Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction
Main Author: | |
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Publication Date: | 2022 |
Other Authors: | , , , , , |
Format: | Article |
Language: | eng |
Source: | Brazilian Journal of Medical and Biological Research |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2022000100654 |
Summary: | The study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P<0.05) between average BC changes and the improvements in upper-extremity Fugl-Meyer (UE-FM) scores at the primary sensorimotor cortex and the supplementary motor area for the CL hemisphere. These regions participate in processes related to the selection, planning, and execution of movement. Generally, higher increases in average BC over these areas were related to larger improvements in UE-FM assessment. Although the sample was small, these results suggest the possibility of using BC as an indication of brain plasticity mechanisms following stroke. |
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Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery predictionBetweenness centralityfMRIStrokeGraph metricsNetwork analysisThe study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P<0.05) between average BC changes and the improvements in upper-extremity Fugl-Meyer (UE-FM) scores at the primary sensorimotor cortex and the supplementary motor area for the CL hemisphere. These regions participate in processes related to the selection, planning, and execution of movement. Generally, higher increases in average BC over these areas were related to larger improvements in UE-FM assessment. Although the sample was small, these results suggest the possibility of using BC as an indication of brain plasticity mechanisms following stroke.Associação Brasileira de Divulgação Científica2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2022000100654Brazilian Journal of Medical and Biological Research v.55 2022reponame:Brazilian Journal of Medical and Biological Researchinstname:Associação Brasileira de Divulgação Científica (ABDC)instacron:ABDC10.1590/1414-431x2022e12036info:eu-repo/semantics/openAccessAlmeida,S.R.M.Stefano Filho,C.A.Vicentini,J.Novi,S.L.Mesquita,R.C.Castellano,G.Li,L.M.eng2022-08-11T00:00:00Zoai:scielo:S0100-879X2022000100654Revistahttps://www.bjournal.org/https://old.scielo.br/oai/scielo-oai.phpbjournal@terra.com.br||bjournal@terra.com.br1414-431X0100-879Xopendoar:2022-08-11T00:00Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC)false |
dc.title.none.fl_str_mv |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
title |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
spellingShingle |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction Almeida,S.R.M. Betweenness centrality fMRI Stroke Graph metrics Network analysis |
title_short |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
title_full |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
title_fullStr |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
title_full_unstemmed |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
title_sort |
Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction |
author |
Almeida,S.R.M. |
author_facet |
Almeida,S.R.M. Stefano Filho,C.A. Vicentini,J. Novi,S.L. Mesquita,R.C. Castellano,G. Li,L.M. |
author_role |
author |
author2 |
Stefano Filho,C.A. Vicentini,J. Novi,S.L. Mesquita,R.C. Castellano,G. Li,L.M. |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Almeida,S.R.M. Stefano Filho,C.A. Vicentini,J. Novi,S.L. Mesquita,R.C. Castellano,G. Li,L.M. |
dc.subject.por.fl_str_mv |
Betweenness centrality fMRI Stroke Graph metrics Network analysis |
topic |
Betweenness centrality fMRI Stroke Graph metrics Network analysis |
description |
The study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P<0.05) between average BC changes and the improvements in upper-extremity Fugl-Meyer (UE-FM) scores at the primary sensorimotor cortex and the supplementary motor area for the CL hemisphere. These regions participate in processes related to the selection, planning, and execution of movement. Generally, higher increases in average BC over these areas were related to larger improvements in UE-FM assessment. Although the sample was small, these results suggest the possibility of using BC as an indication of brain plasticity mechanisms following stroke. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2022000100654 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2022000100654 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1414-431x2022e12036 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Divulgação Científica |
publisher.none.fl_str_mv |
Associação Brasileira de Divulgação Científica |
dc.source.none.fl_str_mv |
Brazilian Journal of Medical and Biological Research v.55 2022 reponame:Brazilian Journal of Medical and Biological Research instname:Associação Brasileira de Divulgação Científica (ABDC) instacron:ABDC |
instname_str |
Associação Brasileira de Divulgação Científica (ABDC) |
instacron_str |
ABDC |
institution |
ABDC |
reponame_str |
Brazilian Journal of Medical and Biological Research |
collection |
Brazilian Journal of Medical and Biological Research |
repository.name.fl_str_mv |
Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC) |
repository.mail.fl_str_mv |
bjournal@terra.com.br||bjournal@terra.com.br |
_version_ |
1754302949004148736 |