Cargando…
Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In th...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599905/ https://www.ncbi.nlm.nih.gov/pubmed/36289925 http://dx.doi.org/10.3390/biomedicines10102662 |
_version_ | 1784816708705320960 |
---|---|
author | Laiou, Petroula Biondi, Andrea Bruno, Elisa Viana, Pedro F. Winston, Joel S. Rashid, Zulqarnain Ranjan, Yatharth Conde, Pauline Stewart, Callum Sun, Shaoxiong Zhang, Yuezhou Folarin, Amos Dobson, Richard J. B. Schulze-Bonhage, Andreas Dümpelmann, Matthias Richardson, Mark P. |
author_facet | Laiou, Petroula Biondi, Andrea Bruno, Elisa Viana, Pedro F. Winston, Joel S. Rashid, Zulqarnain Ranjan, Yatharth Conde, Pauline Stewart, Callum Sun, Shaoxiong Zhang, Yuezhou Folarin, Amos Dobson, Richard J. B. Schulze-Bonhage, Andreas Dümpelmann, Matthias Richardson, Mark P. |
author_sort | Laiou, Petroula |
collection | PubMed |
description | Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems. |
format | Online Article Text |
id | pubmed-9599905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95999052022-10-27 Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence Laiou, Petroula Biondi, Andrea Bruno, Elisa Viana, Pedro F. Winston, Joel S. Rashid, Zulqarnain Ranjan, Yatharth Conde, Pauline Stewart, Callum Sun, Shaoxiong Zhang, Yuezhou Folarin, Amos Dobson, Richard J. B. Schulze-Bonhage, Andreas Dümpelmann, Matthias Richardson, Mark P. Biomedicines Article Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems. MDPI 2022-10-21 /pmc/articles/PMC9599905/ /pubmed/36289925 http://dx.doi.org/10.3390/biomedicines10102662 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Laiou, Petroula Biondi, Andrea Bruno, Elisa Viana, Pedro F. Winston, Joel S. Rashid, Zulqarnain Ranjan, Yatharth Conde, Pauline Stewart, Callum Sun, Shaoxiong Zhang, Yuezhou Folarin, Amos Dobson, Richard J. B. Schulze-Bonhage, Andreas Dümpelmann, Matthias Richardson, Mark P. Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence |
title | Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence |
title_full | Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence |
title_fullStr | Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence |
title_full_unstemmed | Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence |
title_short | Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence |
title_sort | temporal evolution of multiday, epileptic functional networks prior to seizure occurrence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599905/ https://www.ncbi.nlm.nih.gov/pubmed/36289925 http://dx.doi.org/10.3390/biomedicines10102662 |
work_keys_str_mv | AT laioupetroula temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT biondiandrea temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT brunoelisa temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT vianapedrof temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT winstonjoels temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT rashidzulqarnain temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT ranjanyatharth temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT condepauline temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT stewartcallum temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT sunshaoxiong temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT zhangyuezhou temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT folarinamos temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT dobsonrichardjb temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT schulzebonhageandreas temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT dumpelmannmatthias temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT richardsonmarkp temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence AT temporalevolutionofmultidayepilepticfunctionalnetworkspriortoseizureoccurrence |