Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity
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- dc.contributor.author Ponce-Alvarez, Adriánca
- dc.contributor.author Deco, Gustavoca
- dc.contributor.author Hagmann, Patricca
- dc.contributor.author Romani, Gian Lucaca
- dc.contributor.author Mantini, Danteca
- dc.contributor.author Corbetta, Maurizioca
- dc.date.accessioned 2015-10-30T08:28:02Z
- dc.date.available 2015-10-30T08:28:02Z
- dc.date.issued 2015ca
- dc.description.abstract Spatial patterns of coherent activity across different brain areas have been identified during/nthe resting-state fluctuations of the brain. However, recent studies indicate that resting-state/nactivity is not stationary, but shows complex temporal dynamics. We were interested in the/nspatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals/nfrom human subjects.We found that the global phase synchrony of the BOLD signals/nevolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations/nreflect the transient formation and dissolution of multiple communities of synchronized brain/nregions. Synchronized communities reoccurred intermittently in time and across scanning/nsessions. We found that the synchronization communities relate to previously defined functional/nnetworks known to be engaged in sensory-motor or cognitive function, called restingstate/nnetworks (RSNs), including the default mode network, the somato-motor network, the/nvisual network, the auditory network, the cognitive control networks, the self-referential network,/nand combinations of these and other RSNs. We studied the mechanism originating/nthe observed spatiotemporal synchronization dynamics by using a network model of phase/noscillators connected through the brain’s anatomical connectivity estimated using diffusion/nimaging human data. The model consistently approximates the temporal and spatial synchronization/npatterns of the empirical data, and reveals that multiple clusters that transiently/nsynchronize and desynchronize emerge from the complex topology of anatomical connections,/nprovided that oscillators are heterogeneous.
- dc.description.sponsorship GD was supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project SAF2010-16085, the FP7-ICT BrainScales and the Flagship Human Brain Project.
- dc.format.mimetype application/pdfca
- dc.identifier.citation Ponce-Alvarez A, Deco G, Hagmann P, Romani GL, Mantini D, Corbetta M. Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLoS Comput Biol. 2015;11(2):e1004100. DOI: 10.1371/journal.pcbi.1004100ca
- dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1004100
- dc.identifier.issn 1553-734Xca
- dc.identifier.uri http://hdl.handle.net/10230/24966
- dc.language.iso engca
- dc.publisher Public Library of Science (PLoS)ca
- dc.relation.ispartof PLoS Computational Biology. 2015;11(2):e1004100
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129ca
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269921
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/SAF2010-16085
- dc.rights This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedicationca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri https://creativecommons.org/publicdomain/zero/1.0/
- dc.title Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneityca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/publishedVersionca