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Related Experiment Video

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Voxel-wise information theoretic EEG-fMRI feature integration.

Dirk Ostwald1, Camillo Porcaro, Andrew P Bagshaw

  • 1School of Psychology, University of Birmingham, UK. dirk.ostwald@bccn-berlin.de

Neuroimage
|December 21, 2010
PubMed
Summary

This study introduces a new method using low-resolution electromagnetic tomography (LORETA) to analyze electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. While both methods show stimulus-related information, they do not reveal EEG-fMRI activity dependence.

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Area of Science:

  • Neuroscience
  • Biophysics
  • Information Theory

Background:

  • Previous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) integration studies relied on a priori constraints for data selection.
  • This limited the scope of analysis, for example, by using single EEG electrodes or small fMRI voxel clusters.

Purpose of the Study:

  • To develop and evaluate a more principled approach for EEG-fMRI data integration using information theoretic quantities (ITQs).
  • To assess the topographical informativeness of EEG and fMRI features with respect to visual stimuli and each other.
  • To investigate the impact of advanced EEG artifact removal techniques on information topography.

Main Methods:

  • Combined standard fMRI preprocessing with LORETA for whole-brain ITQ evaluation.
  • Applied the method to simultaneous EEG-fMRI data acquired during checkerboard stimulation.
  • Utilized Gaussian null model simulations and false-discovery rate for statistical assessment.
  • Compared the influence of independent component analysis and functional source separation for EEG artifact reduction.

Main Results:

  • Information theoretic effect size maps revealed topographically focused informativeness for both EEG and fMRI features concerning the stimulus.
  • No significant EEG-fMRI activity dependence was detected with the current feature selection.
  • Advanced EEG preprocessing improved stimulus-informativeness of features but did not change EEG-fMRI dependencies.

Conclusions:

  • The proposed LORETA-based approach enables comprehensive, whole-brain analysis of EEG-fMRI data for information theoretic investigations.
  • Both EEG and fMRI exhibit stimulus-specific information distributed across the brain.
  • Further research is needed to explore EEG-fMRI activity dependencies with potentially more advanced feature extraction or analysis techniques.