Less is more: balancing noise reduction and data retention in fMRI with projection scrubbing

Functional MRI (fMRI) data may be contaminated by artifacts arising from a myriad of sources, including subject head motion, respiration, heartbeat, scanner drift, and thermal noise. These artifacts 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 the accuracy and power of 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, among them the need to choose a threshold, a lack of generalizability to multi-band acquisitions, and high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods (e.g., DVARS) are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we use "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 find that ICA-based projection scrubbing yields more substantial improvements to functional connectivity validity and reliability than motion scrubbing, while excluding a fraction of the number of volumes or entire sessions. We find that while data-driven scrubbing more generally outperforms motion scrubbing, ICA projection scrubbing tends to be more beneficial than DVARS or other projections, illustrating the advantages of this data-driven, statistically principled approach for identifying contaminated volumes in fMRI.