The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

作者：Zhenhu Liang, Zhilei Lan, Yong Wang, Yang Bai, Jianghong He, Juan Wang, Xiaoli Li · 发表于：Journal of Neural Engineering · 年份：2023 · DOI：10.1088/1741-2552/ad12dc · 被引用次数：11 · 研究领域：Functional Brain Connectivity Studies、EEG and Brain-Computer Interfaces、Optical Imaging and Spectroscopy Techniques

Abstract Objective. 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. Approach. 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. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased ( p < 0.001), PFC increased ( p < 0.001) and PCMI decreased ( p < 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) ( p < 0.001) and nodal efficiency (NE) ( p < 0.001) decreased in these two spaces. Global network indicators normalized average path length ( L a v e / L r ) ( p < 0.01) and modularity ( Q ) ( p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient ( C a v e / C r ) and small-world index ( σ ) did not change significantly. Moreover, the dominance of hub nodes...