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A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease
,
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...
| Published in: | PLOS Computational Biology |
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| ISSN: | 1553-7358 |
| Published: |
Public Library of Science (PLoS)
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa61204 |
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| 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 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. |
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| Keywords: |
Alzheimer's disease, Electroencephalography, Epilepsy, Network analysis, Neural networks, Neuroimaging, Normal distribution, Permutation. |
| College: |
Faculty of Science and Engineering |
| Funders: |
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]. |
| Issue: |
8 |
| Start Page: |
e1009252 |