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Analysis of Epileptic Seizures with Complex Network

Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered conn...

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Detalles Bibliográficos
Autores principales: Ni, Yan, Wang, Yinghua, Yu, Tao, Li, Xiaoli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134795/
https://www.ncbi.nlm.nih.gov/pubmed/25147576
http://dx.doi.org/10.1155/2014/283146
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author Ni, Yan
Wang, Yinghua
Yu, Tao
Li, Xiaoli
author_facet Ni, Yan
Wang, Yinghua
Yu, Tao
Li, Xiaoli
author_sort Ni, Yan
collection PubMed
description Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures.
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spelling pubmed-41347952014-08-21 Analysis of Epileptic Seizures with Complex Network Ni, Yan Wang, Yinghua Yu, Tao Li, Xiaoli Comput Math Methods Med Research Article Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures. Hindawi Publishing Corporation 2014 2014-07-23 /pmc/articles/PMC4134795/ /pubmed/25147576 http://dx.doi.org/10.1155/2014/283146 Text en Copyright © 2014 Yan Ni et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ni, Yan
Wang, Yinghua
Yu, Tao
Li, Xiaoli
Analysis of Epileptic Seizures with Complex Network
title Analysis of Epileptic Seizures with Complex Network
title_full Analysis of Epileptic Seizures with Complex Network
title_fullStr Analysis of Epileptic Seizures with Complex Network
title_full_unstemmed Analysis of Epileptic Seizures with Complex Network
title_short Analysis of Epileptic Seizures with Complex Network
title_sort analysis of epileptic seizures with complex network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134795/
https://www.ncbi.nlm.nih.gov/pubmed/25147576
http://dx.doi.org/10.1155/2014/283146
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