Improved motion correction for functional MRI using an omnibus regression model

TL;DR

This paper introduces a new omnibus regression-based motion correction method for fMRI that effectively reduces head motion artifacts and outperforms traditional sequential regression approaches, especially in high-motion and clinical datasets.

Contribution

The study presents a novel concatenated regression pipeline that simultaneously regresses multiple artifacts, improving motion artifact suppression in fMRI data.

Findings

Significantly reduces the correlation between head motion and functional connectivity.

Outperforms traditional sequential regression pipelines in artifact removal.

Effective in datasets with high motion and disease phenotypes.

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.