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An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data
A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures durin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835283/ https://www.ncbi.nlm.nih.gov/pubmed/33510622 http://dx.doi.org/10.3389/fnint.2020.491403 |
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author | Carr, Sarah J. A. Gershon, Arthur Shafiabadi, Nassim Lhatoo, Samden D. Tatsuoka, Curtis Sahoo, Satya S. |
author_facet | Carr, Sarah J. A. Gershon, Arthur Shafiabadi, Nassim Lhatoo, Samden D. Tatsuoka, Curtis Sahoo, Satya S. |
author_sort | Carr, Sarah J. A. |
collection | PubMed |
description | A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied. |
format | Online Article Text |
id | pubmed-7835283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78352832021-01-27 An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data Carr, Sarah J. A. Gershon, Arthur Shafiabadi, Nassim Lhatoo, Samden D. Tatsuoka, Curtis Sahoo, Satya S. Front Integr Neurosci Neuroscience A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied. Frontiers Media S.A. 2021-01-12 /pmc/articles/PMC7835283/ /pubmed/33510622 http://dx.doi.org/10.3389/fnint.2020.491403 Text en Copyright © 2021 Carr, Gershon, Shafiabadi, Lhatoo, Tatsuoka and Sahoo. 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 | Neuroscience Carr, Sarah J. A. Gershon, Arthur Shafiabadi, Nassim Lhatoo, Samden D. Tatsuoka, Curtis Sahoo, Satya S. An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data |
title | An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data |
title_full | An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data |
title_fullStr | An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data |
title_full_unstemmed | An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data |
title_short | An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data |
title_sort | integrative approach to study structural and functional network connectivity in epilepsy using imaging and signal data |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835283/ https://www.ncbi.nlm.nih.gov/pubmed/33510622 http://dx.doi.org/10.3389/fnint.2020.491403 |
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