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Functional magnetic resonance imaging (fMRI) data contain high levels ofnoise and artifacts. To avoid contamination of downstream analyses, fMRI-basedstudies must identify and remove these noise sources prior to statisticalanalysis. One common approach is the "scrubbing" of fMRI volumes that arethought to contain high levels of noise. However, existing scrubbing techniquesare based on ad hoc measures of signal change. We consider scrubbing viaoutlier detection, where volumes containing artifacts are consideredmultidimensional outliers. Robust multivariate outlier detection methods areproposed using robust distances (RDs), which are related to the Mahalanobisdistance. These RDs have a known distribution when the data are i.i.d. normal,and that distribution can be used to determine a threshold for outliers wherefMRI data violate these assumptions. Here, we develop a robust multivariateoutlier detection method that is applicable to non-normal data. The objectiveis to obtain threshold values to flag outlying volumes based on their RDs. Wepropose two threshold candidates that embark on the same two steps, but thechoice of which depends on a researcher's purpose. Our main steps are dimensionreduction and selection, robust univariate outlier imputation to get rid of theeffect of outliers on the distribution, and estimating an outlier thresholdbased on the upper quantile of the RD distribution without outliers. The firstthreshold candidate is an upper quantile of the empirical distribution of RDsobtained from the imputed data. The second threshold candidate calculates theupper quantile of the RD distribution that a nonparametric bootstrap uses toaccount for uncertainty in the empirical quantile. We compare our proposed fMRIscrubbing method to motion scrubbing, data-driven scrubbing, and restrictiveparametric multivariate outlier detection methods.

A robust multivariate, non-parametric outlier identification method for scrubbing in fMRI