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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-7920393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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