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

Frontiers in Computational Neuroscience

Volumn , Issue SEPTEMBER, 2012, Pages 1-7

  Deco, Gustavo ^a   Senden, Mario ^b   Jirsa, Viktor ^c  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b MAASTRICHT UNIVERSITY   (Netherlands)

^c INSERM   (France)

Author keywords

Computational neuroscience; Connectivity matrix; fMRI modeling; Ongoing activity; Resting state

Indexed keywords

BRAIN MODELS; DYNAMICAL SYSTEMS; ISING MODEL; MAGNETIC RESONANCE IMAGING; SPIN DYNAMICS; TENSORS;

COMPUTATIONAL NEUROSCIENCE; CONNECTIVITY MATRIX; MULTIPLE ATTRACTORS; NEUROANATOMICAL CONNECTIVITY; RESTING STATE; SPATIO-TEMPORAL ORGANIZATION; SPECTRUM IMAGING; THEORETICAL STUDY;

DIFFUSION TENSOR IMAGING;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN FUNCTION; CONTROLLED STUDY; ENTROPY; FUNCTIONAL ANATOMY; FUNCTIONAL ASSESSMENT; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GLOBAL COUPLING STRENGTH; HUMAN; HUMAN EXPERIMENT; ISING SPIN ATTRACTOR MODEL; NERVE CELL NETWORK; NERVOUS SYSTEM PARAMETERS; NEUROANATOMY; RESTING STATE NETWORK; STOCHASTIC MODEL; STRUCTURE ANALYSIS;

EID: 84866790204     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2012.00068     Document Type: Article

References (29)

1. Albert, R., and Barabási, A. L. (2002). Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47-97.
2. Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541.
3. Bullmore, E., and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198.
4. Clauset, A., Shalizi, C., and Newman, M. (2007). Power-law distributions in empirical data. SIAM Rev. 51, 661-703.
5. Crauel, H., Debussche, A., and Flandoli, F. (1997). Random attractors. J. Dyn. Differ. Equ. 9, 307-341.
6. Creutzfeldt, O. D., Watanabe, S., and Lux, H. D. (1966). Relations between EEG phenomena and potentials of single cortical cells. I. Evoked responses after thalamic and epicortical stimulation. Electroencephalogr. Clin. Neurophysiol. 20, 1-18.
7. Damoiseaux, J. S., Rombouts, S. A., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., and Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U.S.A. 103, 13848-13853.
8. Deco, G., and Jirsa, V. K. (2012). Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. J. Neurosci. 32, 3366-3375.
9. Deco, G., Jirsa, V., and McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci. 12, 43-56.
10. Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., and Kotter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proc. Natl. Acad. Sci. U.S.A. 106, 10302-10307.
11. Engel, A. K. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704.
12. Friston, K., and Ao, P. (2012). Free energy, value, and attractors. Comput. Math. Methods Med. 2012, 1-27.
13. Fox, M. D., and Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700-711.
14. Ghosh, A., Rho, Y., McIntosh, A. R., Kotter, R., and Jirsa, V. K. (2008). Noise during rest enables the exploration of the brain's dynamic repertoire. PLoS Comput. Biol. 4:e1000196. doi: 10.1371/journal.pcbi.1000196
15. Glauber, R. J. (1963). Time-dependent statistics of the ising model. J. Math. Phys. 4, 294.
16. Goh, K.-I., Kahng, B., and Kim, D. (2001). Universal behavior of load distribution in scale-free networks. Phys. Rev. Lett. 87, 278701-278704.
17. Gros, C. (2009). Cognitive computation with autonomously active neural networks: an emerging field. Cogn. Comput. 1, 77-90.
18. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., and Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biol. 6:e159. doi: 10.1371/journal.pbio.0060159
19. Honey, C. J., Kotter, R., Breakspear, M., and Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. U.S.A. 104, 10240-10245.
20. Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., and Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U.S.A. 106, 2035-2040.
21. Jirsa, V., Sporns, O., Breakspear, M., Deco, G., and McIntosh, A. R. (2010). Towards the Virtual brain: network modeling of the intact and the damaged brain. Arch. Ital. Biol. 148, 189-205.
22. Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., and Arieli, A. (2003). Spontaneously emerging cortical representations of visual attributes. Nature 425, 954-956.
23. Kitzbichler, M., Smith, M., Christensen, S., and Bullmore, E. (2009). Broadband criticality of human brain networks synchronization. PLoS Comput. Biol. 5:e1000314. doi: 10.1371/journal.pcbi.1000314
24. Knock, S. A., McIntosh, A. R., Sporns, O., Kötter, R., Hagmann, P., and Jirsa, V. K. (2009). The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models. J. Neurosci. Methods 183, 86-94.
25. Kötter, R. (2004). Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2, 127-144.
26. Senden, M., Goebel, R., and Deco, G. (2012). Structural connectivity allows for multi-threading during rest: the structure of the cortex leads to efficient alternation between resting state exploratory behavior and default mode processing. Neuroimage 60, 2274-2284.
27. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., and Beckmann, C. F. (2009). Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A. 106, 13040-13045.
28. Watts, D. J., and Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature 393, 409-410.
29. Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., and Bullmore, E. T. (2010). Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50, 970-983