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

doi:10.1016/j.neuroimage.2019.116400

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 journal › Article › peer-review

85 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 language	English (US)
Article number	116400
Number of pages	1
Journal	NeuroImage
Volume	208
DOIs	https://doi.org/10.1016/j.neuroimage.2019.116400
State	Published - 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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10.1016/j.neuroimage.2019.116400

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Fair, D. A., Miranda-Dominguez, O., Snyder, A. Z., Perrone, A., Earl, E. A., Van, A. N., Koller, J. M., Feczko, E., Tisdall, M. D., van der Kouwe, A., Klein, R. L., Mirro, A. E., Hampton, J. M., Adeyemo, B., Laumann, T. O., Gratton, C., Greene, D. J., Schlaggar, B. L., Hagler, D. J., & Watts, R. (2020). Correction of respiratory artifacts in MRI head motion estimates. NeuroImage, 208, Article 116400. https://doi.org/10.1016/j.neuroimage.2019.116400

Fair, DA , Miranda-Dominguez, O, Snyder, AZ, Perrone, A, Earl, EA, Van, AN, Koller, JM, Feczko, E, Tisdall, MD, van der Kouwe, A, Klein, RL, Mirro, AE, Hampton, JM, Adeyemo, B, Laumann, TO, Gratton, C, Greene, DJ, Schlaggar, BL, Hagler, DJ & Watts, R 2020, 'Correction of respiratory artifacts in MRI head motion estimates.', NeuroImage, vol. 208, 116400. https://doi.org/10.1016/j.neuroimage.2019.116400

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title = "Correction of respiratory artifacts in MRI head motion estimates.",

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.",

keywords = "MOTION, FUNCTIONAL magnetic resonance imaging, NOTCH filters, MAGNETIC resonance imaging, NEURAL development",

author = "Fair, {Damien A.} and Oscar Miranda-Dominguez and Snyder, {Abraham Z.} and Anders Perrone and Earl, {Eric A.} and Van, {Andrew N.} and Koller, {Jonathan M.} and Eric Feczko and Tisdall, {M. Dylan} and {van der Kouwe}, Andre and Klein, {Rachel L.} and Mirro, {Amy E.} and Hampton, {Jacqueline M.} and Babatunde Adeyemo and Laumann, {Timothy O.} and Caterina Gratton and Greene, {Deanna J.} and Schlaggar, {Bradley L.} and Hagler, {Donald J.} and Richard Watts",

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: {\textcopyright} 2019 The Authors",

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doi = "10.1016/j.neuroimage.2019.116400",

language = "English (US)",

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AU - Fair, Damien A.

AU - Miranda-Dominguez, Oscar

AU - Snyder, Abraham Z.

AU - Perrone, Anders

AU - Earl, Eric A.

AU - Van, Andrew N.

AU - Koller, Jonathan M.

AU - Feczko, Eric

AU - Tisdall, M. Dylan

AU - van der Kouwe, Andre

AU - Klein, Rachel L.

AU - Mirro, Amy E.

AU - Hampton, Jacqueline M.

AU - Adeyemo, Babatunde

AU - Laumann, Timothy O.

AU - Gratton, Caterina

AU - Greene, Deanna J.

AU - Schlaggar, Bradley L.

AU - Hagler, Donald J.

AU - Watts, Richard

N1 - 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

PY - 2020/3/1

Y1 - 2020/3/1

N2 - 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.

AB - 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.

KW - MOTION

KW - FUNCTIONAL magnetic resonance imaging

KW - NOTCH filters

KW - MAGNETIC resonance imaging

KW - NEURAL development

U2 - 10.1016/j.neuroimage.2019.116400

DO - 10.1016/j.neuroimage.2019.116400

M3 - Article

C2 - 31778819

SN - 1053-8119

VL - 208

JO - NeuroImage

JF - NeuroImage

M1 - 116400

ER -

Correction of respiratory artifacts in MRI head motion estimates.

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

Publisher link

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