Scrubbing and Other Data Cleaning Routines for fMRI
Artifact images
fMRI data for scrub and CompCor
DVARS
Robust empirical rule
Framewise Displacement
Flags to nuisance spikes
fMRIscrub: fMRI scrubbing and other data cleaning routines
Which components have high kurtosis?
Leverage
noise parameters for CompCor
Plot a "scrub_DVARS" object
Plot a "scrub_FD" object
Plot a "scrub_projection" object
Plot a "scrub_projection_multi" object
Plot scrubbing results
Get internal name of this projection
Projection scrubbing
Convert "pscrub_multi" to "pscrub"
Compare projection scrubbing measures with pscrub_multi
pscrub
Impute outliers for robust distance
Robust distance calculation
Univariate outlier detection for robust distance
Stabilize the center and scale of a timeseries robustly
Robust linear model on DCT bases
Robust distance scrubbing
Data-driven scrubbing
"scrub" plot sub-function
Scrub fMRI data in CIFTI format
Estimate SD robustly using the half IQR
Robust outlier detection based on SHASH distribution
SHASH to normal data transformation
Summarize a "scrub_DVARS" object
Summarize a "scrub_FD" object
Summarize a "scrub_projection" object
Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.