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.

Gustavo Deco was supported by the Spanish Research/nProject SAF2010-16085 and by the CONSOLIDER-INGENIO/n2010 Programme CSD2007-00012. The research reported/nherein was supported by the ERC Advanced Grant:/nDYSTRUCTURE, by the Brain Network Recovery Group/nthrough the James S. McDonnell Foundation and the FP7-ICT/nBrainScales.