Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI.

Autor: Raval V; University of Texas at Dallas, Richardson, TX, USA.; University of Washington, Seattle, WA, USA., Nguyen KP; University of Texas Southwestern Medical Center, Dallas, TX, USA., Pinho M; University of Texas Southwestern Medical Center, Dallas, TX, USA., Dewey RB Jr; University of Texas Southwestern Medical Center, Dallas, TX, USA., Trivedi M; University of Texas Southwestern Medical Center, Dallas, TX, USA., Montillo AA; University of Texas at Dallas, Richardson, TX, USA. Albert.montillo@utsouthwestern.edu.; University of Texas Southwestern Medical Center, Dallas, TX, USA. Albert.montillo@utsouthwestern.edu.
Jazyk: angličtina
Zdroj: Neuroinformatics [Neuroinformatics] 2022 Oct; Vol. 20 (4), pp. 879-896. Date of Electronic Publication: 2022 Mar 15.
DOI: 10.1007/s12021-022-09565-8
Abstrakt: In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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