Identification of optimal structural connectivity using functional connectivity and neural modeling

Deco, Gustavo; McIntosh, Anthony R.; Shen, Kelly; Hutchison, R. Matthew; Menon, Ravi S.; Everling, Stefan; Hagmann, Patric; Jirsa, Viktor K.

doi:http://dx.doi.org/10.1523/JNEUROSCI.4423-13.2014

Identification of optimal structural connectivity using functional connectivity and neural modeling

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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

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.