络集成在最小意识状态 (MCS) 患者. 这些EEG复杂性和功能连接性的变化可以帮助在麻醉期间区分意识状态.
科学领域:
- 神经科学是一个神经科学.
- 麻醉学 麻醉学
- 计算神经科学是一种神经科学.
背景情况:
- 一般麻醉 (GA) 可逆地诱导意识丧失.
- 在GA期间处于最小意识状态 (MCS) 的患者的脑电图 (EEG) 特性尚不清楚.
- 在麻醉期间调查大脑动态对于理解意识至关重要.
研究的目的:
- 研究在全身麻醉 (GA) 下的最小意识状态 (MCS) 患者的脑电图 (EEG) 复杂性和功能连接性.
- 探索GA如何影响MCS患者的大脑网络组织和信息整合.
- 评估特定EEG指标在GA期间区分意识状态的实用性.
主要方法:
- 在GA期间记录了9名MCS患者的EEG数据.
- 量化EEG复杂性使用合Lempel-Ziv复杂性 (PLZC) 和合波动复杂性 (PFC).
- 在传感器和源空间中使用换交叉相互信息 (PCMI) 评估功能连接和大脑网络.
主要成果:
- 在GA期间,PLZC和PCMI下降,而PFC与手术前状态相比,在传感器和源空间中增加.
- 节点网络指标 (节点聚类系数和节点效率) 降低,中心节点在前线区域的主导地位下降.
- 康复后,一些指标显示部分正常化,但没有完全恢复到手术前的水平,表明GA的持久影响.
结论:
- 全身麻醉显著改变EEG复杂性,减少信息整合,并重新配置MCS患者的大脑网络.
- 使用的指标 (PLZC,PFC,PCMI和基于PCMI的大脑网络分析) 可以有效地区分MCS患者在GA期间的意识状态.
- 研究结果提供了关于意识丧失和在麻醉下恢复基础的神经机制的见解.
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