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August 1, 2007Proceedings of the National Academy of Sciences1,997 citationsOpen Access

Electrophysiological signatures of resting state networks in the human brain

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DMDante MantiniMPMauro Gianni PerrucciCGCosimo Del Gratta

Key Points

  • To determine the relationship between slow resting-state hemodynamic fluctuations and fast electrical oscillations across large-scale functional networks in the human brain.
  • Recorded simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during resting wakefulness.
  • Applied independent component analysis (ICA) to fMRI data to isolate resting state networks.
  • Correlated blood oxygenation level-dependent (BOLD) signal variations of identified networks with EEG power across delta, theta, alpha, beta, and gamma frequency bands (1–80 Hz).
  • Identified six widely distributed resting state networks via data-driven fMRI independent component analysis.
  • Found that each resting state network is characterized by a specific electrophysiological signature combining multiple distinct EEG frequency rhythms.
  • Demonstrated that integrating EEG and fMRI data provides a finer physiological fractionation of resting human brain networks.

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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Cite This Study

Mantini et al. (2007) studied this question.

synapsesocial.com/papers/69d77e1cf44a16d01ef31622https://doi.org/10.1073/pnas.0700668104
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