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Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy

Dominik Krzemiński Orcid Logo, Naoki Masuda Orcid Logo, Khalid Hamandi Orcid Logo, Krish D. Singh Orcid Logo, Bethany Routley, Jiaxiang Zhang Orcid Logo

Network Neuroscience, Volume: 4, Issue: 2, Pages: 374 - 396

Swansea University Author: Jiaxiang Zhang Orcid Logo

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DOI (Published version): 10.1162/netn_a_00125

Abstract

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME...

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Published in: Network Neuroscience
ISSN: 2472-1751
Published: MIT Press - Journals 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa61206
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Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. 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spelling 2022-10-03T15:01:26.2797856 v2 61206 2022-09-13 Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy 555e06e0ed9a87608f2d035b3bde3a87 0000-0002-4758-0394 Jiaxiang Zhang Jiaxiang Zhang true false 2022-09-13 SCS Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices. Journal Article Network Neuroscience 4 2 374 396 MIT Press - Journals 2472-1751 Maximum entropy model, MEG, Energy landscape, Resting-state networks, Juvenile myoclonic epilepsy 1 4 2020 2020-04-01 10.1162/netn_a_00125 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Krish D. Singh, Medical Research Council (http://dx.doi.org/10.13039/501100000265), Award ID: MR/K005464/1. Dominik Krzeminski, Engineering and Physical Sciences Research Coun- ´ cil (http://dx.doi.org/10.13039/501100000266), Award ID: EP/N509449/1. Jiaxiang Zhang, European Research Council, Award ID: 716321. Bethany Routley, Medical Research Council (http://dx.doi.org/10.13039/501100000265), Award ID: MR/K501086/1. Khalid Hamandi, Health Care Research Wales. 2022-10-03T15:01:26.2797856 2022-09-13T13:53:17.4795041 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Dominik Krzemiński 0000-0003-4568-0583 1 Naoki Masuda 0000-0003-1567-801x 2 Khalid Hamandi 0000-0001-7116-262x 3 Krish D. Singh 0000-0001-9981-0465 4 Bethany Routley 5 Jiaxiang Zhang 0000-0002-4758-0394 6 61206__25288__9a5e5a9f8f6b439bb83e86b8c2166b99.pdf 61206_VoR.pdf 2022-10-03T14:59:55.7651920 Output 2200160 application/pdf Version of Record true © 2020 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license true eng http://creativecommons.org/licenses/by/4.0/
title Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
spellingShingle Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
Jiaxiang Zhang
title_short Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
title_full Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
title_fullStr Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
title_full_unstemmed Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
title_sort Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy
author_id_str_mv 555e06e0ed9a87608f2d035b3bde3a87
author_id_fullname_str_mv 555e06e0ed9a87608f2d035b3bde3a87_***_Jiaxiang Zhang
author Jiaxiang Zhang
author2 Dominik Krzemiński
Naoki Masuda
Khalid Hamandi
Krish D. Singh
Bethany Routley
Jiaxiang Zhang
format Journal article
container_title Network Neuroscience
container_volume 4
container_issue 2
container_start_page 374
publishDate 2020
institution Swansea University
issn 2472-1751
doi_str_mv 10.1162/netn_a_00125
publisher MIT Press - Journals
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
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description Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.
published_date 2020-04-01T04:19:52Z
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score 11.037603

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