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Improved motion correction for functional MRI using an omnibus regression model.

Vyom Raval1,2, Kevin P Nguyen1, Cooper Mellema1

  • 1The University of Texas Southwestern Medical Center.

Proceedings. IEEE International Symposium on Biomedical Imaging
|March 26, 2021
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Summary
This summary is machine-generated.

This study introduces a new pipeline to correct head motion artifacts in functional Magnetic Resonance Imaging (fMRI) data. The novel approach significantly reduces motion-related noise, improving the accuracy of brain connectivity studies.

Keywords:
Parkinson’s Diseaseconcatenated regressionfMRIhead motionnoise suppression

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Biomedical Engineering

Background:

  • Head motion during functional Magnetic Resonance Imaging (fMRI) acquisition introduces significant artifacts, contaminating neural signals and altering functional connectivity.
  • These motion-induced artifacts can confound findings in studies of brain development, aging, and neurological diseases.
  • Traditional sequential regression methods for artifact suppression can inadvertently reintroduce artifacts.

Purpose of the Study:

  • To develop and evaluate a novel motion correction pipeline for fMRI data.
  • To address the limitations of sequential regression by proposing a simultaneous artifact removal approach.
  • To quantitatively assess the performance of the new pipeline against existing methods in reducing motion artifacts and preserving neural signal integrity.

Main Methods:

  • A new motion correction pipeline utilizing an omnibus regression model was developed.
  • This pipeline simultaneously regresses out multiple artifacts using optimized algorithms for artifact estimation.
  • The proposed concatenated regression pipeline was evaluated against traditional sequential regression methods on a large, heterogeneous dataset (n=151) including high-motion subjects and diverse disease phenotypes.

Main Results:

  • The proposed concatenated regression pipeline significantly reduced the association between head motion and functional connectivity.
  • The new pipeline demonstrated superior performance compared to traditional sequential regression pipelines in eliminating distance-dependent head motion artifacts.
  • Quantitative evaluation confirmed the effectiveness of the omnibus regression model in motion artifact suppression.

Conclusions:

  • The developed concatenated regression pipeline offers a robust solution for mitigating head motion artifacts in fMRI.
  • This improved motion correction method enhances the reliability of functional connectivity analyses, particularly in vulnerable populations.
  • The findings suggest a significant advancement in preprocessing techniques for neuroimaging studies affected by head motion.