Identification of optimal structural connectivity using functional connectivity and neural modeling.

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State: Public
Version: author
Serval ID
serval:BIB_80DF55DC3DB7
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Identification of optimal structural connectivity using functional connectivity and neural modeling.
Journal
Journal of Neuroscience
Author(s)
Deco G., McIntosh A.R., Shen K., Hutchison R.M., Menon R.S., Everling S., Hagmann P., Jirsa V.K.
ISSN
1529-2401 (Electronic)
ISSN-L
0270-6474
Publication state
Published
Issued date
2014
Peer-reviewed
Oui
Volume
34
Number
23
Pages
7910-7916
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't Publication Status: ppublish PDF : Brief Communication
Abstract
The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.
Pubmed
Web of science
Open Access
Yes
Create date
18/07/2014 17:52
Last modification date
20/08/2019 14:41
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