Visualize a vertex mask on the surface of a subject.

A mask is a logical vector that contains one value per vertex. You can create it manually, or use functions like mask.from.labeldata.for.hemi to create and modify it. Check the example for this function.

Usage

vis.mask.on.subject(
  subjects_dir,
  vis_subject_id,
  mask_lh,
  mask_rh,
  surface = "white",
  views = c("t4"),
  rgloptions = rglo(),
  rglactions = list(),
  draw_colorbar = FALSE,
  makecmap_options = list(colFn = label.colFn.inv),
  style = "default"
)

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.

vis_subject_id

string. The subject identifier from which to obtain the surface for data visualization. Example: 'fsaverage'.

mask_lh

logical vector or NULL, the mask to visualize on the left hemisphere surface. Must have the same length as the lh surface of the vis_subject_id has vertices. If NULL, this surface will not be rendered. Only one of mask_lh or mask_rh is allowed to be NULL.

mask_rh

logical vector or NULL, the mask to visualize on the right hemisphere surface. Must have the same length as the rh surface of the vis_subject_id has vertices. If NULL, this surface will not be rendered. Only one of mask_lh or mask_rh is allowed to be NULL.

surface

string. The display surface. E.g., "white", "pial", or "inflated". Defaults to "white".

views

list of strings. Valid entries include: 'si': single interactive view. 't4': tiled view showing the brain from 4 angles. 't9': tiled view showing the brain from 9 angles.

rgloptions

option list passed to par3d. Example: rgloptions = list("windowRect"=c(50,50,1000,1000)).

rglactions

named list. A list in which the names are from a set of pre-defined actions. The values can be used to specify parameters for the action. The following example clips outliers in the data before plotting and writes a screenshot in PNG format: rglactions = list("snapshot_png"="~/fsbrain.png", "clip_data"=c(0.05, 0.95)). See rglactions.

draw_colorbar

logical or one of the character strings 'vertical' or 'horizontal', whether to draw a colorbar. Notice: the colorbar is drawn to a separate subplot, and this only works if there is enough space for it, i.e., the plot resolution must be high enough. You may have to increase the plot size for the colorbar to show up, see the vignette for instructions. Defaults to FALSE. See coloredmesh.plot.colorbar.separate for an alternative.

makecmap_options

named list of parameters to pass to makecmap. Must not include the unnamed first parameter, which is derived from 'measure'. Should include at least a colormap function as name 'colFn'.

style

character string, a rendering style, e.g., 'default', 'shiny' or 'semitransparent'.

Value

list of coloredmeshes. The coloredmeshes used for the visualization.

Note

Drawing a colorbar for label data makes limited sense, use a legend instead. The colorbar can give a rough overview of the relative number of label and non-label vertices though, so it is possible to request one.

See also

Other mask functions: coloredmesh.from.mask(), mask.from.labeldata.for.hemi()

Other visualization functions: highlight.vertices.on.subject(), highlight.vertices.on.subject.spheres(), vis.color.on.subject(), vis.data.on.fsaverage(), vis.data.on.subject(), vis.labeldata.on.subject(), vis.region.values.on.subject(), vis.rglwidget(), vis.subject.annot(), vis.subject.label(), vis.subject.morph.native(), vis.subject.morph.standard(), vis.subject.pre(), vis.symmetric.data.on.subject(), vis.volume.on.surface(), vislayout.from.coloredmeshes()

Examples

if (FALSE) { # \dontrun{
   fsbrain::download_optional_data();

  # Define the data to use:
  subjects_dir = fsbrain::get_optional_data_filepath("subjects_dir");
  subject_id = 'subject1';
  surface = 'white';
  hemi = 'both';
  atlas = 'aparc';
  region = 'bankssts';

  # Create a mask from a region of an annotation:
  lh_annot = subject.annot(subjects_dir, subject_id, 'lh', atlas);
  rh_annot = subject.annot(subjects_dir, subject_id, 'rh', atlas);
  lh_label = label.from.annotdata(lh_annot, region);
  rh_label = label.from.annotdata(rh_annot, region);
  lh_mask = mask.from.labeldata.for.hemi(lh_label, length(lh_annot$vertices));
  rh_mask = mask.from.labeldata.for.hemi(rh_label, length(rh_annot$vertices));

  # Edit the mask: add the vertices from another region to it:
  region2 = 'medialorbitofrontal';
  lh_label2 = label.from.annotdata(lh_annot, region2);
  rh_label2 = label.from.annotdata(rh_annot, region2);
  lh_mask2 = mask.from.labeldata.for.hemi(lh_label2, length(lh_annot$vertices),
   existing_mask = lh_mask);
  rh_mask2 = mask.from.labeldata.for.hemi(rh_label2, length(rh_annot$vertices),
   existing_mask = rh_mask);
  # Visualize the mask:
  vis.mask.on.subject(subjects_dir, subject_id, lh_mask2, rh_mask2);
} # }