Abstract
A major challenge in neuroscience is posed by the need for relating the emerging dynamical features of brain activity with the underlying modular structure of neural connections, hierarchically organized throughout several scales. The spontaneous emergence of coherence and synchronization across such scales is crucial to neural function, while its anomalies often relate to pathological conditions. Here we provide a numerical study of synchronization dynamics in the human connectome network. Our purpose is to provide a detailed characterization of the recently uncovered broad dynamic regime, interposed between order and disorder, which stems from the hierarchical modular organization of the human connectome. In this regime—similar in essence to a Griffiths phase—synchronization dynamics are trapped within metastable attractors of local coherence. Here we explore the role of noise, as an effective description of external perturbations, and discuss how its presence accounts for the ability of the system to escape intermittently from such attractors and explore complex dynamic repertoires of locally coherent states, in analogy with experimentally recorded patterns of cerebral activity.
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Notes
- 1.
Notice that this definition of \(\sigma \), that we call, “time variability” is closely related to the chimera index introduced by Shanahan [31]. While chimera indices are averaged between individual network moduli and measure the onset of local coherence, \(\sigma \) is defined at the global level and records fluctuations of the global order parameter.
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Acknowledgments
We acknowledge financial support from J. de Andalucía P09-FQM-4682 and the Spanish MINECO FIS2012-37655-C02-01 and FIS2013-43201-P.
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Villegas, P., Hidalgo, J., Moretti, P., Muñoz, M.A. (2016). Complex Synchronization Patterns in the Human Connectome Network. In: Battiston, S., De Pellegrini, F., Caldarelli, G., Merelli, E. (eds) Proceedings of ECCS 2014. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-29228-1_7
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