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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

Chung-Ki Wong1, Vadim Zotev1, Masaya Misaki1

  • 1Laureate Institute for Brain Research, Tulsa, OK, USA.

Neuroimage
|January 31, 2016
PubMed
Summary

An automated method, aE-REMCOR, significantly reduces head motion artifacts in electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data. This improves data quality for large-scale studies by enhancing signal-to-noise ratio and motion correction efficiency.

Keywords:
E-REMCOREEGEEG-fMRIICAMotion artifactsMotion correctionfMRI

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Head motion during functional magnetic resonance imaging (fMRI) degrades data quality and introduces artifacts, complicating result interpretation.
  • Simultaneous electroencephalography (EEG) recordings offer high-temporal-resolution data on brain activity and head movements.
  • Existing EEG-assisted retrospective motion correction (E-REMCOR) methods require manual identification of motion-related components.

Purpose of the Study:

  • To develop and validate an automated implementation of E-REMCOR (aE-REMCOR) for large-scale EEG-fMRI studies.
  • To streamline the motion correction process by automating EEG data preprocessing, independent component analysis (ICA), and motion-related component identification.

Main Methods:

  • Developed the aE-REMCOR algorithm in MATLAB for automated EEG preprocessing and ICA.
  • Applied aE-REMCOR to 305 fMRI datasets from 16 subjects undergoing simultaneous EEG-fMRI.
  • Evaluated performance based on temporal signal-to-noise ratio (TSNR) improvement and motion parameter spike reduction.

Main Results:

  • aE-REMCOR substantially reduced head motion artifacts in fMRI data.
  • Average TSNR improvement reached 27%, with top values exceeding 55% for significant motion.
  • Average correction efficiency was 18%, with a maximum of 71%.

Conclusions:

  • aE-REMCOR provides a convenient and efficient solution for improving fMRI motion correction in large clinical EEG-fMRI studies.
  • The automated approach facilitates the application of E-REMCOR, enhancing data reliability.
  • Utilizing aE-REMCOR reduced motion-induced errors in resting-state default mode network connectivity analysis.