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.
Abstract:
Head motion during functional Magnetic Resonance Imaging acquisition can significantly contaminate the neural signal and introduce spurious, distance-dependent changes in signal correlations. This can heavily confound studies of development, aging, and disease. Previous approaches to suppress head motion artifacts have involved sequential regression of nuisance covariates, but this has been shown to reintroduce artifacts. We propose a new motion correction pipeline using an omnibus regression model that avoids this problem by simultaneously regressing out multiple artifacts using the best performing algorithms to estimate each artifact. We quantitatively evaluate its motion artifact suppression performance against sequential regression pipelines using a large heterogeneous dataset (n=151) which includes high-motion subjects and multiple disease phenotypes. The proposed concatenated regression pipeline significantly reduces the association between head motion and functional connectivity while significantly outperforming the traditional sequential regression pipelines in eliminating distance-dependent head motion artifacts.
Insights
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.
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.
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