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 Watts

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

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. • Respiratory perturbations of the main field inflate fMRI head motion estimates. • Breathing-related head motion artifacts compromise functional connectivity quality. • Notch filtering motion estimates (respiratory frequency band) improves data quality. • Motion estimate filtering can be achieved in real-time with FIRMM software.
Original languageEnglish (US)
Article number116400
Number of pages1
JournalNeuroImage
Volume208
DOIs
StatePublished - Mar 1 2020

Bibliographical note

Funding Information:
We would like to thank the ABCD MRI image acquisition working group, including BJ Casey and Jonathan Polimeni, for their work in organizing the group and developing the ABCD MRI acquisition protocols, respectively. We would also like to thank the other massive contributions to the ABCD effort by the other PIs and staff (see https://abcdstudy.org/ ). This work was supported by the National Institutes of Health (grants R01 MH096773 and K99/R00 MH091238 to D.A.F., R01 MH115357 to D.A.F, J.T.N., R01 MH086654 , J.T.N., U24 DA04112 to A.D., U01 DA041148 to D.A.F., S.W.F., B.J.N, R44 MH122066 to D.A.F., N.U.F.D., K23 NS088590 , K12 TR000448 to N.U.F.D., R00 HD074649 to M.D.T.), the Oregon Clinical and Translational Research Institute (D.A.F), the Bill & Melinda Gates Foundation (D.A.F), the Destafano Innovation Fund (D.A.F.), a OHSU Fellowship for Diversity and Inclusion in Research Program (O.M.-D.), a Tartar Trust Award (O.M.-D.), the OHSU Parkinson Center Pilot Grant Program (O.M.-D.) and a National Library of Medicine Postdoctoral Fellowship (E.F.), the Jacobs Foundation grant 2016121703 (N.U.F.D), the Child Neurology Foundation (N.U.F.D.); the McDonnell Center for Systems Neuroscience (N.U.F.D., B.L.S.); the Mallinckrodt Institute of Radiology grant 14-011 (N.U.F.D.); the Hope Center for Neurological Disorders (N.U.F.D., B.L.S.); and the Kiwanis Neuroscience Research Foundation (N.U.F.D., B.L.S.). Appendix A

Publisher Copyright:
© 2019 The Authors

Keywords

  • MOTION
  • FUNCTIONAL magnetic resonance imaging
  • NOTCH filters
  • MAGNETIC resonance imaging
  • NEURAL development

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Journal Article
  • Research Support, N.I.H., Extramural

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