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Multilayer brain networks can identify the epileptogenic zone and seizure dynamics

Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80–200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the...

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Autores principales: Shahabi, Hossein, Nair, Dileep R, Leahy, Richard M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065796/
https://www.ncbi.nlm.nih.gov/pubmed/36929752
http://dx.doi.org/10.7554/eLife.68531
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author Shahabi, Hossein
Nair, Dileep R
Leahy, Richard M
author_facet Shahabi, Hossein
Nair, Dileep R
Leahy, Richard M
author_sort Shahabi, Hossein
collection PubMed
description Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80–200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.
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spelling pubmed-100657962023-04-01 Multilayer brain networks can identify the epileptogenic zone and seizure dynamics Shahabi, Hossein Nair, Dileep R Leahy, Richard M eLife Computational and Systems Biology Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80–200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks. eLife Sciences Publications, Ltd 2023-03-17 /pmc/articles/PMC10065796/ /pubmed/36929752 http://dx.doi.org/10.7554/eLife.68531 Text en © 2023, Shahabi et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Shahabi, Hossein
Nair, Dileep R
Leahy, Richard M
Multilayer brain networks can identify the epileptogenic zone and seizure dynamics
title Multilayer brain networks can identify the epileptogenic zone and seizure dynamics
title_full Multilayer brain networks can identify the epileptogenic zone and seizure dynamics
title_fullStr Multilayer brain networks can identify the epileptogenic zone and seizure dynamics
title_full_unstemmed Multilayer brain networks can identify the epileptogenic zone and seizure dynamics
title_short Multilayer brain networks can identify the epileptogenic zone and seizure dynamics
title_sort multilayer brain networks can identify the epileptogenic zone and seizure dynamics
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065796/
https://www.ncbi.nlm.nih.gov/pubmed/36929752
http://dx.doi.org/10.7554/eLife.68531
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