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Quantification and Selection of Ictogenic Zones in Epilepsy Surgery

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our...

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Autores principales: Laiou, Petroula, Avramidis, Eleftherios, Lopes, Marinho A., Abela, Eugenio, Müller, Michael, Akman, Ozgur E., Richardson, Mark P., Rummel, Christian, Schindler, Kaspar, Goodfellow, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779811/
https://www.ncbi.nlm.nih.gov/pubmed/31632339
http://dx.doi.org/10.3389/fneur.2019.01045
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author Laiou, Petroula
Avramidis, Eleftherios
Lopes, Marinho A.
Abela, Eugenio
Müller, Michael
Akman, Ozgur E.
Richardson, Mark P.
Rummel, Christian
Schindler, Kaspar
Goodfellow, Marc
author_facet Laiou, Petroula
Avramidis, Eleftherios
Lopes, Marinho A.
Abela, Eugenio
Müller, Michael
Akman, Ozgur E.
Richardson, Mark P.
Rummel, Christian
Schindler, Kaspar
Goodfellow, Marc
author_sort Laiou, Petroula
collection PubMed
description Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected.
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spelling pubmed-67798112019-10-18 Quantification and Selection of Ictogenic Zones in Epilepsy Surgery Laiou, Petroula Avramidis, Eleftherios Lopes, Marinho A. Abela, Eugenio Müller, Michael Akman, Ozgur E. Richardson, Mark P. Rummel, Christian Schindler, Kaspar Goodfellow, Marc Front Neurol Neurology Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected. Frontiers Media S.A. 2019-10-01 /pmc/articles/PMC6779811/ /pubmed/31632339 http://dx.doi.org/10.3389/fneur.2019.01045 Text en Copyright © 2019 Laiou, Avramidis, Lopes, Abela, Müller, Akman, Richardson, Rummel, Schindler and Goodfellow. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Laiou, Petroula
Avramidis, Eleftherios
Lopes, Marinho A.
Abela, Eugenio
Müller, Michael
Akman, Ozgur E.
Richardson, Mark P.
Rummel, Christian
Schindler, Kaspar
Goodfellow, Marc
Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
title Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
title_full Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
title_fullStr Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
title_full_unstemmed Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
title_short Quantification and Selection of Ictogenic Zones in Epilepsy Surgery
title_sort quantification and selection of ictogenic zones in epilepsy surgery
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779811/
https://www.ncbi.nlm.nih.gov/pubmed/31632339
http://dx.doi.org/10.3389/fneur.2019.01045
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