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Identifying intrinsic connectivity network pattern during propofol-induced loss of consciousness: a multivariate analysis
Li, Huandong1; Liu, Xiaoyuan2; Jie, Nanfeng1; Zhu, Maohu3; Jiang, Tianzi1
2016
会议名称IET International Conference on Biomedical Image and Signal Processing (ICBISP)
会议录名称ICBISP conference proceedings
会议日期2015-11-19
会议地点北京
会议主办者The Institution of Engineering and Technology (IET)
摘要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.
其他摘要
关键词Propofol Functional Mri Svm-foba Feature Selection Consciousness
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收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/10557
专题脑网络组研究中心
通讯作者Li, Huandong
作者单位1.Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
2.Capital University of Medical Sciences Affiliated Tiantan Hospital
3.Elementary Educational College, Jiangxi Normal University
推荐引用方式
GB/T 7714
Li, Huandong,Liu, Xiaoyuan,Jie, Nanfeng,et al. Identifying intrinsic connectivity network pattern during propofol-induced loss of consciousness: a multivariate analysis[C],2016.
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