PMID- 30562649
OWN - NLM
STAT- MEDLINE
DCOM- 20190319
LR  - 20190319
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 110
DP  - 2019 Feb
TI  - Estimating coupling strength between multivariate neural series with multivariate 
      permutation conditional mutual information.
PG  - 159-169
LID - S0893-6080(18)30328-9 [pii]
LID - 10.1016/j.neunet.2018.11.006 [doi]
AB  - Recently, coupling between groups of neurons or different brain regions has been 
      widely studied to provide insights into underlying mechanisms of brain functions. 
      To comprehensively understand the effect of such coupling, it is necessary to 
      accurately extract the coupling strength information among multivariate neural 
      signals from the whole brain. This study proposed a new method named multivariate 
      permutation conditional mutual information (MPCMI) to quantitatively estimate the 
      coupling strength of multivariate neural signals (MNS). The performance of the 
      MPCMI method was validated on the simulated MNS generated by multi-channel neural 
      mass model (MNMM). The coupling strength feature of simulated MNS extracted by 
      MPCMI showed better performance compared with standard methods, such as 
      permutation conditional mutual information (PCMI), multivariate Granger causality 
      (MVGC), and Granger causality analysis (GCA). Furthermore, the MPCMI was applied 
      to estimate the coupling strengths of two-channel resting-state 
      electroencephalographic (rsEEG) signals from different brain regions of 19 
      patients with amnestic mild cognitive impairment (aMCI) with type 2 diabetes 
      mellitus (T2DM) and 20 normal control (NC) with T2DM in Alpha1 and Alpha2 
      frequency bands. Empirical results showed that the MPCMI could effectively 
      extract the coupling strength features that were significantly different between 
      the aMCI and the NC. Hence, the proposed MPCMI method could be an effective 
      estimate of coupling strengths of MNS, and might be a viable biomarker for 
      clinical applications.
CI  - Copyright (c) 2018 Elsevier Ltd. All rights reserved.
FAU - Wen, Dong
AU  - Wen D
AD  - 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; The 
      Key Laboratory for Software Engineering of Hebei Province, Yanshan University, 
      Qinhuangdao 066004, China. Electronic address: xjwd@ysu.edu.cn.
FAU - Jia, Peilei
AU  - Jia P
AD  - 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; The 
      Key Laboratory for Software Engineering of Hebei Province, Yanshan University, 
      Qinhuangdao 066004, China.
FAU - Hsu, Sheng-Hsiou
AU  - Hsu SH
AD  - Swartz Center for Computational Neuroscience, University of California San Diego, 
      La Jolla, CA, 92093, United States.
FAU - Zhou, Yanhong
AU  - Zhou Y
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao 
      066004, China; School of Mathematics and Information Science and Technology, 
      Hebei Normal University of Science and Technology, Qinhuangdao 066004, China. 
      Electronic address: yhzhou168@163.com.
FAU - Lan, Xifa
AU  - Lan X
AD  - Department of Neurology, First Hospital of Qinhuangdao, Qinhuangdao 066000, 
      China.
FAU - Cui, Dong
AU  - Cui D
AD  - 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.
FAU - Li, Guolin
AU  - Li G
AD  - School of Mathematics and Information Science and Technology, Hebei Normal 
      University of Science and Technology, Qinhuangdao 066004, China.
FAU - Yin, Shimin
AU  - Yin S
AD  - Department of Neurology, The Rocket Force General Hospital of Chinese People's 
      Liberation Army, Beijing 100088, China.
FAU - Wang, Lei
AU  - Wang L
AD  - Department of Neurology, The Rocket Force General Hospital of Chinese People's 
      Liberation Army, Beijing 100088, China.
LA  - eng
PT  - Journal Article
DEP - 20181203
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - Brain/physiology/*physiopathology
MH  - Cognitive Dysfunction/diagnosis/physiopathology
MH  - Diabetes Mellitus, Type 2/diagnosis/physiopathology
MH  - Electroencephalography/*methods/statistics & numerical data
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Multivariate Analysis
MH  - *Neurons/physiology
OTO - NOTNLM
OT  - Amnestic mild cognitive impairment
OT  - Coupling strength
OT  - Multi-channel neural mass model
OT  - Multivariate neural series
OT  - Multivariate permutation conditional mutual information
OT  - Resting state EEG signals
EDAT- 2018/12/19 06:00
MHDA- 2019/03/20 06:00
CRDT- 2018/12/19 06:00
PHST- 2018/04/21 00:00 [received]
PHST- 2018/10/05 00:00 [revised]
PHST- 2018/11/20 00:00 [accepted]
PHST- 2018/12/19 06:00 [pubmed]
PHST- 2019/03/20 06:00 [medline]
PHST- 2018/12/19 06:00 [entrez]
AID - S0893-6080(18)30328-9 [pii]
AID - 10.1016/j.neunet.2018.11.006 [doi]
PST - ppublish
SO  - Neural Netw. 2019 Feb;110:159-169. doi: 10.1016/j.neunet.2018.11.006. Epub 2018 
      Dec 3.