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Neuroimage. 2015 Jan 15;105:525-35. doi: 10.1016/j.neuroimage.2014.11.001. Epub 2014 Nov 10.

Functional connectivity dynamics: modeling the switching behavior of the resting state.

Author information

1
Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, 27Bd Jean Moulin, 13005 Marseille, France. Electronic address: enrique-carlos.hansen@etu.univ-amu.fr.
2
Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, 27Bd Jean Moulin, 13005 Marseille, France; Bernstein Center for Computational Neuroscience, Am Faßberg 17, 37077 Göttingen, Germany. Electronic address: demian.battaglia@univ-amu.fr.
3
Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, 27Bd Jean Moulin, 13005 Marseille, France. Electronic address: andreas.spiegler@univ-amu.fr.
4
Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: gustavo.deco@upf.edu.
5
Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, 27Bd Jean Moulin, 13005 Marseille, France. Electronic address: viktor.jirsa@univ-amu.fr.

Abstract

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here 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. We then demonstrate that 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.

KEYWORDS:

Brain dynamics; Functional connectivity; Functional connectivity dynamics; Resting state; Structural connectivity; Whole brain computational model

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