Metastable Resting State Brain Dynamics

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dc.contributor.authorbeim Graben, Peter
dc.contributor.authorJimenez-Marin, Antonio
dc.contributor.authorDiez, Ibai
dc.contributor.authorCortes, Jesus M.
dc.contributor.authorDesroches, Mathieu
dc.contributor.authorRodrigues, Serafim
dc.date.accessioned2019-11-08T09:13:37Z
dc.date.available2019-11-08T09:13:37Z
dc.date.issued2019-09
dc.description.abstractMetastability 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.en
dc.description.sponsorshipSR 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.en
dc.divisionSaluden
dc.identifier.citationBeim Graben, Peter, Antonio Jimenez-Marin, Ibai Diez, Jesus M. Cortes, Mathieu Desroches, and Serafim Rodrigues. “Metastable Resting State Brain Dynamics.” Frontiers in Computational Neuroscience 13 (September 6, 2019). doi:10.3389/fncom.2019.00062.en
dc.identifier.doi10.3389/fncom.2019.00062en
dc.identifier.issn1662-5188en
dc.identifier.urihttp://hdl.handle.net/11556/794
dc.journal.titleFrontiers in Computational Neuroscienceen
dc.language.isoengen
dc.page.initial62en
dc.publisherFrontiers Media S.A.en
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/SEV2017-0718
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de InvestigaciĂłn CientĂ­fica y TĂ©cnica y de InnovaciĂłn 2017-2020/RTI2018-093860-B-C21
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/DPI2016-79874-R
dc.relation.projectIDinfo:eu-repo/grantAgreement//KK-2018/00032
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.keywordsResting stateen
dc.subject.keywordsRecurrence structure analysisen
dc.subject.keywordsMetastabilityen
dc.subject.keywordsBOLD fMRIen
dc.subject.keywordsDiffusion tensor imagingen
dc.subject.keywordsBrain hierarchical atlasen
dc.titleMetastable Resting State Brain Dynamicsen
dc.typejournal articleen
dc.volume.number13en
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