Resting brains never rest: computational insights into potential cognitive architectures

See on Scoop.itBiobit: Computational Neuroscience & Biocomputation

Nima Dehghani‘s insight:

interesting story…press news from Deco’s unversity:

During the normal waking state, the brain is in a constant state of internal exploration through the formation and dissolution of resting-state functional networks (RSNs).

Based on large-scale computer models of the brain, the best fit to observed data comes when the networks are at the ‘edge of instability’. Such a position is a distinct advantage for the efficiency and speed of network mobilization for perception and action.

A research article, published on May 2 in the journal Trends in Neurosciences, led byGustavo Deco, an ICREA researcher at DTIC ICREA-UPF and director of the Center for Brain and Cognition and Computational Neuroscience Group at Pompeu Fabra University, in collaboration with Viktor K. Jirsa, from the INSERM Marseille (France), and Anthony R. McIntosh, from the University of Toronto (Canada), provides theoretical and empirical questions to better link resting-state networks to cognitive architectures.

Resting-state networks (RSNs), which have become a main focus in neuroimaging research, can be best simulated by large-scale cortical models in which networks teeter on the edge of instability. In this state, the functional networks are in a low firing stable state while they are continuously pulled towards multiple other configurations. Small extrinsic perturbations can shape task-related network dynamics, whereas perturbations from intrinsic noise generate excursions reflecting the range of available functional networks.

This is particularly advantageous for the efficiency and speed of network mobilization. Thus, the resting state reflects the dynamical capabilities of the brain, which emphasizes the vital interplay of time and space. In this article, we propose a new theoretical framework for RSNs that can serve as a fertile ground for empirical testing.

One of the concluding remarks of the study is as brain network properties change, be it due to intersubject variability, learning, disease, aging, or development, the critical point of the network will also change, as will its associated dynamic features, including the subspace spanned by the RSNs and optimality of information processing through the network.

If criticality is indeed a principle of functional brain organization, then homeostatic mechanisms are required to maintain the brain network at criticality, which can be explored in theoretical and empirical work 

The measurement and modelling techniques discussed in this paper are therefore relevant not only for basic neuroscience, but also for their applications in the field of biomedicine.

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