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

Ponce-Alvarez A, Deco G, Hagmann P, Romani GL, Mantini D, Corbetta M - PLoS Comput. Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

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.

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Dynamical range of the anatomically-constrained phase oscillators’ network.a) Time evolution of the phase difference between two nodes of the anatomically-connected heterogeneous Kuramoto model, for three values of the global coupling G. Left: in the weakly connected case (G = 0.025) the phases run almost independently; middle: with moderate coupling (G = 0.25) the phases tend to lock for short periods of time, as revealed by the deflections in the trajectory of the relative phase, indicating the presence of metastability; right: with strong coupling (G = 1.25) the phases are locked. b) Corresponding oscillations of the two nodes, for the three dynamical regimes.
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pcbi.1004100.g006: Dynamical range of the anatomically-constrained phase oscillators’ network.a) Time evolution of the phase difference between two nodes of the anatomically-connected heterogeneous Kuramoto model, for three values of the global coupling G. Left: in the weakly connected case (G = 0.025) the phases run almost independently; middle: with moderate coupling (G = 0.25) the phases tend to lock for short periods of time, as revealed by the deflections in the trajectory of the relative phase, indicating the presence of metastability; right: with strong coupling (G = 1.25) the phases are locked. b) Corresponding oscillations of the two nodes, for the three dynamical regimes.

Mentions: Altogether, the above results show that the empirical statistics are well described in the region of partial synchronization, the parameter range between disorder (asynchrony) and complete order (full synchrony). This parameter region is characterized by metastability (Fig. 6). Indeed, it has been argued that step-like increases of the order parameter as a function of the global coupling is an indication of metastability [31]. This is clearly shown by the behavior of the relative phase between two given nodes of the network model (Fig. 6a). For small values of the global coupling (G), the phases run practically independently. For intermediate values of G, quasi-phase-locking events are indicated by the deflections of the relative phase’s trajectory. Such events appear transiently and are separated by periods during which the phase-lock is lost. For high values of G, the nodes are synchronized, leading to a stable relation between their phases.


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

Ponce-Alvarez A, Deco G, Hagmann P, Romani GL, Mantini D, Corbetta M - PLoS Comput. Biol. (2015)

Dynamical range of the anatomically-constrained phase oscillators’ network.a) Time evolution of the phase difference between two nodes of the anatomically-connected heterogeneous Kuramoto model, for three values of the global coupling G. Left: in the weakly connected case (G = 0.025) the phases run almost independently; middle: with moderate coupling (G = 0.25) the phases tend to lock for short periods of time, as revealed by the deflections in the trajectory of the relative phase, indicating the presence of metastability; right: with strong coupling (G = 1.25) the phases are locked. b) Corresponding oscillations of the two nodes, for the three dynamical regimes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4333573&req=5

pcbi.1004100.g006: Dynamical range of the anatomically-constrained phase oscillators’ network.a) Time evolution of the phase difference between two nodes of the anatomically-connected heterogeneous Kuramoto model, for three values of the global coupling G. Left: in the weakly connected case (G = 0.025) the phases run almost independently; middle: with moderate coupling (G = 0.25) the phases tend to lock for short periods of time, as revealed by the deflections in the trajectory of the relative phase, indicating the presence of metastability; right: with strong coupling (G = 1.25) the phases are locked. b) Corresponding oscillations of the two nodes, for the three dynamical regimes.
Mentions: Altogether, the above results show that the empirical statistics are well described in the region of partial synchronization, the parameter range between disorder (asynchrony) and complete order (full synchrony). This parameter region is characterized by metastability (Fig. 6). Indeed, it has been argued that step-like increases of the order parameter as a function of the global coupling is an indication of metastability [31]. This is clearly shown by the behavior of the relative phase between two given nodes of the network model (Fig. 6a). For small values of the global coupling (G), the phases run practically independently. For intermediate values of G, quasi-phase-locking events are indicated by the deflections of the relative phase’s trajectory. Such events appear transiently and are separated by periods during which the phase-lock is lost. For high values of G, the nodes are synchronized, leading to a stable relation between their phases.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

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.

Show MeSH
Related in: MedlinePlus