Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

• DOI: 10.1371/journal.pcbi.1004100
• Publication Date: 2015-02-18T14:01:17Z
• Authors: Adrián Ponce-Alvarez; Gustavo Deco; Patric Hagmann; Gian Luca Romani; Dante Mantini; Maurizio Corbetta
• Keywords: Adult; Male; Brain Mapping; QH301-705.5; Electroencephalography Phase Synchronization; Models, Neurological; Brain; Classificació AMS::92 Biology and other natural sciences::92C Physiological, cellular and medical topics; Classificació AMS::92 Biology and other natural sciences::92C Physiological; cellular and medical topics; Cervell -- Investigació; Magnetic Resonance Imaging; Brain -- Research; Àrees temàtiques de la UPC::Enginyeria biomèdica; Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia; Neurologia; Young Adult; 03 medical and health sciences; 0302 clinical medicine; Neurology; Humans; Female; Biology (General); Adult; Brain; Brain Mapping; Electroencephalography Phase Synchronization; Female; Humans; Magnetic Resonance Imaging; Male; Young Adult; Models, Neurological; Ecology, Evolution, Behavior and Systematics; Modeling and Simulation; Ecology; Molecular Biology; Genetics; Cellular and Molecular Neuroscience; Computational Theory and Mathematics; Research Article

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.<br/>PLoS Computational Biology, 11 (2)<br/>ISSN:1553-734X<br/>ISSN:1553-7358<br/>