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Correcting for Superficial Bias in 7T Gradient Echo fMRI.

Pei Huang1,2, Marta M Correia2, Catarina Rua3

  • 1Singapore Institute for Clinical Sciences, A∗STAR, Singapore, Singapore.

Frontiers in Neuroscience
|October 11, 2021
PubMed
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Ultra-high field fMRI enables laminar analysis, but superficial bias in the blood oxygenation level dependent (BOLD) signal confounds results. Deming regression and ROI ratio methods robustly correct this bias, crucial for accurate findings.

Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)
  • Human Brain Anatomy

Background:

  • Submillimeter ultra-high field fMRI allows for detailed cortical layer activation analysis.
  • Gradient echo (GE) fMRI's blood oxygenation level dependent (BOLD) signal is biased towards superficial cortical layers, complicating laminar investigations.

Purpose of the Study:

  • To compare existing and novel methods for correcting superficial bias in 7 Tesla (7T) fMRI data.
  • To evaluate the effectiveness of these correction methods in computational simulations and a pilot human dataset.

Main Methods:

  • Computational simulations using 7T fMRI data from a visual attention paradigm.
  • Application of univariate and multivariate bias correction methods, including ROI ratio and Deming regression.
Keywords:
7T GE-fMRIDeming regressioncomputational modelingfMRI methodssuperficial bias correction

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  • Testing on a pilot dataset of human 7T fMRI data.

    • Main Results:

    • Simulations identified the ROI ratio and Deming regression as the most robust methods for correcting superficial bias.
    • Deming regression offers an advantage by not requiring differences in mean activation across voxels within an ROI.
    • Application to pilot data showed differing layer profiles with various attention metrics, but Deming regression and ROI ratio obscured these differences, suggesting successful bias correction.

    Conclusions:

    • Accurate correction of superficial bias is essential to prevent erroneous conclusions in laminar analyses of GE fMRI data.
    • The findings are supported by both simulation results and pilot human 7T fMRI data analysis.
    • Deming regression and ROI ratio methods provide robust bias correction for laminar fMRI studies.