Limits...
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.

Show MeSH

Related in: MedlinePlus

Emergence of transient synchronization patterns.a) Temporal evolution of activation strengths of the communities of the anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). b) Temporal evolution of the total community activation strength S (top) and the order parameter R (bottom) of the model anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). The correlation coefficient between S(t) and R(t) is 0.82 (p<10–3). c)Top: The largest correlation coefficient rmax (and 95% confidence interval) between the model communities and the empirical communities was calculated for various number of components (K = 2, …, 16) as a function of the global coupling. Middle: rmax was compared to the expected upper 95% confidence bound of the largest correlation coefficient (rmax, perm) between the model communities and 103 random permutations of the n elements of each empirical communities, for each K and each G. Bottom: Probability that rmax> rmax, perm.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4333573&req=5

pcbi.1004100.g007: Emergence of transient synchronization patterns.a) Temporal evolution of activation strengths of the communities of the anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). b) Temporal evolution of the total community activation strength S (top) and the order parameter R (bottom) of the model anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). The correlation coefficient between S(t) and R(t) is 0.82 (p<10–3). c)Top: The largest correlation coefficient rmax (and 95% confidence interval) between the model communities and the empirical communities was calculated for various number of components (K = 2, …, 16) as a function of the global coupling. Middle: rmax was compared to the expected upper 95% confidence bound of the largest correlation coefficient (rmax, perm) between the model communities and 103 random permutations of the n elements of each empirical communities, for each K and each G. Bottom: Probability that rmax> rmax, perm.

Mentions: In the following, we tested whether the model can generate the observed transient synchronized clusters, as a result of transitions among the metastable states. This was done by constructing the interaction tensor of the model and applying the NNTF (Fig. 7). We found that fluctuations in the global synchronization result from intermittent activation of synchronized communities (Fig. 7a-b). Moreover, the correlation maxima between the model’s communities and the empirical communities indicate a significant similarity between the model and the empirical community structures, for G ranging from 0.15 to 0.5 (Fig. 7c).


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)

Emergence of transient synchronization patterns.a) Temporal evolution of activation strengths of the communities of the anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). b) Temporal evolution of the total community activation strength S (top) and the order parameter R (bottom) of the model anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). The correlation coefficient between S(t) and R(t) is 0.82 (p<10–3). c)Top: The largest correlation coefficient rmax (and 95% confidence interval) between the model communities and the empirical communities was calculated for various number of components (K = 2, …, 16) as a function of the global coupling. Middle: rmax was compared to the expected upper 95% confidence bound of the largest correlation coefficient (rmax, perm) between the model communities and 103 random permutations of the n elements of each empirical communities, for each K and each G. Bottom: Probability that rmax> rmax, perm.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004100.g007: Emergence of transient synchronization patterns.a) Temporal evolution of activation strengths of the communities of the anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). b) Temporal evolution of the total community activation strength S (top) and the order parameter R (bottom) of the model anatomically connected heterogeneous Kuramoto model (G = 0.2 and K = 10). The correlation coefficient between S(t) and R(t) is 0.82 (p<10–3). c)Top: The largest correlation coefficient rmax (and 95% confidence interval) between the model communities and the empirical communities was calculated for various number of components (K = 2, …, 16) as a function of the global coupling. Middle: rmax was compared to the expected upper 95% confidence bound of the largest correlation coefficient (rmax, perm) between the model communities and 103 random permutations of the n elements of each empirical communities, for each K and each G. Bottom: Probability that rmax> rmax, perm.
Mentions: In the following, we tested whether the model can generate the observed transient synchronized clusters, as a result of transitions among the metastable states. This was done by constructing the interaction tensor of the model and applying the NNTF (Fig. 7). We found that fluctuations in the global synchronization result from intermittent activation of synchronized communities (Fig. 7a-b). Moreover, the correlation maxima between the model’s communities and the empirical communities indicate a significant similarity between the model and the empirical community structures, for G ranging from 0.15 to 0.5 (Fig. 7c).

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