机构：[a]Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China[b]Capital University of Medical Sciences Affiliated Tiantan Hospital, Beijing, 100050, China首都医科大学附属天坛医院[c]Elementary Educational College, Jiangxi Normal University, Nanchang, 330022, China[d]Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, 100093, China
Despite the routine use of general anesthesia during surgery, no consensus has been reached on the precise mechanisms by which anesthetic agents suppress consciousness. Recent functional magnetic resonance imaging studies have shown that changes in connectivity is generally observed during propofol-induced loss of consciousness, especially in the fronto-parietal association cortex. Here, we developed a novel feature selection approach based on linear support vector machine with a forward-back search strategy to investigate the mostly discriminative connectivity patterns of different consciousness states. The classification accuracy between wakefulness and deep sedation was up to 96.4%. Weight analysis further revealed that consciousness could be linked to functional connectivity within and across the default mode network, executive control network, salience network and cerebellum. Moreover, the angular, supplementary motor cortex, inferior parietal, insula, and cerebellum exhibited significantly larger weight, suggesting important roles in consciousness. In all, our study sheds light on the mechanism of consciousness.
语种：
外文
第一作者：
推荐引用方式(GB/T 7714)：
Li H,Liu X,Jie N,et al.Identifying intrinsic connectivity network patterns during propofol-induced loss of consciousness: A multivariate analysis[J].2015,2015(CP680):
APA：
Li, H,Liu, X,Jie, N,Zhu, M,Wang, B&Jiang, T.(2015).Identifying intrinsic connectivity network patterns during propofol-induced loss of consciousness: A multivariate analysis.,2015,(CP680)
MLA：
Li, H,et al."Identifying intrinsic connectivity network patterns during propofol-induced loss of consciousness: A multivariate analysis". 2015..CP680(2015)
