Evidence map›Paper›PMID 30700279›Full record

ArticleBMC medical informatics and decision making2019

Epileptic foci localization based on mapping the synchronization of dynamic brain network.

Tian Mei, Xiaoyan Wei, Ziyi Chen, Xianghua Tian, Nan Dong, Dongmei Li, Yi Zhou

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Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Tian MeiDepartment of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
Xiaoyan WeiDepartment of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
Ziyi ChenDepartment of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
Xianghua TianDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, China.
Nan DongDepartment of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
Dongmei LiCollege of Public Health, Xinjiang Medical University, Urumqi, 830011, China.
Yi ZhouDepartment of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China. zhouyi@mail.sysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCharacterizing the synchronous changes of epileptic seizures in different stages between different regions is profound to understand the transmission pathways of epileptic brain network and epileptogenic foci. There is currently no adequate quantitative calculation method for describing the propagation pathways of electroencephalogram (EEG) signals in the brain network from the short and long term. The goal of this study is to explore the innovative method to locate epileptic foci, mapping synchronization in the brain networks based on EEG.

methodsMutual information was used to analyze the short-term synchronization in the full electrodes; while nonlinear dynamics quantifies the statistical independencies in the long -term among all electrodes. Then graph theory based on the complex network was employed to construct a dynamic brain network for epilepsy patients when they were awake, asleep and in seizure, analyzing the changing topology indexes.

resultsEpileptic network achieved a high degree of nonlinear synchronization compared to awake time. and the main path of epileptiform activity was revealed by searching core nodes. The core nodes of the brain network were in connection with the onset zone. Seizures always happened with a high degree of distribution.

conclusionsThis study indicated the path of EEG synchronous propagation in seizures, and core nodes could locate the epileptic foci accurately in some epileptic patients.

Indexed as

Models, TheoreticalCerebral CortexElectroencephalography Phase SynchronizationEpilepsyHumansNerve NetDynamic brain networkEpilepsyFoci locationSynchronization

Identifiers

PMID30700279
PMCPMC6354332

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