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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sarah J. A. Carr [verfasserIn] Arthur Gershon [verfasserIn] Nassim Shafiabadi [verfasserIn] Samden D. Lhatoo [verfasserIn] Curtis Tatsuoka [verfasserIn] Satya S. Sahoo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	epileptic seizure networks diffusion tensor imaging stereotactic EEG functional connectivity structural connectivity hierarchical clustering

Übergeordnetes Werk:	In: Frontiers in Integrative Neuroscience - Frontiers Media S.A., 2008, 14(2021)
Übergeordnetes Werk:	volume:14 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnint.2020.491403

Katalog-ID:	DOAJ073937398

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spelling	10.3389/fnint.2020.491403 doi (DE-627)DOAJ073937398 (DE-599)DOAJ7c1e4789d7094b039494dde1bc0c229d DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sarah J. A. Carr verfasserin aut An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. epileptic seizure networks diffusion tensor imaging stereotactic EEG functional connectivity structural connectivity hierarchical clustering Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Sarah J. A. Carr verfasserin aut Arthur Gershon verfasserin aut Nassim Shafiabadi verfasserin aut Nassim Shafiabadi verfasserin aut Samden D. Lhatoo verfasserin aut Curtis Tatsuoka verfasserin aut Curtis Tatsuoka verfasserin aut Satya S. Sahoo verfasserin aut Satya S. Sahoo verfasserin aut In Frontiers in Integrative Neuroscience Frontiers Media S.A., 2008 14(2021) (DE-627)579826406 (DE-600)2452962-X 16625145 nnns volume:14 year:2021 https://doi.org/10.3389/fnint.2020.491403 kostenfrei https://doaj.org/article/7c1e4789d7094b039494dde1bc0c229d kostenfrei https://www.frontiersin.org/articles/10.3389/fnint.2020.491403/full kostenfrei https://doaj.org/toc/1662-5145 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021
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allfieldsGer	10.3389/fnint.2020.491403 doi (DE-627)DOAJ073937398 (DE-599)DOAJ7c1e4789d7094b039494dde1bc0c229d DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sarah J. A. Carr verfasserin aut An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. epileptic seizure networks diffusion tensor imaging stereotactic EEG functional connectivity structural connectivity hierarchical clustering Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Sarah J. A. Carr verfasserin aut Arthur Gershon verfasserin aut Nassim Shafiabadi verfasserin aut Nassim Shafiabadi verfasserin aut Samden D. Lhatoo verfasserin aut Curtis Tatsuoka verfasserin aut Curtis Tatsuoka verfasserin aut Satya S. Sahoo verfasserin aut Satya S. Sahoo verfasserin aut In Frontiers in Integrative Neuroscience Frontiers Media S.A., 2008 14(2021) (DE-627)579826406 (DE-600)2452962-X 16625145 nnns volume:14 year:2021 https://doi.org/10.3389/fnint.2020.491403 kostenfrei https://doaj.org/article/7c1e4789d7094b039494dde1bc0c229d kostenfrei https://www.frontiersin.org/articles/10.3389/fnint.2020.491403/full kostenfrei https://doaj.org/toc/1662-5145 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021
allfieldsSound	10.3389/fnint.2020.491403 doi (DE-627)DOAJ073937398 (DE-599)DOAJ7c1e4789d7094b039494dde1bc0c229d DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sarah J. A. Carr verfasserin aut An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. epileptic seizure networks diffusion tensor imaging stereotactic EEG functional connectivity structural connectivity hierarchical clustering Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Sarah J. A. Carr verfasserin aut Arthur Gershon verfasserin aut Nassim Shafiabadi verfasserin aut Nassim Shafiabadi verfasserin aut Samden D. Lhatoo verfasserin aut Curtis Tatsuoka verfasserin aut Curtis Tatsuoka verfasserin aut Satya S. Sahoo verfasserin aut Satya S. Sahoo verfasserin aut In Frontiers in Integrative Neuroscience Frontiers Media S.A., 2008 14(2021) (DE-627)579826406 (DE-600)2452962-X 16625145 nnns volume:14 year:2021 https://doi.org/10.3389/fnint.2020.491403 kostenfrei https://doaj.org/article/7c1e4789d7094b039494dde1bc0c229d kostenfrei https://www.frontiersin.org/articles/10.3389/fnint.2020.491403/full kostenfrei https://doaj.org/toc/1662-5145 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021
language	English
source	In Frontiers in Integrative Neuroscience 14(2021) volume:14 year:2021
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institution	findex.gbv.de
topic_facet	epileptic seizure networks diffusion tensor imaging stereotactic EEG functional connectivity structural connectivity hierarchical clustering Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system
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container_title	Frontiers in Integrative Neuroscience
authorswithroles_txt_mv	Sarah J. A. Carr @@aut@@ Arthur Gershon @@aut@@ Nassim Shafiabadi @@aut@@ Samden D. Lhatoo @@aut@@ Curtis Tatsuoka @@aut@@ Satya S. Sahoo @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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callnumber-first	R - Medicine
author	Sarah J. A. Carr
spellingShingle	Sarah J. A. Carr misc RC321-571 misc RC346-429 misc epileptic seizure networks misc diffusion tensor imaging misc stereotactic EEG misc functional connectivity misc structural connectivity misc hierarchical clustering misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data
authorStr	Sarah J. A. Carr
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topic_title	RC321-571 RC346-429 An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data epileptic seizure networks diffusion tensor imaging stereotactic EEG functional connectivity structural connectivity hierarchical clustering
topic	misc RC321-571 misc RC346-429 misc epileptic seizure networks misc diffusion tensor imaging misc stereotactic EEG misc functional connectivity misc structural connectivity misc hierarchical clustering misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system
topic_unstemmed	misc RC321-571 misc RC346-429 misc epileptic seizure networks misc diffusion tensor imaging misc stereotactic EEG misc functional connectivity misc structural connectivity misc hierarchical clustering misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system
topic_browse	misc RC321-571 misc RC346-429 misc epileptic seizure networks misc diffusion tensor imaging misc stereotactic EEG misc functional connectivity misc structural connectivity misc hierarchical clustering misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system
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title	An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data
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title_full	An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data
author_sort	Sarah J. A. Carr
journal	Frontiers in Integrative Neuroscience
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author_browse	Sarah J. A. Carr Arthur Gershon Nassim Shafiabadi Samden D. Lhatoo Curtis Tatsuoka Satya S. Sahoo
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author-letter	Sarah J. A. Carr
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title_sort	integrative approach to study structural and functional network connectivity in epilepsy using imaging and signal data
callnumber	RC321-571
title_auth	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data
url	https://doi.org/10.3389/fnint.2020.491403 https://doaj.org/article/7c1e4789d7094b039494dde1bc0c229d https://www.frontiersin.org/articles/10.3389/fnint.2020.491403/full https://doaj.org/toc/1662-5145
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author2	Sarah J. A. Carr Arthur Gershon Nassim Shafiabadi Samden D. Lhatoo Curtis Tatsuoka Satya S. Sahoo
author2Str	Sarah J. A. Carr Arthur Gershon Nassim Shafiabadi Samden D. Lhatoo Curtis Tatsuoka Satya S. Sahoo
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up_date	2024-07-03T20:26:24.216Z
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