title: A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment.
abstract: 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 (GSI) 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, beta1 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.
authors.affiliation: School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wen
authors.affiliation: Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Xue
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing100875, China.
authors.forename: Chengbiao
authors.initials: C
authors.lastname: Lu
authors.affiliation: College of Liren, Yanshan University, Qinhuangdao 066004, China.
authors.forename: Xinyong
authors.initials: X
authors.lastname: Guan
authors.affiliation: Xuanwu Hospital, Capital Medical University, Beijing 100053, China. Electronic address: mdwangyp@yahoo.com.cn.
authors.forename: Yuping
authors.initials: Y
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China. Electronic address: xiaoli@bnu.edu.cn.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
mesh_terms: D000368:Aged; D000465:Algorithms; D000513:Alpha Rhythm; D001611:Beta Rhythm; D001921:Brain / Q000503:physiopathology*; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000503:physiopathology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D058407:Electroencephalography Phase Synchronization; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D013269:Stochastic Processes
keywords: Global coupling index; Mild cognitive impairment; Multi-channel neural mass model; Multivariate neural series; Synchronization