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BOLD correlates of EEG topography reveal rapid resting-state network dynamics
Open accessAbstract
Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these maps with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These networks were spatially correlated with four of the resting-state networks (RSNs) that were found by the conventional fMRI group-level independent component analysis (ICA). These RSNs have previously been attributed to phonological processing, visual imagery, attention reorientation, and subjective interoceptive-autonomic processing. We found no EEG-correlate of the default mode network. Thus, the four typical microstates of the spontaneous EEG seem to represent the neurophysiological correlate of four of the RSNs and show that they are fluctuating much more rapidly than fMRI alone suggests.
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“The EEG microstates have been found to correlate with the fMRI signal in the resting state (173–175).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [174]
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Metadata
- Authors
- Britz, J., Van De Ville, D., & Michel, C. M.
- Affiliations
- University of Geneva (CH)
- Corresponding author
- Britz, Juliane
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 52
- Issue
- 4
- Pages
- 1162-1170
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2131216884
- Cited by
- 1,070
- References
- 72
- Citations by year
- 2026: 139 · 2025: 161 · 2024: 101 · 2023: 97 · 2022: 89 · 2021: 76 · 2020: 77 · 2019: 43 · 2018: 45 · 2017: 45 · 2016: 33 · 2015: 22 · 2014: 36 · 2013: 40 · 2012: 39
- FWCI
- 20.56
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Swiss National Science Foundation (32003B-118315, PP00P2-123438)
- Center for Biomedical Imaging (CIBM) of the Geneva and Lausanne Universities
- Leenaards and Louis-Jeantet Foundations
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- resting-state networks, EEG microstates, BOLD signal, EEG-fMRI correlation, hemodynamic response function, spontaneous EEG, functional connectivity, default mode network, independent component analysis, large-scale brain networks, network dynamics
- MeSH terms
- Action Potentials, Adult, Biological Clocks, Brain, Electroencephalography, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Rest
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