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dc.contributor.authorbeim Graben, P.
dc.contributor.authorJimenez-Marin, A.
dc.contributor.authorDiez, I.
dc.contributor.authorCortes, J.M.
dc.contributor.authorDesroches, M. 
dc.contributor.authorRodrigues, S. 
dc.date.accessioned2019-09-29T20:08:57Z
dc.date.available2019-09-29T20:08:57Z
dc.date.issued2019-09-06
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1016
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 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.en_US
dc.description.sponsorshipIkerbasque, FEDER grant DPI2016-79874-R, Elkartek Program KK-2018/00032en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectResting Stateen_US
dc.subjectRecurrence Structure Analysisen_US
dc.subjectMetastabilityen_US
dc.subjectBOLD fMRIen_US
dc.subjectDiffusion Tensor Imagingen_US
dc.subjectBrain Hierarchical Atlasen_US
dc.titleMetastable resting state brain dynamicsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.3389/fncom.2019.00062
dc.relation.publisherversionhttps://www.frontiersin.org/articles/10.3389/fncom.2019.00062/fullen_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/2PE/RTI2018-093860-B-C21en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleFrontiers in Computational Neuroscienceen_US


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