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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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Temporal dynamics of phase interactions.a) Example narrowband signals from two different brain regions (rPARH and rFUS). b) Probability density function of the phase differences, Pr(Δφ), across all pairs of brain regions and all time steps. c) Temporal evolution of Pr(Δφ), for 3 example scanning sessions from different subjects. The color code indicates the values of Pr(Δφ) at each time step (see the color bar at the bottom).White trace: Time course of the order parameter R(t) (right y-axis). d) Probability density function of the order parameter values for all sessions (black) compared to phase-randomized surrogates (red) and broadband signals (brown). e) Power spectrum of the order parameter. Gray traces: single scanning sessions; black trace: mean across all sessions. f) Comparison of the averaged power spectrum of the order parameter for narrowband (black) and broadband signals (brown). Power spectra were normalized by the corresponding maximum value.
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pcbi.1004100.g001: Temporal dynamics of phase interactions.a) Example narrowband signals from two different brain regions (rPARH and rFUS). b) Probability density function of the phase differences, Pr(Δφ), across all pairs of brain regions and all time steps. c) Temporal evolution of Pr(Δφ), for 3 example scanning sessions from different subjects. The color code indicates the values of Pr(Δφ) at each time step (see the color bar at the bottom).White trace: Time course of the order parameter R(t) (right y-axis). d) Probability density function of the order parameter values for all sessions (black) compared to phase-randomized surrogates (red) and broadband signals (brown). e) Power spectrum of the order parameter. Gray traces: single scanning sessions; black trace: mean across all sessions. f) Comparison of the averaged power spectrum of the order parameter for narrowband (black) and broadband signals (brown). Power spectra were normalized by the corresponding maximum value.

Mentions: The interaction between BOLD signals of different brain regions was measured using the instantaneous phase synchronization. For obtaining the phase of each brain region, the signals were first band-pass filtered within the narrowband 0.04–0.07Hz. Previous work has shown that this frequency band contains more robust and functionally relevant signals than the other bands [26]. Moreover, narrowband filtering is a methodological requirement for obtaining meaningful signal phases [26]. As shown in the Methods section, phase interactions fairly describe the interactions among the narrowband signals and, as shown in the following, they allow for a time-resolved analysis of interactions (see also S1 Fig.). As shown in Fig. 1a, the phase relation between two given BOLD narrowband signals changes over time, alternating periods during which the oscillations are in-phase and periods during which the oscillations are out-of-phase. To quantify these phase relations we computed the instantaneous phase φk(t) of each narrowband signal k using the Hilbert transform (HT) (see Methods). After this, the first and last 10 time steps were discarded to avoid border effects inherent to the HT, so that in the following T = 280. We next calculated the phase difference, Δφkl(t) = φk(t)–φl(t), for each pair of brain regions k and l, at each time step t. Across all pairs of signals and time steps, the probability density function (p.d.f.) of the pairwise phase differences, noted Pr(Δφ), is centred on zero (Fig. 1b). However, Pr(Δφ) is not constant, but evolves over time, going from a uniform distribution (i.e., the phases are independent) to a distribution that is densely concentrated around zero (i.e., high phase synchrony) (Fig. 1c, color plots).


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)

Temporal dynamics of phase interactions.a) Example narrowband signals from two different brain regions (rPARH and rFUS). b) Probability density function of the phase differences, Pr(Δφ), across all pairs of brain regions and all time steps. c) Temporal evolution of Pr(Δφ), for 3 example scanning sessions from different subjects. The color code indicates the values of Pr(Δφ) at each time step (see the color bar at the bottom).White trace: Time course of the order parameter R(t) (right y-axis). d) Probability density function of the order parameter values for all sessions (black) compared to phase-randomized surrogates (red) and broadband signals (brown). e) Power spectrum of the order parameter. Gray traces: single scanning sessions; black trace: mean across all sessions. f) Comparison of the averaged power spectrum of the order parameter for narrowband (black) and broadband signals (brown). Power spectra were normalized by the corresponding maximum value.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4333573&req=5

pcbi.1004100.g001: Temporal dynamics of phase interactions.a) Example narrowband signals from two different brain regions (rPARH and rFUS). b) Probability density function of the phase differences, Pr(Δφ), across all pairs of brain regions and all time steps. c) Temporal evolution of Pr(Δφ), for 3 example scanning sessions from different subjects. The color code indicates the values of Pr(Δφ) at each time step (see the color bar at the bottom).White trace: Time course of the order parameter R(t) (right y-axis). d) Probability density function of the order parameter values for all sessions (black) compared to phase-randomized surrogates (red) and broadband signals (brown). e) Power spectrum of the order parameter. Gray traces: single scanning sessions; black trace: mean across all sessions. f) Comparison of the averaged power spectrum of the order parameter for narrowband (black) and broadband signals (brown). Power spectra were normalized by the corresponding maximum value.
Mentions: The interaction between BOLD signals of different brain regions was measured using the instantaneous phase synchronization. For obtaining the phase of each brain region, the signals were first band-pass filtered within the narrowband 0.04–0.07Hz. Previous work has shown that this frequency band contains more robust and functionally relevant signals than the other bands [26]. Moreover, narrowband filtering is a methodological requirement for obtaining meaningful signal phases [26]. As shown in the Methods section, phase interactions fairly describe the interactions among the narrowband signals and, as shown in the following, they allow for a time-resolved analysis of interactions (see also S1 Fig.). As shown in Fig. 1a, the phase relation between two given BOLD narrowband signals changes over time, alternating periods during which the oscillations are in-phase and periods during which the oscillations are out-of-phase. To quantify these phase relations we computed the instantaneous phase φk(t) of each narrowband signal k using the Hilbert transform (HT) (see Methods). After this, the first and last 10 time steps were discarded to avoid border effects inherent to the HT, so that in the following T = 280. We next calculated the phase difference, Δφkl(t) = φk(t)–φl(t), for each pair of brain regions k and l, at each time step t. Across all pairs of signals and time steps, the probability density function (p.d.f.) of the pairwise phase differences, noted Pr(Δφ), is centred on zero (Fig. 1b). However, Pr(Δφ) is not constant, but evolves over time, going from a uniform distribution (i.e., the phases are independent) to a distribution that is densely concentrated around zero (i.e., high phase synchrony) (Fig. 1c, color plots).

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