Correction of respiratory artifacts in MRI head motion estimates

Damien A. Fair*, Oscar Miranda-Dominguez, Abraham Z. Snyder, Anders Perrone, Eric A. Earl, Andrew N. Van, Jonathan M. Koller, Eric Feczko, M. Dylan Tisdall, Andre van der Kouwe, Rachel L. Klein, Amy E. Mirro, Jacqueline M. Hampton, Babatunde Adeyemo, Timothy O. Laumann, Caterina Gratton, Deanna J. Greene, Bradley L. Schlaggar, Donald J. Hagler, Richard WattsHugh Garavan, Deanna M. Barch, Joel T. Nigg, Steven E. Petersen, Anders M. Dale, Sarah W. Feldstein-Ewing, Bonnie J. Nagel, Nico U.F. Dosenbach

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic 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 comparison ‘single-shot’ datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

Original languageEnglish (US)
Article number116400
StatePublished - Mar 2020

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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