How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model

G. Deco, M. Senden, V. Jirsa

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article number68
Number of pages7
JournalFrontiers in Computational Neuroscience
Volume6
DOIs
Publication statusPublished - 20 Sep 2012

Keywords

  • CORTEX
  • FLUCTUATIONS
  • FUNCTIONAL CONNECTIVITY
  • NETWORKS
  • STRUCTURAL CONNECTIVITY
  • computational neuroscience
  • connectivity matrix
  • fMRI modeling
  • ongoing activity
  • resting state

Cite this

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title = "How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model",
abstract = "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.",
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language = "English",
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How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model. / Deco, G.; Senden, M.; Jirsa, V.

In: Frontiers in Computational Neuroscience, Vol. 6, 68, 20.09.2012.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model

AU - Deco, G.

AU - Senden, M.

AU - Jirsa, V.

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AB - 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.

KW - CORTEX

KW - FLUCTUATIONS

KW - FUNCTIONAL CONNECTIVITY

KW - NETWORKS

KW - STRUCTURAL CONNECTIVITY

KW - computational neuroscience

KW - connectivity matrix

KW - fMRI modeling

KW - ongoing activity

KW - resting state

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JO - Frontiers in Computational Neuroscience

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