EEG microstates are correlated with brain functional networks during slow-wave sleep
Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the “atoms of thought”. Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting...
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Elsevier
2020-07-01
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Series: | NeuroImage |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920302731 |
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doaj-ece491e0b8634ea4889514a9fb9d968d |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jing Xu Yu Pan Shuqin Zhou Guangyuan Zou Jiayi Liu Zihui Su Qihong Zou Jia-Hong Gao |
spellingShingle |
Jing Xu Yu Pan Shuqin Zhou Guangyuan Zou Jiayi Liu Zihui Su Qihong Zou Jia-Hong Gao EEG microstates are correlated with brain functional networks during slow-wave sleep NeuroImage EEG microstates BOLD fMRI Brain functional networks Slow-wave sleep |
author_facet |
Jing Xu Yu Pan Shuqin Zhou Guangyuan Zou Jiayi Liu Zihui Su Qihong Zou Jia-Hong Gao |
author_sort |
Jing Xu |
title |
EEG microstates are correlated with brain functional networks during slow-wave sleep |
title_short |
EEG microstates are correlated with brain functional networks during slow-wave sleep |
title_full |
EEG microstates are correlated with brain functional networks during slow-wave sleep |
title_fullStr |
EEG microstates are correlated with brain functional networks during slow-wave sleep |
title_full_unstemmed |
EEG microstates are correlated with brain functional networks during slow-wave sleep |
title_sort |
eeg microstates are correlated with brain functional networks during slow-wave sleep |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2020-07-01 |
description |
Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the “atoms of thought”. Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks. |
topic |
EEG microstates BOLD fMRI Brain functional networks Slow-wave sleep |
url |
http://www.sciencedirect.com/science/article/pii/S1053811920302731 |
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doaj-ece491e0b8634ea4889514a9fb9d968d2020-11-25T03:28:55ZengElsevierNeuroImage1095-95722020-07-01215116786EEG microstates are correlated with brain functional networks during slow-wave sleepJing Xu0Yu Pan1Shuqin Zhou2Guangyuan Zou3Jiayi Liu4Zihui Su5Qihong Zou6Jia-Hong Gao7Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, ChinaLaboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, ChinaCenter for MRI Research, Peking University, Beijing, China; Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, ChinaCenter for MRI Research, Peking University, Beijing, China; Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, ChinaCenter for MRI Research, Peking University, Beijing, China; Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, ChinaNuffield Department of Clinical Neurosciences, Oxford University, Oxford, United KingdomCenter for MRI Research, Peking University, Beijing, China; Corresponding author. Center for MRI Research, Peking University, Beijing, 100871, China.Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China; Center for MRI Research, Peking University, Beijing, China; Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, Beijing, China; Corresponding author. Center for MRI Research, Peking University, Beijing, 100871, China.Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the “atoms of thought”. Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.http://www.sciencedirect.com/science/article/pii/S1053811920302731EEG microstatesBOLD fMRIBrain functional networksSlow-wave sleep |