机构：[a]School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China[b]The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China[c]Xuanwu Hospital, Capital Medical University, Beijing 100053, China首都医科大学宣武医院[d]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China[e]College of Liren, Yanshan University, Qinhuangdao 066004, China[f]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China[g]Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing 100053, China.首都医科大学宣武医院
Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (CSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, betal and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings. (C) 2014 Published by Elsevier Ltd.
基金：
the National Natural Science Foundation of China (Grant No. 61025019, 61273063, 61105027,31070938, 81271422)
the open project of State Key Laboratory of Cognitive Neuroscience and Learning in Beijing Normal University(2012–2013)
第一作者机构：[a]School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China[b]The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China
通讯作者：
通讯机构：[c]Xuanwu Hospital, Capital Medical University, Beijing 100053, China[*1]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, XinJieKouWai St., HaiDian District, Beijing 100875, China.
推荐引用方式(GB/T 7714)：
Dong Wen,Qing Xue,Chengbiao Lu,et al.A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment[J].NEURAL NETWORKS.2014,56:44935.doi:10.1016/j.neunet.2014.03.001.
APA：
Dong Wen,Qing Xue,Chengbiao Lu,Xinyong Guan,Yuping Wang&Xiaoli Li.(2014).A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment.NEURAL NETWORKS,56,
MLA：
Dong Wen,et al."A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment".NEURAL NETWORKS 56.(2014):44935
计算机科学