Rethinking segregation and integration: contributions of whole-brain modelling

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  • dc.contributor.author Deco, Gustavoca
  • dc.contributor.author Tononi, Giulioca
  • dc.contributor.author Boly, Melanieca
  • dc.contributor.author Kringelbach, Morten L.ca
  • dc.date.accessioned 2016-07-19T07:36:41Z
  • dc.date.available 2016-07-19T07:36:41Z
  • dc.date.issued 2015ca
  • dc.description.abstract The brain regulates information flow by balancing the segregation and integration of incoming stimuli to facilitate flexible cognition and behaviour. The topological features of brain networks--in particular, network communities and hubs--support this segregation and integration but do not provide information about how external inputs are processed dynamically (that is, over time). Experiments in which the consequences of selective inputs on brain activity are controlled and traced with great precision could provide such information. However, such strategies have thus far had limited success. By contrast, recent whole-brain computational modelling approaches have enabled us to start assessing the effect of input perturbations on brain dynamics in silico.
  • dc.description.sponsorship G.D. is supported by the European Research Council (ERC) Advanced grant: DYSTRUCTURE (no. 295129), by the Spanish Research Project SAF2010-16085, by the FP7-ICT BrainScales and by the Brain Network Recovery Group through the James S. McDonnell Foundation. G.T. is supported by the Paul Allen Family Foundation and by the James S. McDonnell Foundation. M.B. is supported by the Mind Science Foundation. M.L.K. is supported by the ERC Consolidator grant: CAREGIVING (no. 615539) and by the TrygFonden Charitable Foundation. The authors thank P. Maquet for agreeing to share the previously published sleep and wakefulness functional MRI data for the purposes of this article.
  • dc.format.mimetype application/pdfca
  • dc.identifier.citation Deco G, Tononi G, Boly M, Kringelbach ML. Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci. 2015;16(7):430-9. DOI: 10.1038/nrn3963ca
  • dc.identifier.doi http://dx.doi.org/10.1038/nrn3963
  • dc.identifier.issn 1471-003Xca
  • dc.identifier.uri http://hdl.handle.net/10230/27083
  • dc.language.iso engca
  • dc.publisher Nature Publishing Groupca
  • dc.relation.ispartof Nature Reviews Neuroscience. 2015;16(7):430-9
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129ca
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/615539
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269921
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/SAF2010-16085
  • dc.rights © Nature Publishing Group. http://dx.doi.org/10.1038/nrn3963ca
  • dc.rights.accessRights info:eu-repo/semantics/openAccessca
  • dc.subject.keyword Diffusion tensor imaging
  • dc.subject.keyword Functional magnetic resonance imaging
  • dc.subject.keyword Network models
  • dc.title Rethinking segregation and integration: contributions of whole-brain modellingca
  • dc.type info:eu-repo/semantics/articleca
  • dc.type.version info:eu-repo/semantics/acceptedVersionca

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