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How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
by
Senden, Mario
, Deco, Gustavo
, Jirsa, Viktor
in
Anatomy
/ Blood
/ Brain
/ Brain architecture
/ Computational neuroscience
/ Connectivity matrix
/ Entropy
/ fMRI modeling
/ Ghosts
/ Neural networks
/ Neuroscience
/ Neurosciences
/ Ongoing activity
/ Resting state
2012
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How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
by
Senden, Mario
, Deco, Gustavo
, Jirsa, Viktor
in
Anatomy
/ Blood
/ Brain
/ Brain architecture
/ Computational neuroscience
/ Connectivity matrix
/ Entropy
/ fMRI modeling
/ Ghosts
/ Neural networks
/ Neuroscience
/ Neurosciences
/ Ongoing activity
/ Resting state
2012
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Do you wish to request the book?
How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
by
Senden, Mario
, Deco, Gustavo
, Jirsa, Viktor
in
Anatomy
/ Blood
/ Brain
/ Brain architecture
/ Computational neuroscience
/ Connectivity matrix
/ Entropy
/ fMRI modeling
/ Ghosts
/ Neural networks
/ Neuroscience
/ Neurosciences
/ Ongoing activity
/ Resting state
2012
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How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
Journal Article
How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
2012
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Overview
Resting state networks (RSNs) show a surprisingly coherent and robust spatiotemporal organization. Previous theoretical studies demonstrated that these patterns can be understood as emergent on the basis of the underlying neuroanatomical connectivity skeleton. Integrating the biologically realistic DTI/DSI-(Diffusion Tensor Imaging/Diffusion Spectrum Imaging)based neuroanatomical connectivity into a brain model of Ising spin dynamics, we found a system with multiple attractors, which can be studied analytically. The multistable attractor landscape thus defines a functionally meaningful dynamic repertoire of the brain network that is inherently present in the neuroanatomical connectivity. We demonstrate that the more entropy of attractors exists, the richer is the dynamical repertoire and consequently the brain network displays more capabilities of computation. We hypothesize therefore that human brain connectivity developed a scale free type of architecture in order to be able to store a large number of different and flexibly accessible brain functions.
Publisher
Frontiers Research Foundation,Frontiers,Frontiers Media S.A
Subject
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