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Compute a mask for a subject.

Source: R/fsdir_abstraction_subject.R
subject.mask.Rd

Compute a binary vertex mask for the surface vertices of a subject. By defaults, the medial wall is masked.

Usage

subject.mask(
  subjects_dir,
  subject_id,
  hemi = "both",
  from_label = "cortex",
  surf_num_verts = "white",
  invert_mask = TRUE
)

Arguments

subjects_dir

string. The FreeSurfer SUBJECTS_DIR, i.e., a directory containing the data for all your subjects, each in a subdir named after the subject identifier.

subject_id

string. The subject identifier

hemi

string, one of 'lh', 'rh' or 'both'. The hemisphere name. Used to construct the names of the annotation and morphometry data files to be loaded.

from_label

string, the label file to use. Defaults to 'cortex', which will result in a mask of the medial wall versus cortex vertices.

surf_num_verts

string or integer. If an integer, interpreted as the number of vertices in the respective surface (lh or rh). If a character string, interpreted as a surface name, (e.g.,white or pial), and the respective surface will be loaded to determine the number of vertices in it. If parameter hemi is set to both and you supply the vertex count as an integer, this can be a vector of length 2 if the surfaces have different vertex counts (the first entry for lh, the second for rh).

invert_mask

logical, whether to invert the mask. E.g., when the mask is loaded from the cortex labels, if this is set to FALSE, the cortex would be masked (set to 0 in the final mask). If you want everything but the cortex to be masked (set to 0), you should set this to TRUE. Defaults to TRUE.

Value

the mask, a logical vector with the length of the vertices in the surface. If parameter hemi is set to both, a named list with entries lh and rh is returned, and the values of are the respective masks.

See also

Other label functions: apply.label.to.morphdata(), apply.labeldata.to.morphdata(), subject.lobes(), vis.labeldata.on.subject(), vis.subject.label()

Examples

if (FALSE) { # \dontrun{
   # Generate a binary mask of the medial wall. Wall vertices will
   #  be set to 0, cortex vertices will be set to 1.
   fsbrain::download_optional_data();
   subjects_dir = fsbrain::get_optional_data_filepath("subjects_dir");
   mask = subject.mask(subjects_dir, "subject1");
   # Print some information on the mask:
   #cat(sprintf("lh: %d verts, %d in cortex, %d medial wall.\n", length(mask$lh),
   # sum(mask$lh), (length(mask$lh)- sum(mask$lh))))
   # Output: lh: 149244 verts, 140891 in cortex, 8353 medial wall.
   # Now visualize the mask to illustrate that it is correct:
   vis.mask.on.subject(subjects_dir, "subject1", mask$lh, mask$rh);
} # }