Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

Handayani, Nita; Haryanto, Freddy; Khotimah, Siti Nurul; Arif, Imran; Taruno, Warsito P.

doi:10.2478/pjmpe-2018-0001

Cited by 20 publications

(16 citation statements)

References 44 publications

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“…Evidence for functional connectivity loss in aMCI, with respect to the control group, during resting state prior to a task, were reported in various previous studies (Gómez et al, 2009;Zeng et al, 2015;Handayani et al, 2018). For instance, the use of PLI during resting state in Zeng et al (2015), showed a significant decrease in lower alpha, upper alpha, and beta bands at both short and long intra-hemispheric connections.…”

Section: Reduction In Functional Connectivity In Amnestic Mild Cognitive Impairmentmentioning

confidence: 68%

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Youssef

Xiao

Liu

et al. 2021

Front. Comput. Neurosci.

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The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

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Section: Reduction In Functional Connectivity In Amnestic Mild Cognitive Impairmentmentioning

confidence: 68%

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Youssef

Xiao

Liu

et al. 2021

Front. Comput. Neurosci.

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“…Normally, the coherence in pairs of adjacent electrodes showed higher values. Conversely, the coherence was reduced as a function of the spatial distance between pairs of electrodes (Handayani et al, 2018). Nevertheless, distal coherence for T7-T8 electrode pair showed higher values.…”

Section: Discussionmentioning

confidence: 88%

Wavelet Phase Coherence Estimation of EEG Signals for Neuromarketing Studies

Ural¹,

Kaçar²,

Canan³

2019

Neuroquantology

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This paper presents an Electroencephalography (EEG), Pulse Plethysmography (PPG) and Galvanic Skin Response (GSR) study for neuromarketing studies, which are related to analysis of relationship between electrodes and emotional stress across to commercial advertisement. Neuromarketing is a field of study with respect the application of neuroscience methods to analyze consumer behavior related to markets. Wavelet coherence (WC) and phase difference (PD) were used for investigating how the marketing stimuli induced the degree of information flow occurs between electrodes of EEG. Pulse rate variability (PRV) obtained from finger PPG and computed the powers in high frequency (HF), low frequency (LF). The powers in the LF and HF bands are regulated by Autonomic nervous systems (ANS) and then, Skin conductance level (SCL) computed from GSR by measuring changes in the conductivity of the skin. These electrophysiological measurements computed to evaluate emotional stress. The signals were recorded simultaneously from 30 subjects for two stages: prior to advertising stimuli (control stage) and during the advertising stimuli (experimental stage) using iMotions system in Uskudar University (Istanbul, Turkey). The WC and PD for each electrode pairs were computed for five frequency sub-bands (delta, theta, alpha, beta and gamma) of EEG. While the value of WC was generally higher in experimental stage than control stage especially in the theta, alpha and beta frequency, the value of PD was generally lower especially in gamma band. An increase of interhemispheric coherence in experimental stage occurred in the anterior frontal-temporal-parietal-area. At the same time, the LF/HF ratio and SCL were generally higher in experimental stage. We investigated whether there were statistically significant differences in WC, PD, the LF/HF ratio and SCL between the experimental and control stage. Results were demonstrated significant differences in WC and PD, LF/HF ratio between experimental stage and control stage, but there was not in SCL.

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“…Coherence measurement for the characterization of AD patients were reported in the study [15,[22][23][24][25]. Meanwhile, a phase synchronization study for characterization of AD patients was carried out in the study [26,27]. The results of this study reported that there was a decrease in EEG coherence or synchronization in patients with cognitive impairment related to AD compared to normal/elderly healthy subjects.…”

Section: Related Workmentioning

confidence: 98%

Hemispheric Coherence Analysis of Wide Band EEG Signals for Characterization of Post-Stroke Patients with Dementia

Hadiyoso¹,

Zakaria²,

Ong³

et al. 2021

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Post-stroke dementia (PSD) is a type of vascular dementia (VaD) that might be occurred in post-stroke patients. Memory, language and behavior tests can be used for the analysis of cognitive impairment caused by PSD. Often a supporting clinical examination such as an electroencephalogram (EEG) is used to support the diagnosis or analyze the characteristic changes that occur in the brain. Conventional analysis or visual inspection of EEG signals can be very difficult, since the nature of the signal tends to be non-stationer. Therefore, this study proposes a quantitative analysis for the characterization of EEG signals in stroke survivors with dementia. It is thought that it has different characteristics with the normal subject so that this study can be used as a reference in supporting dementia detection in post-stroke survivors. The quantitative analysis used in this study is coherence analysis. Coherence analysis was performed on EEG signals recorded from six poststroke patients with dementia and then compared with ten normal healthy subjects. Analysis of coherence between brain areas includes inter and intra-hemispheric coherence. Validation was carried out by using the independent t-test where the confidence level was 95%, indicating that the p-value <0.05 had a significant difference. The test results show that in general the coherence of the electrode pairs in patients with dementia is lower than in the normal healthy group. It is notably, i) In interhemispheric, the C3-C4, T3-T4, and T5-T6 pairs generate significant differences, ii) the highest decrease in intrahemispheric coherence was found in C3-T5 with p = 0.0005. The coherence study presented in this paper is expected to be used for early detection of PSD in the future.

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Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

Cited by 20 publications

References 44 publications

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Wavelet Phase Coherence Estimation of EEG Signals for Neuromarketing Studies

Hemispheric Coherence Analysis of Wide Band EEG Signals for Characterization of Post-Stroke Patients with Dementia

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