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PLoS Comput Biol. 2015 Feb 18;11(2):e1004100. doi: 10.1371/journal.pcbi.1004100. eCollection 2015 Feb.

Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity.

Author information

1
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
2
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain.
3
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland; Signal Processing Lab 5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
4
Institute of Advanced Biomedical Technologies-G. d'Annunzio University Foundation, Department of Neuroscience and Imaging, G. d'Annunzio University, Chieti, Italy.
5
Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
6
Department of Neurology, Radiology, Anatomy of Neurobiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America.

Abstract

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain's anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.

PMID:
25692996
PMCID:
PMC4333573
DOI:
10.1371/journal.pcbi.1004100
[Indexed for MEDLINE]
Free PMC Article

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