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Updated: Apr 11, 2026
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI
Simon Schwab1, Thomas Koenig1, Yosuke Morishima2
1Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, and University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland.
Abstract:
Microstates (MS), the fingerprints of the momentarily and time-varying states of the brain derived from electroencephalography (EEG), are associated with the resting state networks (RSNs). However, using MS fluctuations along different EEG frequency bands to model the functional MRI (fMRI) signal has not been investigated so far, or elucidated the role of the thalamus as a fundamental gateway and a putative key structure in cortical functional networks. Therefore, in the current study, we used MS predictors in standard frequency bands to predict blood oxygenation level dependent (BOLD) signal fluctuations. We discovered that multivariate modeling of BOLD-fMRI using six EEG-MS classes in eight frequency bands strongly correlated with thalamic areas and large-scale cortical networks. Thalamic nuclei exhibited distinct patterns of correlations for individual MS that were associated with specific EEG frequency bands. Anterior and ventral thalamic nuclei were sensitive to the beta frequency band, medial nuclei were sensitive to both alpha and beta frequency bands, and posterior nuclei such as the pulvinar were sensitive to delta and theta frequency bands. These results demonstrate that EEG-MS informed fMRI can elucidate thalamic activity not directly observable by EEG, which may be highly relevant to understand the rapid formation of thalamocortical networks.
Insights
Brain microstates (MS) from EEG predict fMRI signals, revealing specific thalamic nuclei
Area of Science:
- Neuroscience
- Brain Imaging
- Electrophysiology
Background:
- Brain microstates (MS) derived from electroencephalography (EEG) are linked to resting-state networks (RSNs).
- The role of MS fluctuations across EEG frequency bands in modeling functional MRI (fMRI) signals, particularly concerning the thalamus, remains underexplored.
- The thalamus is a critical gateway and key structure in cortical functional networks.
Purpose of the Study:
- To investigate the predictive power of EEG-derived microstate (MS) fluctuations across different frequency bands for blood oxygenation level dependent (BOLD) fMRI signal.
- To elucidate the specific contributions of the thalamus and its nuclei in the context of rapid thalamocortical network formation.
- To explore the relationship between distinct MS classes, EEG frequency bands, and thalamic activity patterns.
Main Methods:
- Utilized multivariate modeling to predict BOLD-fMRI signals using six EEG-MS classes across eight frequency bands.
- Analyzed correlations between EEG-MS predictors and fMRI signals, focusing on thalamic nuclei and large-scale cortical networks.
- Investigated frequency-specific sensitivity of individual thalamic nuclei to distinct MS.
Main Results:
- Multivariate modeling of BOLD-fMRI using EEG-MS classes showed strong correlations with thalamic areas and cortical networks.
- Specific thalamic nuclei exhibited distinct correlation patterns with individual MS and EEG frequency bands.
- Anterior and ventral thalamic nuclei were sensitive to beta band activity; medial nuclei to alpha and beta; posterior nuclei (e.g., pulvinar) to delta and theta bands.
Conclusions:
- EEG-MS informed fMRI successfully models BOLD signal fluctuations and highlights thalamic involvement in brain networks.
- This approach can reveal thalamic activity patterns not directly observable via EEG alone.
- Findings are highly relevant for understanding the rapid formation and dynamics of thalamocortical networks.
