Mitigating head motion artifact in functional connectivity MRI

Abstract

Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.

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Fig. 1: Workflow for motion correction of functional connectivity MRI data.
Fig. 2: Summary of subject-level performance diagnostics and anticipated results (Steps 28–33).
Fig. 3: Summary of group-level performance diagnostics and anticipated results (Steps 34–36).

Data availability

Sample data and results for testing the denoising and benchmarking protocol are available publicly at FigShare under the MIT license (https://doi.org/10.6084/m9.figshare.6225968.v1).

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Acknowledgements

This work was supported by grants from the National Institutes of Health: R01MH107703 (T.D.S.), R01MH112847 (T.D.S.), R21MH106799 (D.S.B. and T.D.S.), R01EB022573 (C.D.), and R01MH101111 (D.H.W.); and the Lifespan Brain Institute at Penn/CHOP. D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory through contract number W911NF1020022, the Army Research Office through contract numbers W911NF1410679 and W911NF1610474, and the National Institute of Health (grants R01DC00920911, R01HD086888, R01MH107235, R01MH109520, and R01NS099348).The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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T.D.S., D.H.W., and R.C. developed and designed the protocol with numerous contributions from the functional neuroimaging community. R.C. developed the software implementation of the protocol. R.C., A.F.G.R., M.C., and A.A. developed the associated software libraries. M.C. and A.A. built Docker and Singularity containers for the associated software libraries. G.E. reviewed and tested the code and implemented the software on the Image Processing Portal. G.E., P.A.C., D.S.B., and C.D. provided consultation and guidance for methodological implementation and interpretation of results. R.C. and T.D.S. wrote the manuscript. R.C. prepared the figures and tables. All authors reviewed and revised the manuscript.

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Correspondence to Theodore D. Satterthwaite.

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Key references using this protocol

Satterthwaite, T. D. et al. Neuroimage 64, 240–256 (2013): https://doi.org/10.1016/j.neuroimage.2012.08.052

Ciric, R. et al. Neuroimage 154, 174–187 (2017): https://doi.org/10.1016/j.neuroimage.2017.03.020

Satterthwaite, T. D. et al. Neuroimage 83, 45–57 (2013): https://doi.org/10.1016/j.neuroimage.2013.06.045

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Ciric, R., Rosen, A.F.G., Erus, G. et al. Mitigating head motion artifact in functional connectivity MRI. Nat Protoc 13, 2801–2826 (2018). https://doi.org/10.1038/s41596-018-0065-y

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