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Revealing epilepsy type using a computational analysis of interictal EEG
Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI doe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6629665/ https://www.ncbi.nlm.nih.gov/pubmed/31308412 http://dx.doi.org/10.1038/s41598-019-46633-7 |
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author | Lopes, Marinho A. Perani, Suejen Yaakub, Siti N. Richardson, Mark P. Goodfellow, Marc Terry, John R. |
author_facet | Lopes, Marinho A. Perani, Suejen Yaakub, Siti N. Richardson, Mark P. Goodfellow, Marc Terry, John R. |
author_sort | Lopes, Marinho A. |
collection | PubMed |
description | Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG. |
format | Online Article Text |
id | pubmed-6629665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66296652019-07-23 Revealing epilepsy type using a computational analysis of interictal EEG Lopes, Marinho A. Perani, Suejen Yaakub, Siti N. Richardson, Mark P. Goodfellow, Marc Terry, John R. Sci Rep Article Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG. Nature Publishing Group UK 2019-07-15 /pmc/articles/PMC6629665/ /pubmed/31308412 http://dx.doi.org/10.1038/s41598-019-46633-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lopes, Marinho A. Perani, Suejen Yaakub, Siti N. Richardson, Mark P. Goodfellow, Marc Terry, John R. Revealing epilepsy type using a computational analysis of interictal EEG |
title | Revealing epilepsy type using a computational analysis of interictal EEG |
title_full | Revealing epilepsy type using a computational analysis of interictal EEG |
title_fullStr | Revealing epilepsy type using a computational analysis of interictal EEG |
title_full_unstemmed | Revealing epilepsy type using a computational analysis of interictal EEG |
title_short | Revealing epilepsy type using a computational analysis of interictal EEG |
title_sort | revealing epilepsy type using a computational analysis of interictal eeg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6629665/ https://www.ncbi.nlm.nih.gov/pubmed/31308412 http://dx.doi.org/10.1038/s41598-019-46633-7 |
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