Plot Nutmeg style volumetric source estimates using nilearn.
VectorSourceEstimateThe vector source estimate to plot.
SourceSpaces | instance of SourceMorphThe source space. Can also be a SourceMorph to morph the STC to a new subject (see Examples).
Changed in version 0.18: Support for SpatialImage.
str | NoneThe FreeSurfer subject name.
If None, stc.subject will be used.
NoneThe path to the directory containing the FreeSurfer subjects
reconstructions. If None, defaults to the SUBJECTS_DIR environment
variable.
strThe plotting mode to use. Either ‘stat_map’ (default) or ‘glass_brain’. For “glass_brain”, activation absolute values are displayed after being transformed to a standard MNI brain.
SpatialImage | strThe background image used in the nilearn plotting function.
Can also be a string to use the bg_img file in the subject’s
MRI directory (default is 'T1.mgz').
Not used in “glass brain” plotting.
If True, display a colorbar on the right of the plots.
str | np.ndarray of float, shape(n_colors, 3 | 4)Name of colormap to use or a custom look up table. If array, must be (n x 3) or (n x 4) array for with RGB or RGBA values between 0 and 255.
str | dictColorbar properties specification. If ‘auto’, set clim automatically based on data percentiles. If dict, should contain:
kind‘value’ | ‘percent’Flag to specify type of limits.
limslist | np.ndarray | tuple of float, 3 elementsLower, middle, and upper bounds for colormap.
pos_limslist | np.ndarray | tuple of float, 3 elementsLower, middle, and upper bound for colormap. Positive values will be mirrored directly across zero during colormap construction to obtain negative control points.
Note
Only one of lims or pos_lims should be provided.
Only sequential colormaps should be used with lims, and
only divergent colormaps should be used with pos_lims.
NoneIf True: use a linear transparency between fmin and fmid and make values below fmin fully transparent (symmetrically for divergent colormaps). None will choose automatically based on colormap type.
Show figures if True. Defaults to True.
float | NoneThe initial time to plot. Can be None (default) to use the time point
with the maximal absolute value activation across all voxels
or the initial_pos voxel (if initial_pos is None or not,
respectively).
New in version 0.19.
ndarray, shape (3,) | NoneThe initial position to use (in m). Can be None (default) to use the
voxel with the maximum absolute value activation across all time points
or at initial_time (if initial_time is None or not,
respectively).
New in version 0.19.
str | int | NoneControl verbosity of the logging output. If None, use the default
verbosity level. See the logging documentation and
mne.verbose() for details. Should only be passed as a keyword
argument.
FigureThe figure.
Notes
Click on any of the anatomical slices to explore the time series. Clicking on any time point will bring up the corresponding anatomical map.
The left and right arrow keys can be used to navigate in time. To move in time by larger steps, use shift+left and shift+right.
In 'glass_brain' mode, values are transformed to the standard MNI
brain using the FreeSurfer Talairach transformation
$SUBJECTS_DIR/$SUBJECT/mri/transforms/talairach.xfm.
New in version 0.17.
Changed in version 0.19: MRI volumes are automatically transformed to MNI space in
'glass_brain' mode.
Examples
Passing a mne.SourceMorph as the src
parameter can be useful for plotting in a different subject’s space
(here, a 'sample' STC in 'fsaverage'’s space):
>>> morph = mne.compute_source_morph(src_sample, subject_to='fsaverage')
>>> fig = stc_vol_sample.plot(morph)