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Plot standard space morphometry data for a group of subjects.

Source: R/vis_group.R
vis.group.morph.standard.Rd

Plot standard space morphometry data for a group of subjects and combine them into a single large image.

Usage

vis.group.morph.standard(
  subjects_dir,
  subject_id,
  measure,
  fwhm = "10",
  view_angles = "sd_dorsal",
  output_img = "fsbrain_group_morph.png",
  num_per_row = 5L,
  captions = subject_id,
  rglactions = list(no_vis = 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

vector of character strings, the subject identifiers

measure

vector of character strings, the morphometry measures, e.g., c('thickness', 'area')

fwhm

vector of character strings, the smoothing kernel FWHM strings, e.g., c('0', '10', '15')

view_angles

see get.view.angle.names.

output_img

character string, the file path for the output image. Should end with '.png'.

num_per_row

positive integer, the number of tiles per row.

captions

optional vector of character strings, the short text annotations for the individual tiles. Typically used to plot the subject identifier.

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.

...

extra parameters passed to the subject level visualization function. Not all may make sense in this context. Example: surface='pial'.

Value

named list, see the return value of arrange.brainview.images.grid for details.

Note

The subjects are plotted row-wise, in the order in which they appear in the 'subject_id' parameter. This function is vectorized over 'subject_id', 'measure' and 'fwhm'.

You can force an identical plot range for all subjects, so that one color represents identical values across subjects, via 'makecmap_options'. E.g., for the ... parameter, pass makecmap_options=list('colFn'=viridis::viridis, 'range'=c(0, 4))).

See also

Other group visualization functions: vis.data.on.group.native(), vis.data.on.group.standard(), vis.group.annot(), vis.group.coloredmeshes(), vis.group.morph.native()