Cited by 1,938Functional Brain Connectivity StudiesNeural dynamics and brain functionAdvanced MRI Techniques and Applications

Electrophysiological signatures of resting state networks in the human brain

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Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., Corbetta, M.·2007·Proceedings of the National Academy of Sciences

Abstract

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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  • EEG correlates of resting-state networks have distinct spectral signatures that characterize how neural oscillations support inter-regional interactions within networks.

    “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).”

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  1. Hyva ¨rinen A (1999) IEEE T Neural Network 10:626-634.

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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
Published
2007-08-07
Volume
104
Issue
32
Pages
13170-13175
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
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
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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