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Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures

Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-var...

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Autores principales: Frusque, Gaëtan, Borgnat, Pierre, Gonçalves, Paulo, Jung, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759641/
https://www.ncbi.nlm.nih.gov/pubmed/33362688
http://dx.doi.org/10.3389/fneur.2020.579725
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author Frusque, Gaëtan
Borgnat, Pierre
Gonçalves, Paulo
Jung, Julien
author_facet Frusque, Gaëtan
Borgnat, Pierre
Gonçalves, Paulo
Jung, Julien
author_sort Frusque, Gaëtan
collection PubMed
description Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.
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spelling pubmed-77596412020-12-26 Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures Frusque, Gaëtan Borgnat, Pierre Gonçalves, Paulo Jung, Julien Front Neurol Neurology Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics. Frontiers Media S.A. 2020-12-11 /pmc/articles/PMC7759641/ /pubmed/33362688 http://dx.doi.org/10.3389/fneur.2020.579725 Text en Copyright © 2020 Frusque, Borgnat, Gonçalves and Jung. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Frusque, Gaëtan
Borgnat, Pierre
Gonçalves, Paulo
Jung, Julien
Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures
title Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures
title_full Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures
title_fullStr Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures
title_full_unstemmed Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures
title_short Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures
title_sort semi-automatic extraction of functional dynamic networks describing patient's epileptic seizures
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759641/
https://www.ncbi.nlm.nih.gov/pubmed/33362688
http://dx.doi.org/10.3389/fneur.2020.579725
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