Reduction of Motion Artifacts in Functional Connectivity Resulting from Infrequent Large Motion

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Data Science

Background:

• Head motion is a significant challenge in functional magnetic resonance imaging (fMRI), particularly affecting functional connectivity estimation.
• Infants scanned during natural sleep exhibit infrequent but substantial head movements, leading to residual signal changes post-realignment.
• These motion-induced artifacts can severely distort functional connectivity measures.

Purpose of the Study:

• Introduce and evaluate JumpCor, a novel motion correction technique for fMRI.
• Compare JumpCor's efficacy against existing methods for reducing residual motion artifacts.
• Assess the impact of motion correction on the quality of functional connectivity estimates.

Main Methods:

• Compared JumpCor, motion parameter regression, and signal regression (white matter, CSF, global signals) using real and simulated fMRI data.
• Evaluated the reduction of motion-related signal changes caused by infrequent large movements.
• Assessed improvements in functional connectivity estimates when JumpCor was integrated into standard preprocessing pipelines.

Main Results:

• JumpCor and motion parameter regression significantly reduced motion-related signal changes from infrequent large movements.
• JumpCor demonstrated superior artifact reduction and enhanced functional connectivity estimation quality compared to other methods.
• The technique proved effective even when combined with typical fMRI preprocessing steps.

Conclusions:

• JumpCor offers an effective solution for correcting motion artifacts caused by occasional large head movements in fMRI.
• The method is particularly beneficial for studies involving populations prone to such motion, like sleeping infants.
• Implementing JumpCor can minimize data loss and improve the reliability of functional connectivity findings in challenging datasets.