Description Usage Arguments Value Author(s) Examples
View source: R/simulateBehavior.R
Function simulate behavioral scores based on the lesion load of specific brain areas. Used to run simulation studies.
1 2  | simulateBehavior(lesions.list, parcellation, label = NA, mask = NA,
  errorWeight = 0.5, binaryCheck = FALSE, exponent = 1)
 | 
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.  | 
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.
Dorian Pustina
1 2 3 4 5 6 7 8 9 10 11 12  | {
 ## Not run: 
  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))
 
## End(Not run)
}
 | 
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