##############################################################################
# Display outlines of the regions of interest on top of a statistical map
# -----------------------------------------------------------------------
figure = plotting.plot_surf_stat_map(fsaverage.infl_right,
texture,
hemi='right',
title='Surface right hemisphere',
colorbar=True,
threshold=1.,
bg_map=fsaverage.sulc_right)
plotting.plot_surf_contours(fsaverage.infl_right,
parcellation,
labels=labels,
levels=regions_indices,
figure=figure,
legend=True,
colors=['g', 'k'])
plotting.show()
##############################################################################
# Plot with higher-resolution mesh
# --------------------------------
#
# :func:`~nilearn.datasets.fetch_surf_fsaverage` takes a ``mesh`` argument
# which specifies whether to fetch the low-resolution ``fsaverage5`` mesh, or
# the high-resolution fsaverage mesh. Using ``mesh="fsaverage"`` will result
# in more memory usage and computation time, but finer visualizations.
big_fsaverage = datasets.fetch_surf_fsaverage('fsaverage')
print(f'ROI name: {ROI_name}, number of voxels {np.sum(roi_voxels)}')
figure = plotting.plot_surf_stat_map(
fsaverage['infl_' + hemi_],
roi_voxels.astype(int),
hemi=hemi_,
title=f'Surface {hemi_} hemisphere',
colorbar=False,
threshold=1.,
bg_map=fsaverage['sulc_' + hemi_])
plotting.plot_surf_contours(
fsaverage['infl_' + hemi_],
roi_surf,
levels=[
1,
],
figure=figure,
legend=True,
colors=[
'g',
],
labels=[ROI_name],
output_file=f'{sub_dir}/roi_{ROI_name}.png')
sub_parcel_roi_vxl += roi_voxels.astype(int)
# plot all voxels
all_ROIS = d_parcel_fsaverage[netw_of_interest][hemi + '_ROIs']
print(f'ROI name: {hemi}_ROIs, number of voxels {np.sum(sub_parcel_roi_vxl)}')
figure = plotting.plot_surf_stat_map(fsaverage['infl_' + hemi_],
sub_parcel_roi_vxl.astype(int),
hemi=hemi_,
'language_separFiles_in_fsaverage', ROI_file))
figure = plotting.plot_surf_stat_map(
fsaverage.infl_left,
lh_surf.agg_data(),
hemi='left',
title='Surface left hemisphere',
colorbar=True,
threshold=1.,
bg_map=fsaverage.sulc_left)
plotting.plot_surf_contours(
fsaverage.infl_left,
lh_surf.agg_data(),
levels=[
1,
],
figure=figure,
legend=False,
colors=[
'g',
],
output_file=os.path.join(
path_to_masks, 'language_separFiles_in_fsaverage',
ROI_file.replace('.gii', '.png')))
elif ROI_file.__contains__('_RH_'):
rh_surf = nib.load(
os.path.join(path_to_masks,
'language_separFiles_in_fsaverage', ROI_file))
figure = plotting.plot_surf_stat_map(
fsaverage.infl_right,
rh_surf.agg_data(),
hemi='right',
title='Surface right hemisphere',
roi_map=roi_voxels,
hemi=hemi,
view='lateral',
cmap='hot',
bg_map=network,
bg_on_data=True,
alpha=.3,
darkness=.2,
axes=ax0)
plotting.plot_surf_contours(
fsaverage['infl_' + hemi],
roi_surf,
levels=[
1,
],
axes=ax0,
legend=True,
colors=[
'k',
],
labels=[f'{ROI_name}, #vox: {np.sum(roi_voxels)}'])
ax1 = fig.add_axes((.65, .3, .3, .3))
ax1.hist(samples,
bins=hist_bins,
align='mid',
edgecolor='k',
linewidth=.5)
ax1.set_xlabel("voxel activation")
ax1.set_ylabel("Frequency")
ax1.axvline(x=samples_th, color='r', linewidth=1)
fig.savefig(