Simulation of behavior scores from lesion maps

Function simulate behavioral scores based on the lesion load of specific brain areas. Used to run simulation studies.

simulateBehavior(lesions.list, parcellation, label = NA, mask = NA,
  errorWeight = 0.5, binaryCheck = FALSE, exponent = 1)

Arguments

lesions.list	list of lesions (antsImages) or vector of filenames.
parcellation	mask or parcellation image. If a parcellation is passed, lesion load will be computed for each different label (value) in the image. Zero and non-affected labels are not returned by default. The parcellation input can be an antsImage or a character vector pointing to a file.
label	(default=NA) if a parcellation scheme id being used, you can select which labels to simulate behaviors for (i.e., c(101,43) to simulate behavior for labels with value 101 and 43 only). If not set a simulation will be returned from each parcel.
mask	mask to restrict the count of lesioned voxels. It is not recommended to use a mask, because lesions should affect behavior as they are, without the user restricting the lesions to masks defined in post-processing.
errorWeight	(default=0.5) the amount of error to be added, i.e., 0.5 means half of the simulation will be error, the other half signal
binaryCheck	(default=FALSE) check to make sure all lesions are binary
exponent	power exponent to elevate behavior in order to increase non-linearity relationship with lesion load. 1 is default, and 3 is what Wang (2013) reported as lesion load relationship with behavior.

Value

List of objectas returned:

• behavload - a matrix of simulated behavioral scores. Each column shows simulation for a single parcel. Column names indicate the label number in the parcellation file.

• lesload - same as behavload, but indicates lesions loads of the simulated regions.

• lesbehavCorrelation - vector of Pearson correlations between lesion load and simulated scores.

• LesvolBehavCorrelation - vector of Pearson correlations between lesion size and simulated scores.

Examples

{
 if (FALSE) {
  lesydata = file.path(find.package('LESYMAP'),'extdata')
  parcellation = antsImageRead(
  Sys.glob(file.path(lesydata, 'template', 'Parcellation_403areas.nii.gz')))
  filenames = Sys.glob(file.path(lesydata, 'lesions', '*.nii.gz'))
  lesions = imageFileNames2ImageList(filenames)
  simBehavior = simulateBehavior(lesions.list = lesions, parcellation =
  parcellation, label = c(101,43))
 }
}