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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Understanding gradient artefacts in simultaneous EEG/fMRI.

Winston X Yan1, Karen J Mullinger, Matt J Brookes

  • 1Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK.

Neuroimage
|April 23, 2009
PubMed
Summary
This summary is machine-generated.

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Researchers investigated spatial variations in gradient artifacts during simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Understanding these spatial patterns can improve artifact correction for higher quality neuroimaging data.

Area of Science:

  • Neuroimaging
  • Biophysics
  • Signal Processing

Background:

  • Concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recording is crucial for advanced neuroscience research.
  • Large artifacts in EEG data, generated by fMRI's time-varying magnetic field gradients, compromise data quality.
  • Previous research focused on temporal variations of these gradient artifacts, neglecting their spatial characteristics across EEG leads.

Purpose of the Study:

  • To investigate the spatial characteristics of gradient artifacts during simultaneous EEG/fMRI.
  • To understand the underlying mechanisms generating these spatial artifact variations.
  • To develop physical models for predicting and correcting gradient artifacts based on spatial patterns.

Main Methods:

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  • Developed novel analytic expressions from electromagnetic theory for artifact voltage, considering head orientation.
  • Validated models using numerical simulations with realistic EEG wirepaths and experimental measurements on phantoms and human heads.
  • Assessed spatial patterns of artifact voltage across various head positions and gradient field strengths.

    • Main Results:

    • Accurate reproduction of experimentally measured spatial artifact patterns was achieved through numerical simulations (correlation coefficients up to 0.98).
    • Identified that adjusting the subject's axial position can decrease artifact voltages for both longitudinal and transverse gradients.
    • Demonstrated the ability to model gradient artifacts for any given head orientation.

    Conclusions:

    • The study provides an improved understanding of the spatial characteristics and generation mechanisms of fMRI gradient artifacts in EEG.
    • Accurate spatial artifact modeling facilitates the development of advanced correction algorithms incorporating motion tracking and spatial filtering.
    • These advancements promise greater fidelity in simultaneous EEG/fMRI data acquisition and analysis.