Rodrigo Pereira Rocha — Università degli Studi di Padova #
Criticality unveiling subject variability in brain functional activity #
Large-scale neuronal dynamics models (i.e, whole-brain models) have attracted great attention of the scientific community to unveil key mechanisms about the relationship between brain function and brain structure. Despite the vast amount of whole-brain models found in literature with different degrees of complexity, some empirical signatures of brain at rest, such as the functional connections (FC) between brain regions and the emergence of resting state networks (RSNs) are only partially understood and still poorly reproduced. The increase in the predictive power of whole-brain models is a fundamental step that can trigger many fundamental applications, such as quantitative discriminations between healthy and lesioned subjects. It has been shown [1] that brain at rest may be poised near a critical state, defined as the special point in the space of parameters where the system displays the maximum susceptibility/complexity, i.e. it optimally and collectively responds to external inputs [2]. Exploiting this notion of criticality and following seminal work of Haimovici et al. [2], we propose a biological meaningful yet simple stochastic model which is able to predict the brain organization into resting state networks through only few independent parameters. In this study we developed biological meaningful parametrization of the structural connectivity matrix (SC) (i.e., the human connectome) that increases the match between simulated and empirical data. In addition, using our modeling approach we are able to distinguish between simulated healthy and lesioned brains.

[1] J. M. Beggs and D. Plenz, Neuronal avalanches in neocortical circuits (2003).

[2] A. Haimovici, E. Tagliazucchi, P. Balenzuela, D. R. Chialvo, Brain organization into resting state networks emerges at criticality on a model of the human connectome (2013).