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Unraveling Brain Modularity Through Slow Oscillations

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Nonlinear Dynamics in Computational Neuroscience

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Abstract

The intricate web of connections among the neurons composing the cerebral cortex is the seed of the complexity that our brain is capable to express. Such complexity is organized as it results from a hierarchical and modular organization of the network in which the roles of different cortical areas are distinct. Here, we speculate that such differentiation can be obtained by relying on the granular nature of the cortical surface tiled with ‘canonic’ modules which in turn can be flexibly tuned to compose diverse mesoscopic networks. The remarkable versatility of these cortical modules is governed by few key parameters like the excitability level and the sensitivity to the accumulated activity-dependent fatigue. These modules are naturally endowed with a rich repertoire of activity regimes which range from quasi-stable dynamics, possibly exploited to store information or provide persistent input to other modules, to collective oscillations reminiscent of the Up/Down activity cycle observed during sleep and deep anesthesia. Finally, we conclude showing that such slow oscillations, spontaneously expressed by the isolated cortex, can provide an ideal experimental framework to infer the dynamical properties of these cortical modules which in turn can inform also on cortical function in other brain states, such as during wakefulness.

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Acknowledgements

We thank C. Gonzales-Liencres for comments on this manuscript. This work was in part supported by CORTICONIC (EC FP7 grant 600806) and by the EU Horizon 2020 Research and Innovation Program under HBP SGA1 (grant 720270) to MM and MVSV, by the Spanish Ministerio de Ciencia e Innovación (BFU2017-85048-R) and by PCIN-2015-162-C02-01 (FLAG ERA) to MVSV.

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Authors and Affiliations

  1. Istituto Superiore di Sanità (ISS), Rome, Italy

    Maurizio Mattia

  2. ICREA, Systems Neuroscience, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain

    Maria V. Sanchez-Vives

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  1. Maurizio Mattia
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  2. Maria V. Sanchez-Vives
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Correspondence to Maurizio Mattia .

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  1. Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, Turin, Italy

    Fernando Corinto

  2. Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, Cergy-Pontoise, France

    Alessandro Torcini

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Mattia, M., Sanchez-Vives, M.V. (2019). Unraveling Brain Modularity Through Slow Oscillations. In: Corinto, F., Torcini, A. (eds) Nonlinear Dynamics in Computational Neuroscience. PoliTO Springer Series. Springer, Cham. https://doi.org/10.1007/978-3-319-71048-8_2

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