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Related Experiment Video

Updated: Apr 11, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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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.

Neuroimage
|June 9, 2015
PubMed
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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.
Keywords:
EEG microstatesEEG topographyResting-stateThalamusfMRI

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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.