A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease

Tait, Luke; Lopes, Marinho A.; Stothart, George; Baker, John; Kazanina, Nina; Zhang, Jiaxiang; Goodfellow, Marc

doi:10.1371/journal.pcbi.1009252

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A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease

Luke Tait

, Marinho A. Lopes, George Stothart, John Baker, Nina Kazanina, Jiaxiang Zhang

, Marc Goodfellow

PLOS Computational Biology, Volume: 17, Issue: 8, Start page: e1009252

Swansea University Author: Jiaxiang Zhang

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Copyright: © 2021 Tait et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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DOI (Published version): 10.1371/journal.pcbi.1009252

Abstract

People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of...

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Published in:	PLOS Computational Biology
ISSN:	1553-7358
Published:	Public Library of Science (PLoS) 2021
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However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. 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spelling	2023-02-09T09:46:11.9136953 v2 61204 2022-09-13 A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease 555e06e0ed9a87608f2d035b3bde3a87 0000-0002-4758-0394 Jiaxiang Zhang Jiaxiang Zhang true false 2022-09-13 SCS People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies. Journal Article PLOS Computational Biology 17 8 e1009252 Public Library of Science (PLoS) 1553-7358 Alzheimer's disease, Electroencephalography, Epilepsy, Network analysis, Neural networks, Neuroimaging, Normal distribution, Permutation. 11 8 2021 2021-08-11 10.1371/journal.pcbi.1009252 http://dx.doi.org/10.1371/journal.pcbi.1009252 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee This work was supported by the European Research Council [Grant Number 716321] (LT/JZ). This work was supported by the EPSRC [Grant Numbers EP/P021417/1 and EP/N014391/1] (MG); a Wellcome Trust Institutional Strategic Support Award (https://wellcome.ac.uk/) [Grant Number WT105618MA] (MG); University Research Fellowship from the University of Bristol (NK); MAL gratefully acknowledges funding from Cardiff University’s Wellcome Trust Institutional Strategic Support Fund (ISSF) [Grant Number 204824/Z/16/Z]. 2023-02-09T09:46:11.9136953 2022-09-13T13:52:24.2324025 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Luke Tait 0000-0002-2351-5328 1 Marinho A. Lopes 2 George Stothart 3 John Baker 4 Nina Kazanina 5 Jiaxiang Zhang 0000-0002-4758-0394 6 Marc Goodfellow 0000-0002-7282-7280 7 61204__26510__720a1af0f89d440789d4a07da4b7c55a.pdf journal.pcbi.1009252.VOR61204.pdf 2023-02-09T09:44:45.8686294 Output 2083792 application/pdf Version of Record true Copyright: © 2021 Tait et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. true eng http://creativecommons.org/licenses/by/4.0/
title	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
spellingShingle	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease Jiaxiang Zhang
title_short	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
title_full	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
title_fullStr	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
title_full_unstemmed	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
title_sort	A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
author_id_str_mv	555e06e0ed9a87608f2d035b3bde3a87
author_id_fullname_str_mv	555e06e0ed9a87608f2d035b3bde3a87_***_Jiaxiang Zhang
author	Jiaxiang Zhang
author2	Luke Tait Marinho A. Lopes George Stothart John Baker Nina Kazanina Jiaxiang Zhang Marc Goodfellow
format	Journal article
container_title	PLOS Computational Biology
container_volume	17
container_issue	8
container_start_page	e1009252
publishDate	2021
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issn	1553-7358
doi_str_mv	10.1371/journal.pcbi.1009252
publisher	Public Library of Science (PLoS)
college_str	Faculty of Science and Engineering
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hierarchy_top_title	Faculty of Science and Engineering
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department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url	http://dx.doi.org/10.1371/journal.pcbi.1009252
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description	People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.
published_date	2021-08-11T04:19:52Z
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A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease

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