Correction of respiratory artifacts in MRI head motion estimates
Damien A. Fair,
Oscar Miranda Dominguez,
Abraham Z. Snyder,
Eric A. Earl,
Andrew N. Van,
Jonathan M. Koller,
Rachel L. Klein,
Amy E. Mirro,
Jacqueline M. Hampton,
Timothy O. Laumann,
Deanna J. Greene,
Bradley L. Schlaggar,
Deanna M. Barch,
Joel T. Nigg,
Steven E. Petersen,
Sarah W. Feldstein-Ewing,
Bonnie J. Nagel,
Nico UF Dosenbach
Posted 07 Jun 2018
bioRxiv DOI: 10.1101/337360 (published DOI: 10.1016/j.neuroimage.2019.116400)
Posted 07 Jun 2018
Head motion represents one of the greatest technical obstacles for brain MRI. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, this estimation may be corrupted by factitious effects owing to main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and a comparison 'single-shot' dataset from Oregon Health & Science University. We show unequivocally that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with degraded quality of functional MRI. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects. Subsequently, we demonstrate that utilizing this filter improves post-processing data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.
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