Scale-freeness or partial synchronization in neural mass phase oscillator networks: pick one of two?

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• dc.contributor.author Daffertshofer, Andreas
• dc.contributor.author Ton, Robert
• dc.contributor.author Pietras, Bastian
• dc.contributor.author Kringelbach, Morten L.
• dc.contributor.author Deco, Gustavo
• dc.date.accessioned 2022-10-18T08:49:29Z
• dc.date.available 2022-10-18T08:49:29Z
• dc.date.issued 2018
• dc.description.abstract Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both.
• dc.description.sponsorship We would like to thank Mark Woolrich for his contribution to data acquisition and analysis and the fruitful discussion about interpretation of our findings. We received the following funding: GD, RT: ERC Advanced Grant: DYSTRUCTURE (n. 295129), GD: Spanish Research Project PSI2013-42091-P, Human Brain Project, and FP7-ICT BrainScales (269921). MLK: ERC Consolidator Grant CAREGIVING (n. 615539) and Center for Music in the Brain/Danish National Research Foundation (DNRF117). AD, BP: H2020-MSA-ITN COSMOS (n. 642563).
• dc.format.mimetype application/pdf
• dc.identifier.citation Daffertshofer A, Ton R, Pietras B, Kringelbach ML, Deco G. Scale-freeness or partial synchronization in neural mass phase oscillator networks: pick one of two? NeuroImage. 2018 Oct 15;180(Pt B):428-41. DOI: 10.1016/j.neuroimage.2018.03.070
• dc.identifier.doi http://dx.doi.org/10.1016/j.neuroimage.2018.03.070
• dc.identifier.issn 1053-8119
• dc.identifier.uri http://hdl.handle.net/10230/54464
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof NeuroImage. 2018 Oct 15;180(Pt B):428-41
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269921
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2013-42091-P
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/615539
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/642563
• dc.rights © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Freeman model
• dc.subject.keyword Wilson-Cowan model
• dc.subject.keyword Phase dynamics
• dc.subject.keyword Synchronization
• dc.subject.keyword Power laws
• dc.subject.keyword Criticality
• dc.title Scale-freeness or partial synchronization in neural mass phase oscillator networks: pick one of two?
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion