Sampling the “dynome” leads to dynamic Functional Connectivity

Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average Functional Connectivity (FC) in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC. In our NeuroImage paper, we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states.

We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. Nevertheless a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)).

Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations, since a proper accounting of its dynamic reconfiguration can help disentangling temporal from inter-subject and inter-cohort variability. Furthermore the capability of computational models to generate structured FCD may allow reverse-engineering its circuit-level underpinnings (and thus the physiological causes of its alterations in pathologies).

To know more:

  • E.C.A. Hansen*, D. Battaglia*, A. Spiegler, G. Deco & V.K. Jirsa. Functional connectivity dynamics: modeling the switching behavior of the resting state. NeuroImage 105, 525–535 (2015). [ *Shared first authorship].