Mitigating head motion artifact in functional connectivity MRI.

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TitleMitigating head motion artifact in functional connectivity MRI.
Publication TypeJournal Article
Year of Publication2018
AuthorsCiric, R, Rosen, AFG, Erus, G, Cieslak, M, Adebimpe, A, Cook, PA, Bassett, DS, Davatzikos, C, Wolf, DH, Satterthwaite, TD
JournalNat Protoc
Date Published2018 12
KeywordsArtifacts, Brain, Brain Mapping, Head, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Movement, Software

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.

Alternate JournalNat Protoc
PubMed ID30446748
Grant ListR01MH107703 / NH / NIH HHS / United States
R01MH112847 / NH / NIH HHS / United States
R21MH106799 / NH / NIH HHS / United States
R01EB022573 / NH / NIH HHS / United States
R01MH101111 / NH / NIH HHS / United States