lmerVoxel function

Run a Linear Mixed Effects Model on all voxels of a NIfTI image within a mask.

Run a Linear Mixed Effects Model on all voxels of a NIfTI image within a mask.

This function is able to run a Linear Mixed Effect Model using the lmer() function. The analysis will run in all voxels in in the mask and will return parametric coefficients at each voxel The function relies on lmerTest to create p-values using the Satterthwaite Approximation.

lmerVoxel(image, mask, fourdOut = NULL, formula = NULL, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

  • image: Input image of type 'nifti' or vector of path(s) to images. If multiple paths, the script will call mergeNifti() and merge across time.
  • mask: Input mask of type 'nifti' or path to mask. Must be a binary mask
  • fourdOut: To be passed to mergeNifti, This is the path and file name without the suffix to save the fourd file. Default (NULL) means script won't write out 4D image.
  • formula: Must be a formula passed to lmer()
  • subjData: Dataframe containing all the covariates used for the analysis
  • mc.preschedule: Argument to be passed to mclapply, whether or not to preschedule the jobs. More info in parallel::mclapply
  • ncores: Number of cores to use
  • ...: Additional arguments passed to lmer()

Returns

returns list of models fitted to each voxel over the masked images passed to function.

Examples

## Not run:

image <- oro.nifti::nifti(img = array(1:1600, dim =c(4,4,4,25)))
mask <- oro.nifti::nifti(img = array(0:1, dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), id = rep(1:5,5))
fm1 <- "~ x + (1|id)"
models <- lmerVoxel(image, mask, formula = fm1, subjData = covs, ncores = 1, REML=T)
## End(Not run)
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