Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke

Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity b...

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Autores: Adhikari, Mohit H., Griffis, Joseph C., Siegel, Joshua S., Thiebaut de Schotten, Michel, Deco, Gustavo, Instabato, Andrea, Gilson, Matthieu, Corbetta, Maurizio
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2021
País:España
Recursos:Universitat Pompeu Fabra
Repositório:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/56049
Acesso em linha:http://hdl.handle.net/10230/56049
http://dx.doi.org/10.1093/braincomms/fcab233
Access Level:Acceso aberto
Palavra-chave:functional connectivity
effective connectivity
classification
whole-brain modelling
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dc.title.none.fl_str_mv Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
title Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
spellingShingle Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
Adhikari, Mohit H.
functional connectivity
effective connectivity
classification
whole-brain modelling
title_short Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
title_full Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
title_fullStr Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
title_full_unstemmed Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
title_sort Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
dc.creator.none.fl_str_mv Adhikari, Mohit H.
Griffis, Joseph C.
Siegel, Joshua S.
Thiebaut de Schotten, Michel
Deco, Gustavo
Instabato, Andrea
Gilson, Matthieu
Corbetta, Maurizio
author Adhikari, Mohit H.
author_facet Adhikari, Mohit H.
Griffis, Joseph C.
Siegel, Joshua S.
Thiebaut de Schotten, Michel
Deco, Gustavo
Instabato, Andrea
Gilson, Matthieu
Corbetta, Maurizio
author_role author
author2 Griffis, Joseph C.
Siegel, Joshua S.
Thiebaut de Schotten, Michel
Deco, Gustavo
Instabato, Andrea
Gilson, Matthieu
Corbetta, Maurizio
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv functional connectivity
effective connectivity
classification
whole-brain modelling
topic functional connectivity
effective connectivity
classification
whole-brain modelling
description Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modelling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke versus healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity versus model effective connectivity in predicting the long-term outcome from acute measures. Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level; however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1- to 2-week, 3-month and 1-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioural domains in which patients suffered deficits, both at 2-week and 1-year post-onset of stroke. Interestingly, patient deficits at 1-year time-point were better predicted by effective connectivity values at 2 weeks rather than at 1-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/56049
http://dx.doi.org/10.1093/braincomms/fcab233
url http://hdl.handle.net/10230/56049
http://dx.doi.org/10.1093/braincomms/fcab233
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Brain Communications. 2021;3(4):fcab233.
https://academic.oup.com/braincomms/article-lookup/doi/10.1093/braincomms/fcab233#supplementary-data
info:eu-repo/grantAgreement/EC/H2020/945539
info:eu-repo/grantAgreement/EC/H2020/785907
info:eu-repo/grantAgreement/EC/H2020/818521
info:eu-repo/grantAgreement/ES/2PE/PID2019-105772GB-I00
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
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spelling Effective connectivity extracts clinically relevant prognostic information from resting state activity in strokeAdhikari, Mohit H.Griffis, Joseph C.Siegel, Joshua S.Thiebaut de Schotten, MichelDeco, GustavoInstabato, AndreaGilson, MatthieuCorbetta, Mauriziofunctional connectivityeffective connectivityclassificationwhole-brain modellingRecent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modelling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke versus healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity versus model effective connectivity in predicting the long-term outcome from acute measures. Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level; however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1- to 2-week, 3-month and 1-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioural domains in which patients suffered deficits, both at 2-week and 1-year post-onset of stroke. Interestingly, patient deficits at 1-year time-point were better predicted by effective connectivity values at 2 weeks rather than at 1-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis.M.H.A. and M.C. were supported by National Institutes of Health grant R01 NS095741 to M.C. M.C. was also supported by Flag-Era joint transnational call 2017; Departments of Excellence Italian Ministry of Research (MIUR); Cariparo Foundation Excellence grants 2019; Ministry of Health Italy RF-2018–12366899. M.T.S. was supported by European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 818521). G.D. is supported by the Spanish Research Project (ref. PID2019-105772GB-I00 AEI FEDER EU), funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI) and European Regional Development Funds (FEDER); and Human Brain Project Specific Grant Agreement 3 (grant agreement no. 945539), funded by the European Union Horizon 2020 Future and Emerging Technologies Flagship program and Research Support Group support (ref. 2017 SGR 1545), funded by the Catalan Agency for Management of University and Research Grants (AGAUR). A.I. was supported by the European Union Horizon 2020 Research and Innovation Programme Grant 785907 (Human Brain Project SGA2) and 945539 (Human Brain Project SGA3). M.G acknowledges funding from the German Excellence Strategy of the Federal Government and the L ̈ander (G:(DE-82)EXS-PF-JARA-SDS005) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 785907 (Human Brain Project SGA2).Oxford University Press202320232021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/56049http://dx.doi.org/10.1093/braincomms/fcab233reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésBrain Communications. 2021;3(4):fcab233.https://academic.oup.com/braincomms/article-lookup/doi/10.1093/braincomms/fcab233#supplementary-datainfo:eu-repo/grantAgreement/EC/H2020/945539info:eu-repo/grantAgreement/EC/H2020/785907info:eu-repo/grantAgreement/EC/H2020/818521info:eu-repo/grantAgreement/ES/2PE/PID2019-105772GB-I00VC The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/560492026-06-12T07:21:37Z
score 15,812429

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