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 (2013)...

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Autores: Beim Graben, Peter, Jiménez Marín, Antonio, Díez Palacio, Ibai, Cortés Díaz, Jesús María, Desroches, Mathieu, Rodrigues, Serafim
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/43855
Acesso em linha:http://hdl.handle.net/10810/43855
Access Level:acceso abierto
Palavra-chave:resting state
recurrence structure analysis
metastability
BOLD fMR
diffusion tensor imaging
brain hierarchical atlas
networks
model
FMRI
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spelling Metastable Resting State Brain DynamicsBeim Graben, PeterJiménez Marín, AntonioDíez Palacio, IbaiCortés Díaz, Jesús MaríaDesroches, MathieuRodrigues, Serafimresting staterecurrence structure analysismetastabilityBOLD fMRdiffusion tensor imagingbrain hierarchical atlasnetworksmodelFMRIMetastability 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 (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) 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. (2015), 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 toward 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.SR would like to acknowledge Ikerbasque (The Basque Foundation for Science) and moreover, this research is supported by the Basque Government through the BERC 2018-2021 program and by the Spanish State Research Agency through BCAM Severo Ochoa excellence accreditation SEV2017-0718 and through project RTI2018-093860-B-C21 funded by (AEI/FEDER, UE) and acronym MathNEURO. JC acknowledges financial support from Ikerbasque, Ministerio Economia, Industria y Competitividad (Spain) and FEDER (grant DPI2016-79874-R) and the Department of Economical Development and Infrastructure of the Basque Country (Elkartek Program, KK-2018/00032). Finally, PG acknowledges BCAM's hospitality during a visiting fellowship in fall 2017.Frontiers Media202020202019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/43855reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MINECO/SEV2017-0718/info:eu-repo/grantAgreement/MINECO/ RTI2018-093860-B-C21/info:eu-repo/grantAgreement/MINECO/DPI2016-79874-R/https://www.frontiersin.org/articles/10.3389/fncom.2019.00062/fullinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/2019 beim Graben, Jimenez-Marin, Diez, Cortes, Desroches and Rodrigues. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Atribución 3.0 Españaoai:addi.ehu.eus:10810/438552026-06-18T09:23:17Z
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, Peter
resting state
recurrence structure analysis
metastability
BOLD fMR
diffusion tensor imaging
brain hierarchical atlas
networks
model
FMRI
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, Peter
Jiménez Marín, Antonio
Díez Palacio, Ibai
Cortés Díaz, Jesús María
Desroches, Mathieu
Rodrigues, Serafim
author Beim Graben, Peter
author_facet Beim Graben, Peter
Jiménez Marín, Antonio
Díez Palacio, Ibai
Cortés Díaz, Jesús María
Desroches, Mathieu
Rodrigues, Serafim
author_role author
author2 Jiménez Marín, Antonio
Díez Palacio, Ibai
Cortés Díaz, Jesús María
Desroches, Mathieu
Rodrigues, Serafim
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv resting state
recurrence structure analysis
metastability
BOLD fMR
diffusion tensor imaging
brain hierarchical atlas
networks
model
FMRI
topic resting state
recurrence structure analysis
metastability
BOLD fMR
diffusion tensor imaging
brain hierarchical atlas
networks
model
FMRI
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 (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) 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. (2015), 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 toward 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
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/43855
url http://hdl.handle.net/10810/43855
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO/SEV2017-0718/
info:eu-repo/grantAgreement/MINECO/ RTI2018-093860-B-C21/
info:eu-repo/grantAgreement/MINECO/DPI2016-79874-R/
https://www.frontiersin.org/articles/10.3389/fncom.2019.00062/full
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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