  Artifacts in functional MRI (fMRI) data cause deviations from common
distributional assumptions, introduce spatial and temporal outliers, and reduce
the signal-to-noise ratio of the data -- all of which can have negative
consequences for downstream statistical analysis. Scrubbing is a technique for
excluding fMRI volumes thought to be contaminated by artifacts and generally
comes in two flavors. Motion scrubbing based on subject head motion-derived
measures is popular but suffers from a number of drawbacks, especially high
rates of censoring of individual volumes and entire subjects. Alternatively,
data-driven scrubbing methods like DVARS are based on observed noise in the
processed fMRI timeseries and may avoid some of these issues. Here we propose
"projection scrubbing", a novel data-driven scrubbing method based on a
statistical outlier detection framework and strategic dimension reduction,
including independent component analysis (ICA), to isolate artifactual
variation. We undertake a comprehensive comparison of motion scrubbing with
data-driven projection scrubbing and DVARS.
  We argue that an appropriate metric for the success of scrubbing is maximal
data retention subject to reasonable performance on typical benchmarks of
functional connectivity. We find that stringent motion scrubbing yields
worsened validity, worsened reliability, and produced small improvements to
fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater
improvements to fingerprinting while not generally worsening validity or
reliability. Importantly, however, data-driven scrubbing excludes a fraction of
the number of volumes or entire sessions compared to motion scrubbing. The
ability of data-driven fMRI scrubbing to improve data retention without
negatively impacting the quality of downstream analysis has major implications
for sample sizes in population neuroscience research.
