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Electrophysiological signatures of resting state networks in the human brain
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Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1–80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
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“The EEG correlates of resting state networks exhibit their distinct spectral signatures, characterizing the way by which neural oscillations support inter-regional interactions within networks (144, 159).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [159]
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Metadata
- Authors
- Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M.
- Affiliations
- University of Chieti-Pescara (IT)
- Institute of Advanced Biomedical Technologies and
- Washington University in St. Louis (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2007-08-07
- Volume
- 104
- Issue
- 32
- Pages
- 13170-13175
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC1941820
- OpenAlex
- W2063294564
- Cited by
- 1,938
- References
- 53
- Citations by year
- 2026: 54 · 2025: 68 · 2024: 87 · 2023: 72 · 2022: 110 · 2021: 140 · 2020: 117 · 2019: 118 · 2018: 112 · 2017: 127 · 2016: 117 · 2015: 107 · 2014: 117 · 2013: 140 · 2012: 112
- FWCI
- 38.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- Funders
- European Commission
- National Institutes of Health
- National Institute of Mental Health (R01 MH 71920-06, R01MH071920)
- National Institute of Neurological Disorders and Stroke (R01NS048013, NS48013)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- resting-state networks, intrinsic brain activity, EEG oscillations, neural oscillations, independent component analysis, large-scale brain networks, simultaneous EEG-fMRI
- MeSH terms
- Adult, Brain, Electroencephalography, Humans, Magnetic Resonance Imaging, Male, Oxygen
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