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fMRIscrub is a collection of routines for data-driven scrubbing (projection scrubbing and DVARS), motion scrubbing, and other fMRI denoising strategies such as anatomical CompCor, detrending, and nuisance regression. The data-driven scrubbing methods are also applicable to other outlier detection tasks involving high-dimensional data.


You can install the development version of fMRIscrub from GitHub with:

# install.packages("devtools")

Quick start guide

s_Dat1 <- scrub(Dat1)
Dat1_cleaned <- Dat1[!s_Dat1$outlier_flag,]


Two scans from the ABIDE I are included in fMRIscrub: Dat1 has many artifacts whereas Dat2 has few visible artifacts. Both are vectorized sagittal slices stored as numeric matrices. They are loaded into the environment upon loading the package.

We acknowledge the corresponding funding for the ABIDE I data:

Primary support for the work by Adriana Di Martino was provided by the (NIMH K23MH087770) and the Leon Levy Foundation. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to MPM ( NIMH R03MH096321).


See this link to view the tutorial vignette.


If using projection scrubbing, you can cite our pre-print at