An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

Sarah Carr; Arthur Gershon; Nassim  Shafiabadi; Samden D. Lhatoo; Curtis Tatsuoka; Satya S. Sahoo

doi:10.3389/fnint.2020.491403

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

Sarah Carr, Arthur Gershon, Nassim Shafiabadi, Samden D. Lhatoo, Curtis Tatsuoka, Satya S. Sahoo

Neuroimaging

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Article number	491403
Pages (from-to)	1-19
Journal	Frontiers in Integrative Neuroscience
Volume	14
DOIs	https://doi.org/10.3389/fnint.2020.491403
Publication status	Published - 12 Jan 2021

Keywords

epileptic seizure networks
diffusion tensor imaging
stereotactic EEG
functional connectivity
structural connectivity
Hierarchical clustering
integrative brain network analysis

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10.3389/fnint.2020.491403

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@article{957bfec9fab54af4a79efb92e5711555,

title = "An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data",

abstract = "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.",

keywords = "epileptic seizure networks, diffusion tensor imaging, stereotactic EEG, functional connectivity, structural connectivity, Hierarchical clustering, integrative brain network analysis",

author = "Sarah Carr and Arthur Gershon and Nassim Shafiabadi and Lhatoo, {Samden D.} and Curtis Tatsuoka and Sahoo, {Satya S.}",

note = "Funding Information: This work was supported in part by the National Institutes of Biomedical Imaging and Bioengineering (NIBIB) Big Data to Knowledge (BD2K) grant (U01EB020955), NSF grants 1636850 and 1561716. Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2021 Carr, Gershon, Shafiabadi, Lhatoo, Tatsuoka and Sahoo. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = jan,

day = "12",

doi = "10.3389/fnint.2020.491403",

language = "English",

volume = "14",

pages = "1--19",

journal = "Frontiers in Integrative Neuroscience",

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T1 - An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

AU - Carr, Sarah

AU - Gershon, Arthur

AU - Shafiabadi, Nassim

AU - Lhatoo, Samden D.

AU - Tatsuoka, Curtis

AU - Sahoo, Satya S.

N1 - Funding Information: This work was supported in part by the National Institutes of Biomedical Imaging and Bioengineering (NIBIB) Big Data to Knowledge (BD2K) grant (U01EB020955), NSF grants 1636850 and 1561716. Publisher Copyright: © Copyright © 2021 Carr, Gershon, Shafiabadi, Lhatoo, Tatsuoka and Sahoo. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/1/12

Y1 - 2021/1/12

N2 - 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.

AB - 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.

KW - epileptic seizure networks

KW - diffusion tensor imaging

KW - stereotactic EEG

KW - functional connectivity

KW - structural connectivity

KW - Hierarchical clustering

KW - integrative brain network analysis

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U2 - 10.3389/fnint.2020.491403

DO - 10.3389/fnint.2020.491403

M3 - Article

VL - 14

SP - 1

EP - 19

JO - Frontiers in Integrative Neuroscience

JF - Frontiers in Integrative Neuroscience

M1 - 491403

ER -

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

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Keywords

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