PT  - JOURNAL ARTICLE
AU  - Damien A. Fair
AU  - Oscar Miranda-Dominguez
AU  - Abraham Z. Snyder
AU  - Anders Perrone
AU  - Eric A. Earl
AU  - Andrew N. Van
AU  - Jonathan M. Koller
AU  - Eric Feczko
AU  - Rachel L. Klein
AU  - Amy E. Mirro
AU  - Jacqueline M. Hampton
AU  - Babatunde Adeyemo
AU  - Timothy O. Laumann
AU  - Caterina Gratton
AU  - Deanna J. Greene
AU  - Bradley L. Schlaggar
AU  - Don Hagler
AU  - Richard Watts
AU  - Hugh Garavan
AU  - Deanna M. Barch
AU  - Joel T. Nigg
AU  - Steven E. Petersen
AU  - Anders Dale
AU  - Sarah W. Feldstein-Ewing
AU  - Bonnie J. Nagel
AU  - Nico U.F. Dosenbach
TI  - Correction of respiratory artifacts in MRI head motion estimates
AID  - 10.1101/337360
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 337360
4099  - http://biorxiv.org/content/early/2018/06/07/337360.short
4100  - http://biorxiv.org/content/early/2018/06/07/337360.full
AB  - 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.