DVARS: DVARS
In fMRIscrub: Scrubbing and Other Data Cleaning Routines for fMRI

DVARS

R Documentation

DVARS

Computes the DSE decomposition and DVARS-related statistics. Based on code from github.com/asoroosh/DVARS .

DVARS(
  X,
  normalize = TRUE,
  cutoff_DPD = 5,
  cutoff_ZD = qnorm(1 - 0.05/nrow(as.matrix_ifti(X))),
  verbose = FALSE
)

`X`	a `T` by `N` numeric matrix representing an fMRI run. There should not be any missing data (`NA` or `NaN`).
`normalize`	Normalize the data? Default: `TRUE`. Normalization removes constant-zero voxels, scales by 100 / the median of the mean image, and then centers each voxel on its mean. To replicate Afyouni and Nichols' procedure for the HCP MPP data, since the HCP scans are already normalized to 10,000, just divide the data by 100 and center the voxels on their means: `Y <- Y/100; DVARS(t(Y - apply(Y, 1, mean)))` where `Y` is the `V` by `T` data matrix. Note that while voxel centering doesn't affect DVARS, it does affect DPD and ZD.
`cutoff_DPD, cutoff_ZD`	Numeric outlier cutoffs. Timepoints exceeding these cutoffs will be flagged as outliers.
`verbose`	Should occasional updates be printed? Default is `FALSE`.

A list with components

measure: A data.frame with T rows, each column being a different variant of DVARS.
measure_info: "DVARS"
outlier_cutoff: The outlier cutoff value(s).
outlier_flag: A logical data.frame with T rows, where TRUE indicates suspected outlier presence.