Projection scrubbing: a more effective, data-driven fMRI denoising method
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Functional MRI (fMRI) data are subject to artifacts from a myriad of sources which can have negative consequences on the accuracy and power of statistical analyses. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts. Here we present "projection scrubbing", a new data-driven scrubbing method based on a statistical outlier detection framework. Projection scrubbing consists of two main steps: projection of the data onto directions likely to represent artifacts, and quantitative comparison of each volume's association with artifactual directions to identify volumes exhibiting artifacts. We assess the ability of projection scrubbing to improve the reliability and predictiveness of functional connectivity (FC) compared with two popular scrubbing methods: motion scrubbing, a measure of subject head displacement, and DVARS, another data-driven measure based on signal intensity change in the fMRI scan. We perform scrubbing in conjunction with regression-based denoising through CompCor, which we found to outperform alternative methods. Projection scrubbing and DVARS were both substantially more beneficial to FC reliability than motion scrubbing, illustrating the advantage of data-driven measures over head motion-based measures for identifying contaminated volumes. The benefit of scrubbing was strongest for connections between subcortical regions and cortical-subcortical connections. Scrubbing with any method did not have a noticeable effect on prediction accuracy of sex or total cognition, suggesting that the ultimate effect of scrubbing on downstream analysis depends on a number of factors specific to a given analysis. To promote the adoption of effective fMRI denoising techniques, all methods are implemented in a user-friendly, open-source R package that is compatible with NIFTI- and CIFTI-format data.
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