Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks
Abstract
The brain is active even in the absence of explicit input or output as demonstrated from electrophysiological as well as imaging studies. Using a combined approach we measured spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal along with electroencephalography (EEG) in eleven healthy subjects during relaxed wakefulness (eyes closed). In contrast to other studies which used the EEG frequency information to guide the functional MRI (fMRI) analysis, we opted for transient EEG events, which identify and quantify brain electric microstates as time epochs with quasi-stable field topography. We then used this microstate information as regressors for the BOLD fluctuations. Single trial EEGs were segmented with a specific module of the LORETA (low resolution electromagnetic tomography) software package in which microstates are represented as normalized vectors constituted by scalp electric potentials, i.e., the related 3-dimensional distribution of cortical current density in the brain. Using the occurrence and the duration of each microstate, we modeled the hemodynamic response function (HRF) which revealed BOLD activation in all subjects. The BOLD activation patterns resembled well known resting-state networks (RSNs) such as the default mode network. Furthermore we "cross validated" the data performing a BOLD independent component analysis (ICA) and computing the correlation between each ICs and the EEG microstates across all subjects. This study shows for the first time that the information contained within EEG microstates on a millisecond timescale is able to elicit BOLD activation patterns consistent with well known RSNs, opening new avenues for multimodal imaging data processing.
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EEG microstates correlate with resting-state fMRI signal.
“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 [173]
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- Authors
- Musso, F., Brinkmeyer, J., Mobascher, A., Warbrick, T., & Winterer, G.
- Affiliations
- Heinrich Heine University Düsseldorf (DE)
- Forschungszentrum Jülich (DE)
- Johannes Gutenberg University Mainz (DE)
- Corresponding author
- Musso, F.
- Brinkmeyer, J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 52
- Issue
- 4
- Pages
- 1149-1161
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2044461825
- Cited by
- 387
- References
- 75
- Citations by year
- 2026: 17 · 2025: 29 · 2024: 16 · 2023: 37 · 2022: 28 · 2021: 30 · 2020: 31 · 2019: 20 · 2018: 22 · 2017: 31 · 2016: 20 · 2015: 14 · 2014: 18 · 2013: 20 · 2012: 33
- FWCI
- 13.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- 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
- spontaneous brain activity, EEG microstates, resting-state networks, default mode network, BOLD signal fluctuations, EEG-fMRI integration, hemodynamic response function, LORETA
- MeSH terms
- Action Potentials, Adult, Biological Clocks, Brain, Electroencephalography, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Rest
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