Identification of optimal structural connectivity using functional connectivity and neural modeling
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- dc.contributor.author Deco, Gustavoca
- dc.contributor.author McIntosh, Anthony R.ca
- dc.contributor.author Shen, Kellyca
- dc.contributor.author Hutchison, R. Matthewca
- dc.contributor.author Menon, Ravi S.ca
- dc.contributor.author Everling, Stefanca
- dc.contributor.author Hagmann, Patricca
- dc.contributor.author Jirsa, Viktor K.ca
- dc.date.accessioned 2015-02-02T09:29:42Z
- dc.date.available 2015-02-02T09:29:42Z
- dc.date.issued 2014ca
- dc.description.abstract The complex network dynamics that arise from the interaction of the brain’s structural and functional architectures give rise to mental/nfunction. Theoretical models demonstrate that the structure–function relation is maximal when the global network dynamics operate at/na critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity/n(SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize/nthe SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small/nnumber of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the/nnotion of a critical working point, where the structure–function interplay is maximal, may provide a new way to link behavior and/ncognition, and a new perspective to understand recovery of function in clinical conditions.en
- dc.description.sponsorship G.D. was supported by the European Research Council Advanced Grant DYSTRUCTURE (n.295129), by the Spanish/nResearch Project SAF2010-16085, and by the CONSOLIDER-INGENIO 2010 Programme CSD2007-00012. V.K.J. and/nG.D. are supported by FP7-ICT BrainScales. The research reported herein was supported by Collaborative Research/nGrant 220020255 from the James S. McDonnell Foundation. P.H. is supported by the Leenaards Foundation
- dc.format.extent 7 p.
- dc.format.mimetype application/pdfca
- dc.identifier.citation Deco G, McIntosh AR, Shen K, Hutchison RM, Menon RS, Everling S, Hagmann P, Jirsa VK. Identification of optimal structural connectivity using functional connectivity and neural modeling. J Neurosci. 2014 Jun;34(23):7910-6. DOI 10.1523/JNEUROSCI.4423-13.2014ca
- dc.identifier.doi http://dx.doi.org/10.1523/JNEUROSCI.4423-13.2014
- dc.identifier.issn 0270-6474ca
- dc.identifier.uri http://hdl.handle.net/10230/23090
- dc.language.iso engca
- dc.publisher Society for Neuroscienceca
- dc.relation.ispartof The Journal of Neuroscience. 2014 Jun;34(23):7910-6
- 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/2PN/CSD2007-00012
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/SAF2010-16085
- dc.rights The work is published under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license, as described at http://creativecommons.org/licenses/by-nc-sa/3.0/ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
- dc.subject.keyword Anatomy
- dc.subject.keyword fMRI
- dc.subject.keyword Functional connectivity
- dc.subject.keyword Modeling
- dc.title Identification of optimal structural connectivity using functional connectivity and neural modelingca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/publishedVersionca