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Interactions between different EEG frequency bands and their effect on alpha-fMRI correlations.

J C de Munck1, S I Gonçalves, R Mammoliti

  • 1Brain Imaging Section-Department of Physics and Medical Technology, VU University Medical Centre, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. jc.demunck@vumc.nl

Neuroimage
|April 21, 2009
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Summary

EEG/fMRI studies often assume alpha power reflects BOLD signals. This study reveals other EEG frequencies significantly influence this correlation, necessitating multi-frequency models for accurate analysis.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are crucial for understanding brain activity.
  • Correlation studies commonly model fMRI's Blood-Oxygen-Level-Dependent (BOLD) signal as a filtered version of EEG alpha power.

Purpose of the Study:

  • To investigate whether EEG frequency components beyond alpha impact the correlation between EEG and BOLD signals.
  • To compare statistical parametric maps (SPMs) generated using different filter models and hemodynamic response functions (HRFs).

Main Methods:

  • Simultaneous EEG/fMRI recording during a 30-minute resting state in 15 healthy young adults.
  • Extraction of power variations from delta, theta, alpha, beta, and gamma EEG bands.
  • Application of three filter models using standard or free HRFs with full EEG spectral bandwidth in a general linear model.

Main Results:

  • SPMs for significant EEG frequency bands, particularly beta, closely resembled those of the alpha rhythm.
  • Including other EEG frequency bands as confounders significantly altered the fMRI-BOLD-alpha correlation SPMs, especially with data-driven HRFs.
  • Unconstrained HRFs for nuisance frequencies drastically reduced the model's statistical power, preventing correlation detection.

Conclusions:

  • Power fluctuations across different EEG frequency bands are highly correlated.
  • A multi-frequency analysis model is essential for accurately extracting SPMs of interest from EEG/fMRI data.
  • Standard models assuming only alpha power may misattribute BOLD signal changes.