Fast transient networks in spontaneous human brain activity
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- Adam P Baker
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
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- Matthew J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
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- Iead A Rezek
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
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- Stephen M Smith
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
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- Timothy Behrens
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, Oxford University, Oxford, United Kingdom
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- Penny J Probert Smith
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
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- Mark Woolrich
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
Description
<jats:p>To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.</jats:p>
Journal
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- eLife
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eLife 3 e01867-, 2014-03-25
eLife Sciences Publications, Ltd
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Details 詳細情報について
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- CRID
- 1360574093681736064
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- ISSN
- 2050084X
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- Data Source
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- Crossref