How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model
Resting state networks (RSNs) show a surprisingly coherent and robust spatiotemporal/norganization. Previous theoretical studies demonstrated that these patterns can be/nunderstood as emergent on the basis of the underlying neuroanatomical connectivity/nskeleton. Integrating the biologically realist...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/23078 |
| Acceso en línea: | http://hdl.handle.net/10230/23078 http://dx.doi.org/10.3389/fncom.2012.00068 |
| Access Level: | acceso abierto |
| Palabra clave: | Computational neuroscience fMRI modeling Ongoing activity Resting state Connectivity matrix |
| Sumario: | Resting state networks (RSNs) show a surprisingly coherent and robust spatiotemporal/norganization. Previous theoretical studies demonstrated that these patterns can be/nunderstood as emergent on the basis of the underlying neuroanatomical connectivity/nskeleton. Integrating the biologically realistic DTI/DSI-(Diffusion Tensor Imaging/Diffusion/nSpectrum Imaging)based neuroanatomical connectivity into a brain model of Ising/nspin dynamics, we found a system with multiple attractors, which can be studied/nanalytically. The multistable attract/nor landscape thus defines a functionally meaningful/ndynamic repertoire of the brain network that is inherently present in the neuroanatomical/nconnectivity. We demonstrate that the more entropy of attractors exists, the richer is the/ndynamical repertoire and consequently the brain network displays more capabilities of/ncomputation. We hypothesize therefore tha/nt human brain connectivity developed a scale/nfree type of architecture in order to be able to store a large number of different and flexibly/naccessible brain functions. |
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