A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment

Dong, Wen; Qin, Xue; Lu, Chengbiao; Guan, Xinyong; Wang, Yuping; Li, Xiaoli

doi:10.1016/j.neunet.2014.03.001

Cited by 14 publications

(11 citation statements)

References 38 publications

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“…The pattern of global functional connectivity changes in the current study shows similarities as well as differences with previous EEG and MEG studies. A lower level of global synchronization in MCI was presented in alpha and beta bands, which has been reported by early studies (Pijnenburg et al, 2004 ; Koenig et al, 2005 ; López et al, 2014 ; Wen et al, 2014 ). However, many other studies with MCI patient also demonstrated different abnormalities of functional connectivity.…”

Section: Discussionsupporting

confidence: 71%

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Zeng

Wang

Ouyang

et al. 2015

Front. Comput. Neurosci.

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Purpose: Diabetes is a great risk factor for dementia and mild cognitive impairment (MCI). This study investigates whether complex network-derived features in resting state EEG (rsEEG) could be applied as a biomarker to distinguish amnestic mild cognitive impairment (aMCI) from normal cognitive function in subjects with type 2 diabetes (T2D).Method: In this study, EEG was recorded in 28 patients with T2D (16 aMCI patients and 12 controls) during a no-task eyes-closed resting state. Pair-wise synchronization of rsEEG signals were assessed in six frequency bands (delta, theta, lower alpha, upper alpha, beta, and gamma) using phase lag index (PLI) and grouped into long distance (intra- and inter-hemispheric) and short distance interactions. PLI-weighted connectivity networks were also constructed, and characterized by mean clustering coefficient and path length. The correlation of these features and Montreal Cognitive Assessment (MoCA) scores was assessed.Results: Main findings of this study were as follows: (1) In comparison with controls, patients with aMCI had a significant decrease of global mean PLI in lower alpha, upper alpha, and beta bands. Lower functional connection at short and long intra-hemispheric distance mainly appeared on the left hemisphere. (2) In the lower alpha band, clustering coefficient was significantly lower in aMCI group, and the path length significantly increased. (3) Cognitive status measured by MoCA had a significant positive correlation with cluster coefficient and negative correlation with path length in lower alpha band.Conclusions: The brain network of aMCI patients displayed a disconnection syndrome and a loss of small-world architecture. The correlation between cognitive states and network characteristics suggested that the more in deterioration of the diabetes patients' cognitive state, the less optimal the network organization become. Hence, the complex network-derived biomarkers based on EEG could be employed to track cognitive function of diabetic patients and provide a new diagnosis tool for aMCI.

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Section: Discussionsupporting

confidence: 71%

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Zeng

Wang

Ouyang

et al. 2015

Front. Comput. Neurosci.

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“…Many methods were used to estimate the synchronization strength of two time series and multiple time series, including of phase synchronization (Tóth et al, 2014 ), S estimator (Dauwels et al, 2010b ), global synchronization (Koenig et al, 2005 ), stochastic events synchronization (Dauwels et al, 2010b ), global synchronization index (GSI) (Cui et al, 2010 ; Lee et al, 2010 ), and global coupling index (GCI) (Wen et al, 2014b ). And these methods were often applied to the studies analyzing the EEG signals of MCI and AD.…”

Section: The Research Situation Of Eeg Signal Analysis Methods Used Fmentioning

confidence: 99%

“…Recently, Wen et al ( 2014b ) improved the GSI method, and proposed a new method named GCI. The results showed that the synchronization strength based on GCI was less affected by the change of frequency bands relative to the other two methods, there existed more excellent performance on GCI method according to the change of coupling coefficient versus GSI and S estimator, and GCI was more sensitive than GSI and S estimator on distinguishing the synchronization strength of EEG signals from MCI and NC, especially in the Alpha frequency band.…”

Section: The Research Situation Of Eeg Signal Analysis Methods Used Fmentioning

confidence: 99%

A Critical Review: Coupling and Synchronization Analysis Methods of EEG Signal with Mild Cognitive Impairment

Dong

Zhou²,

Li³

2015

Front. Aging Neurosci.

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At present, the clinical diagnosis of mild cognitive impairment (MCI) patients becomes the important approach of evaluating early Alzheimer’s disease. The methods of EEG signal coupling and synchronization act as a key role in evaluating and diagnosing MCI patients. Recently, these coupling and synchronization methods were used to analyze the EEG signals of MCI patients according to different angles, and many important discoveries have been achieved. However, considering that every method is single-faceted in solving problems, these methods have various deficiencies when analyzing EEG signals of MCI patients. This paper reviewed in detail the coupling and synchronization analysis methods, analyzed their advantages and disadvantages, and proposed a few research questions needed to solve in the future. Also, the principles and best performances of these methods were described. It is expected that the performance analysis of these methods can provide the theoretical basis for the method selection of analyzing EEG signals of MCI patients and the future research directions.

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“…The resting-state EEG (rsEEG) underlies brain network activity (Steriade, 2006) and can be used for neurological evolution (Rossini et al , 2007, Schmidt et al , 2013. Recent studies have shown that rsEEG rhythms maybe a promising approach to diagnose MCI subjects (Dauwels et al , 2010b, Knyazeva et al , 2013, Babiloni et al , 2014, Dong Wen, 2014. There were also several studies about the cognitive function of T2DM using EEG signals (Gerald Cooray, 2008, Cooray et al , 2011, Baskaran et al , 2012, Bian et al , 2014.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the nature of complex characteristics in EEG signals, many methods were used to analyze the EEG signal from different perspectives (Gerald Cooray, 2008, Dauwels et al , 2010a, Cooray et al , 2011, Baskaran et al , 2012, Knyazeva et al , 2013, Babiloni et al , 2014, Dong Wen, 2014, especially the relationship between EEG signals from different brain regions. These methods include coherence (Brassen et al , 2004, Hidasi et al , 2007, Güntekin et al , 2008, Jelles et al , 2008, Moretti et al , 2008, Dauwels et al , 2010b, Bian et al , 2014, mutual information (Dauwels et al , 2010b) and likelihood synchronization (Babiloni et al , 2006) as well as the coupling analysis (Rosenblum et al , 2001, Rosenblum et al , 2002.…”

Section: Introductionmentioning

confidence: 99%

Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information

Dong

Bian

et al. 2016

Clinical Neurophysiology

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A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment

Cited by 14 publications

References 38 publications

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

A Critical Review: Coupling and Synchronization Analysis Methods of EEG Signal with Mild Cognitive Impairment

Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information

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