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.
Highlights
Characterizing 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
EEG synchronization in different stages EEG synchronization based on Mutual information (MI) There were certain differences between the three stages for each patient from the statistical analysis indicated in Tables 1 and 2
For a patient with frontal lobe epilepsy, EEG synchronization is at the highest level in the ictal stage and synchronization distribution difference is most unbalanced in the ictal stage and most consistent in the awake stage
Summary
Characterizing 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. Characterizing the synchronous changes of epileptic seizures in different stages and investigating the propagation of electroencephalogram (EEG) signals in the brain network will be profound to. Studies have shown that nonlinear synchronization can be applied to evaluate the connectivity of cortex functions in different brain regions and
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