gamVoxel function

Run a Generalized Additive Model on all voxels of a NIfTI image within a mask.

This function is able to run a Generalized Additive Model (GAM) using the mgcv package. The analysis will run in all voxels in in the mask and will return the model fit for each voxel.

gamVoxel(image, mask, fourdOut = NULL, formula, 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 gam()
• 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 gam()

Returns

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

Examples

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))
fm1 <- "~ s(x)"
models <- gamVoxel(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1)