Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity.
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 dy...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4333573?pdf=render |
| _version_ | 1818700573494476800 |
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| author | Adrián Ponce-Alvarez Gustavo Deco Patric Hagmann Gian Luca Romani Dante Mantini Maurizio Corbetta |
| author_facet | Adrián Ponce-Alvarez Gustavo Deco Patric Hagmann Gian Luca Romani Dante Mantini Maurizio Corbetta |
| author_sort | Adrián Ponce-Alvarez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T15:07:06Z |
| format | Article |
| id | doaj.art-a05e923397264cb8a58cf8d4bf19443d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-17T15:07:06Z |
| publishDate | 2015-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-a05e923397264cb8a58cf8d4bf19443d2022-12-21T21:43:46ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-02-01112e100410010.1371/journal.pcbi.1004100Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity.Adrián Ponce-AlvarezGustavo DecoPatric HagmannGian Luca RomaniDante MantiniMaurizio CorbettaSpatial 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.http://europepmc.org/articles/PMC4333573?pdf=render |
| spellingShingle | Adrián Ponce-Alvarez Gustavo Deco Patric Hagmann Gian Luca Romani Dante Mantini Maurizio Corbetta Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLoS Computational Biology |
| title | Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. |
| title_full | Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. |
| title_fullStr | Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. |
| title_full_unstemmed | Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. |
| title_short | Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. |
| title_sort | resting state temporal synchronization networks emerge from connectivity topology and heterogeneity |
| url | http://europepmc.org/articles/PMC4333573?pdf=render |
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