Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study

作     者：Bi, Hui Cao, Shumei Yan, Hanying Jiang, Zhongyi Zhang, Jun Zou, Ling 

作者机构：Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Fudan Univ Huashan Hosp Dept Anesthesiol Shanghai 200040 Peoples R China Fudan Univ Shanghai Canc Ctr Dept Anesthesiol Shanghai 200032 Peoples R China Key Lab Brain Machine Collaborat Intelligence Fdn Hangzhou 310018 Zhejiang Peoples R China 

出 版 物：《IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS》 (IEEE/ACM Trans. Comput. BioL. Bioinf.)

年 卷 期：2022年第19卷第1期

页      面：3-13页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：project of Jiangsu Provincial Science and Technology Department [BE2018638] Jiangsu Province 333 High-level Talent Cultivation Project Changzhou Science and Technology Project [CE20195025] Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Provinice [2020E1001004] Human-Machine Intelligence and Interaction International Joint Laboratory Project Changzhou University Funding of Science Research [ZMF18020322] Jiangsu Educational Project [19KJB520002] Fund of Shanghai Science and Technology Commission [20Y11906200] 

主　　题：Anesthesia electroencephalogram (EEG) functional connectivity sparse representation 

摘      要：The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures, however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.