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MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Luke Tait, Jiaxiang Zhang Orcid Logo

NeuroImage, Volume: 251, Start page: 119006

Swansea University Author: Jiaxiang Zhang Orcid Logo

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DOI (Published version): 10.1016/j.neuroimage.2022.119006

Abstract

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the mic...

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Published in: NeuroImage
ISSN: 1053-8119
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61345
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first_indexed 2022-10-11T11:08:21Z
last_indexed 2023-01-13T19:22:04Z
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spelling 2022-10-11T12:12:40.7845260 v2 61345 2022-09-26 MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses 555e06e0ed9a87608f2d035b3bde3a87 0000-0002-4758-0394 Jiaxiang Zhang Jiaxiang Zhang true false 2022-09-26 SCS EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale. Journal Article NeuroImage 251 119006 Elsevier BV 1053-8119 1 5 2022 2022-05-01 10.1016/j.neuroimage.2022.119006 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This study was supported by European Research Council [grant number 716321]. 2022-10-11T12:12:40.7845260 2022-09-26T11:42:54.4744043 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Luke Tait 1 Jiaxiang Zhang 0000-0002-4758-0394 2 61345__25406__83579e75ee894147a13c4e8f9c55b6f3.pdf 61345_VoR.pdf 2022-10-11T12:09:32.2345868 Output 3161287 application/pdf Version of Record true © 2022 . This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
spellingShingle MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
Jiaxiang Zhang
title_short MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
title_full MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
title_fullStr MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
title_full_unstemmed MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
title_sort MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses
author_id_str_mv 555e06e0ed9a87608f2d035b3bde3a87
author_id_fullname_str_mv 555e06e0ed9a87608f2d035b3bde3a87_***_Jiaxiang Zhang
author Jiaxiang Zhang
author2 Luke Tait
Jiaxiang Zhang
format Journal article
container_title NeuroImage
container_volume 251
container_start_page 119006
publishDate 2022
institution Swansea University
issn 1053-8119
doi_str_mv 10.1016/j.neuroimage.2022.119006
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.
published_date 2022-05-01T04:20:08Z
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