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Dynamics of cortical circuits lead to switching resting state functional connectivity

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If you have a question about this talk, please contact Mikail Rubinov.

Simulations of whole-brain mean-field computational models with a realistic connectivity dictated by tractography studies allow to reproduce with remarkable accuracy average functional interactions in the resting state, showing their emergence from the interplay between anatomy, interaction delays and noise-driven local dynamics. However, previous computational studies did not address experimentally observed occurrence of non-stationarity in resting state functional connectivity. In particular, empirical resting state functional connectivity manifests a rich temporal structure, characterized by switching transitions between a few discrete functional connectivity states. Here we discuss how state-of-the-art computational models fail dramatically in reproducing these spontaneous state transitions, despite their excellent fitting of the time-averaged functional connectivity. In this aspect, they are thus not qualitatively superior to extremely simplified linear stochastic models accounting uniquely for effects due to pure structure. We show then how non-linear dynamics can rescue the situation. Considering a model with dynamic bistability at the level of each area, we explore a novel subcritical dynamical regime which gives rise to a large repertoire of possible dynamical behaviors. The resulting modes of fluctuations are strongly reminiscent of some of the most frequently observed Resting State Networks. Thanks to the noise-driven exploration of this dynamical (and functional) repertoire, we are thus able to reproduce in silico the switching non-stationarity of functional connectivity observed in empirical resting state recordings.

This talk is part of the Brain Mapping Unit Networks Meeting and the Cambridge Connectome Consortium series.

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