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dc.contributor.author | Deco, Gustavo |
dc.contributor.author | McIntosh, Anthony R. |
dc.contributor.author | Shen, Kelly |
dc.contributor.author | Hutchison, R. Matthew |
dc.contributor.author | Menon, Ravi S. |
dc.contributor.author | Everling, Stefan |
dc.contributor.author | Hagmann, Patric |
dc.contributor.author | Jirsa, Viktor K. |
dc.date.accessioned | 2015-02-02T09:29:42Z |
dc.date.available | 2015-02-02T09:29:42Z |
dc.date.issued | 2014 |
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.4423-13.2014 |
dc.identifier.issn | 0270-6474 |
dc.identifier.uri | http://hdl.handle.net/10230/23090 |
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. |
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/pdf |
dc.language.iso | eng |
dc.publisher | Society for Neuroscience |
dc.relation.ispartof | The Journal of Neuroscience. 2014 Jun;34(23):7910-6 |
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/ |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/ |
dc.title | Identification of optimal structural connectivity using functional connectivity and neural modeling |
dc.type | info:eu-repo/semantics/article |
dc.identifier.doi | http://dx.doi.org/10.1523/JNEUROSCI.4423-13.2014 |
dc.subject.keyword | Anatomy |
dc.subject.keyword | fMRI |
dc.subject.keyword | Functional connectivity |
dc.subject.keyword | Modeling |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/295129 |
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.accessRights | info:eu-repo/semantics/openAccess |
dc.type.version | info:eu-repo/semantics/publishedVersion |