Metastable resting state brain dynamics

Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. Beim Graben and Hutt sugges...

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Detalhes bibliográficos
Autores: beim Graben, P., Jimenez-Marin, A., Diez, I., Cortes, J.M., Desroches, M., Rodrigues, S.
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2019
País:España
Recursos:Basque Center for Applied Mathematics (BCAM)
Repositório:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1016
Acesso em linha:http://hdl.handle.net/20.500.11824/1016
Access Level:Acceso aberto
Palavra-chave:Resting State
Recurrence Structure Analysis
Metastability
BOLD fMRI
Diffusion Tensor Imaging
Brain Hierarchical Atlas
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spelling Metastable resting state brain dynamicsbeim Graben, P.Jimenez-Marin, A.Diez, I.Cortes, J.M.Desroches, M.Rodrigues, S.Resting StateRecurrence Structure AnalysisMetastabilityBOLD fMRIDiffusion Tensor ImagingBrain Hierarchical AtlasMetastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. Beim Graben and Hutt suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. for the segmentation of system’s trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al., and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation —BOLD— signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence towards an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.Ikerbasque, FEDER grant DPI2016-79874-R, Elkartek Program KK-2018/00032201920192019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1016reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://www.frontiersin.org/articles/10.3389/fncom.2019.00062/fullinfo:eu-repo/grantAgreement/MINECO//SEV-2017-0718info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093860-B-C21info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/10162026-06-19T12:47:47Z
dc.title.none.fl_str_mv Metastable resting state brain dynamics
title Metastable resting state brain dynamics
spellingShingle Metastable resting state brain dynamics
beim Graben, P.
Resting State
Recurrence Structure Analysis
Metastability
BOLD fMRI
Diffusion Tensor Imaging
Brain Hierarchical Atlas
title_short Metastable resting state brain dynamics
title_full Metastable resting state brain dynamics
title_fullStr Metastable resting state brain dynamics
title_full_unstemmed Metastable resting state brain dynamics
title_sort Metastable resting state brain dynamics
dc.creator.none.fl_str_mv beim Graben, P.
Jimenez-Marin, A.
Diez, I.
Cortes, J.M.
Desroches, M.
Rodrigues, S.
author beim Graben, P.
author_facet beim Graben, P.
Jimenez-Marin, A.
Diez, I.
Cortes, J.M.
Desroches, M.
Rodrigues, S.
author_role author
author2 Jimenez-Marin, A.
Diez, I.
Cortes, J.M.
Desroches, M.
Rodrigues, S.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Resting State
Recurrence Structure Analysis
Metastability
BOLD fMRI
Diffusion Tensor Imaging
Brain Hierarchical Atlas
topic Resting State
Recurrence Structure Analysis
Metastability
BOLD fMRI
Diffusion Tensor Imaging
Brain Hierarchical Atlas
description Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. Beim Graben and Hutt suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. for the segmentation of system’s trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al., and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation —BOLD— signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence towards an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1016
url http://hdl.handle.net/20.500.11824/1016
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.frontiersin.org/articles/10.3389/fncom.2019.00062/full
info:eu-repo/grantAgreement/MINECO//SEV-2017-0718
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093860-B-C21
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021
info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
repository.name.fl_str_mv
repository.mail.fl_str_mv
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