scholarly journals Altered Resting-State Electroencephalography Microstates in Idiopathic Generalized Epilepsy: A Prospective Case-Control Study

2021 ◽  
Vol 12 ◽  
Author(s):  
YuBao Jiang ◽  
MingYu Zhu ◽  
Ying Hu ◽  
Kai Wang
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Temporal Dynamics ◽  
The Other ◽  
Salience Network ◽  
Idiopathic Generalized Epilepsy ◽  
Class A ◽  
Class C ◽  
Microstate Analysis ◽  
Generalized Epilepsy

Objective: Idiopathic generalized epilepsy (IGE) involves aberrant organization and functioning of large-scale brain networks. This study aims to investigate whether the resting-state EEG microstate analysis could provide novel insights into the abnormal temporal and spatial properties of intrinsic brain activities in patients with IGE.Methods: Three groups of participants were chosen for this study (namely IGE-Seizure, IGE-Seizure Free, and Healthy Controls). EEG microstate analysis on the resting-state EEG datasets was conducted for all participants. The average duration (“Duration”), the average number of microstates per second (“Frequency”), as well as the percentage of total analysis time occupied in that state (“Coverage”) of the EEG microstate were compared among the three groups.Results: For microstate classes B and D, the differences in Duration, Frequency, and Coverage among the three groups were not statistically significant. Both Frequency and Coverage of microstate class A were statistically significantly larger in the IGE-Seizure group than in the other two groups. The Duration and Coverage of microstate class C were statistically significantly smaller in the IGE-Seizure group than those in the other two groups.Conclusions: The Microstate class A was regarded as a sensorimotor network and Microstate class C was mainly related to the salience network, this study indicated an altered sensorimotor and salience network in patients with IGE, especially in those who had experienced seizures in the past 2 years, while the visual and attention networks seemed to be intact.Significance: The temporal dynamics of resting-state networks were studied through EEG microstate analysis in patients with IGE, which is expected to generate indices that could be utilized in clinical researches of epilepsy.

Frequency-dependent amplitude alterations of resting-state spontaneous fluctuations in idiopathic generalized epilepsy

2014 ◽  
Vol 108 (5) ◽  
pp. 853-860 ◽  
Author(s):  
Zhengge Wang ◽  
Zhiqiang Zhang ◽  
Wei Liao ◽  
Qiang Xu ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Resting State ◽  
Idiopathic Generalized Epilepsy ◽  
Frequency Dependent ◽  
Generalized Epilepsy ◽  
Spontaneous Fluctuations

scholarly journals EEG resting-state large-scale brain network dynamics are related to depressive symptoms

2019 ◽  
Author(s):  
Alena Damborská ◽  
Miralena I. Tomescu ◽  
Eliška Honzírková ◽  
Richard Barteček ◽  
Jana Hořínková ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Resting State ◽  
Large Scale ◽  
Rating Scale ◽  
Depressive Symptomatology ◽  
Network Dynamics ◽  
Brain Network ◽  
Temporal Characteristics ◽  
Microstate Analysis ◽  
Depressive Episodes

AbstractBackgroundThe few previous studies on resting-state EEG microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology.MethodsTo evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in patients with moderate to severe depression within bipolar affective disorder, depressive episode, and periodic depressive disorder, and in healthy controls.ResultsMicrostate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher symptomatology on the Montgomery-Åsberg Depression Rating Scale, a questionnaire validated as measuring severity of depressive episodes in patients with mood disorders, correlated with higher occurrence of microstate A (Spearman’s rank correlation, r = 0.70, p < 0.01).ConclusionOur results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. These findings suggest the utility of the microstate analysis approach in an objective depression assessment.

scholarly journals Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy

Brain ◽  
2011 ◽  
Vol 134 (10) ◽  
pp. 2912-2928 ◽  
Author(s):  
Zhiqiang Zhang ◽  
Wei Liao ◽  
Huafu Chen ◽  
Dante Mantini ◽  
Ju-Rong Ding ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Idiopathic Generalized Epilepsy ◽  
Structural Coupling ◽  
Generalized Epilepsy

scholarly journals Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects

2021 ◽  
Vol 15 ◽  
Author(s):  
Ming Ke ◽  
Jianpan Li ◽  
Lubin Wang
Keyword(s):  
Sleep Deprivation ◽  
Resting State ◽  
Brain Activity ◽  
Young Male ◽  
Frequency Of Occurrence ◽  
Total Sleep Deprivation ◽  
Class A ◽  
Class D ◽  
Microstate Analysis ◽  
Male Subjects

Purpose: The cognitive effects of total sleep deprivation (TSD) on the brain remain poorly understood. Electroencephalography (EEG) is a very useful tool for detecting spontaneous brain activity in the resting state. Quasi-stable electrical distributions, known as microstates, carry useful information about the dynamics of large-scale brain networks. In this study, microstate analysis was used to study changes in brain activity after 24 h of total sleep deprivation.Participants and Methods: Twenty-seven healthy volunteers were recruited and underwent EEG scans before and after 24 h of TSD. Microstate analysis was applied, and six microstate classes (A–F) were identified. Topographies and temporal parameters of the microstates were compared between the rested wakefulness (RW) and TSD conditions.Results: Microstate class A (a right-anterior to left-posterior orientation of the mapped field) showed lower global explained variance (GEV), frequency of occurrence, and time coverage in TSD than RW, whereas microstate class D (a fronto-central extreme location of the mapped field) displayed higher GEV, frequency of occurrence, and time coverage in TSD compared to RW. Moreover, subjective sleepiness was significantly negatively correlated with the microstate parameters of class A and positively correlated with the microstate parameters of class D. Transition analysis revealed that class B exhibited a higher probability of transition than did classes D and F in TSD compared to RW.Conclusion: The observation suggests alterations of the dynamic brain-state properties of TSD in healthy young male subjects, which may serve as system-level neural underpinnings for cognitive declines in sleep-deprived subjects.

scholarly journals Dynamics of brain function in chronic pain patients assessed by microstate analysis of resting-state electroencephalography

2020 ◽  
Author(s):  
Elisabeth S. May ◽  
Cristina Gil Ávila ◽  
Son Ta Dinh ◽  
Henrik Heitmann ◽  
Vanessa D. Hohn ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Resting State ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Relevant Information ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Microstate Analysis

AbstractChronic pain is a highly prevalent and severely disabling disease, which is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of brain activity during the resting-state. However, brain activity varies over time and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography (EEG) data of 101 patients suffering from chronic pain and 88 age- and gender-matched healthy controls. Microstate analysis describes EEG activity as a sequence of a limited number of topographies termed microstates, which remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, described resting-state brain activity in both groups and conditions. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in healthy participants. This difference was consistently found in eyes-open and eyes-closed EEG recordings. No evidence for differences in other microstates was found. As microstate D has been previously related to attentional networks and functions, abnormalities of microstate D might relate to dysfunctional attentional processes in chronic pain. These findings add to the understanding of the pathophysiology of chronic pain and might eventually contribute to the development of an EEG-based biomarker of chronic pain.

scholarly journals Relationship Between Large-Scale Functional and Structural Covariance Networks in Idiopathic Generalized Epilepsy

2013 ◽  
Vol 3 (3) ◽  
pp. 240-254 ◽  
Author(s):  
Wei Liao ◽  
Zhiqiang Zhang ◽  
Dante Mantini ◽  
Qiang Xu ◽  
Zhengge Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Idiopathic Generalized Epilepsy ◽  
Structural Covariance ◽  
Generalized Epilepsy
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