Liang Zhenhu, Lan Zhilei, Wang Yong, Bai Yang, He Jianghong, Wang Juan, Li Xiaoli
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China.
Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China.
J Neural Eng. 2023 Dec 14;20(6). doi: 10.1088/1741-2552/ad12dc.
General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (< 0.001), PFC increased (< 0.001) and PCMI decreased (< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (< 0.001) and nodal efficiency (NE) (< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (< 0.01) and modularity () (< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/Lrandwere not significantly different from the preoperative resting state in sensor space.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.
全身麻醉（GA）可诱导可逆性意识丧失。尽管如此，处于最低意识状态（MCS）的患者在全身麻醉期间的脑电图（EEG）特征却很少被观察到。我们记录了9例MCS患者在全身麻醉期间的脑电图数据。我们使用排列莱姆尔 - 齐夫复杂度（PLZC）、排列波动复杂度（PFC）来量化I型和II型复杂度。此外，我们使用排列交叉互信息（PCMI）和基于PCMI的脑网络来研究感觉空间和源空间中的功能连接性和脑网络。与术前静息状态相比，在手术麻醉状态维持期间，感觉空间中的PLZC降低（<0.001）、PFC升高（<0.001）且PCMI降低（<0.001）。源空间中这些指标的结果与感觉空间一致。此外，这两个空间中的节点网络指标节点聚类系数（NCC）（<0.001）和节点效率（NE）（<0.001）降低。全局网络指标归一化平均路径长度（Lave/Lr）（<0.01）和模块化程度（）（<0.05）仅在感觉空间中降低，而归一化平均聚类系数（Cave/Cr）和小世界指数（σ）没有显著变化。此外，这两个空间中额叶区域的枢纽节点优势降低。意识恢复后，两个空间中的PFC降低，而PLZC、PCMI升高。NCC、NE和额叶区域枢纽节点优势仅在感觉空间中增加。这些指标未恢复到术前水平。相比之下，感觉空间中的全局网络指标Lave/Lr与术前静息状态无显著差异。全身麻醉会改变脑电图的复杂度，降低信息整合，并伴随着MCS患者脑网络的重新配置。基于PLZC、PFC、PCMI和基于PCMI的脑网络指标可以有效区分MCS患者在全身麻醉期间的意识状态。
Zotero 插件