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Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete...

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Autores principales: Sip, Viktor, Hashemi, Meysam, Vattikonda, Anirudh N., Woodman, Marmaduke M., Wang, Huifang, Scholly, Julia, Medina Villalon, Samuel, Guye, Maxime, Bartolomei, Fabrice, Jirsa, Viktor K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7920393/
https://www.ncbi.nlm.nih.gov/pubmed/33596194
http://dx.doi.org/10.1371/journal.pcbi.1008689
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author Sip, Viktor
Hashemi, Meysam
Vattikonda, Anirudh N.
Woodman, Marmaduke M.
Wang, Huifang
Scholly, Julia
Medina Villalon, Samuel
Guye, Maxime
Bartolomei, Fabrice
Jirsa, Viktor K.
author_facet Sip, Viktor
Hashemi, Meysam
Vattikonda, Anirudh N.
Woodman, Marmaduke M.
Wang, Huifang
Scholly, Julia
Medina Villalon, Samuel
Guye, Maxime
Bartolomei, Fabrice
Jirsa, Viktor K.
author_sort Sip, Viktor
collection PubMed
description Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.
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spelling pubmed-79203932021-03-09 Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography Sip, Viktor Hashemi, Meysam Vattikonda, Anirudh N. Woodman, Marmaduke M. Wang, Huifang Scholly, Julia Medina Villalon, Samuel Guye, Maxime Bartolomei, Fabrice Jirsa, Viktor K. PLoS Comput Biol Research Article Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread. Public Library of Science 2021-02-17 /pmc/articles/PMC7920393/ /pubmed/33596194 http://dx.doi.org/10.1371/journal.pcbi.1008689 Text en © 2021 Sip et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sip, Viktor
Hashemi, Meysam
Vattikonda, Anirudh N.
Woodman, Marmaduke M.
Wang, Huifang
Scholly, Julia
Medina Villalon, Samuel
Guye, Maxime
Bartolomei, Fabrice
Jirsa, Viktor K.
Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
title Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
title_full Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
title_fullStr Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
title_full_unstemmed Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
title_short Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
title_sort data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7920393/
https://www.ncbi.nlm.nih.gov/pubmed/33596194
http://dx.doi.org/10.1371/journal.pcbi.1008689
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