Get ROI Voxel MaskΒΆ

Get proportion of each voxel that exists within a named ROI (this constitutes a probability map for the ROI, with values ranging from 0-1). Plot this probablistic roi mask onto a flatmap.

In order for this to work, the specified ROI must exist in the overlays.svg file in the pycortex filestore for this subject.

plot roi voxel mask

Out:

Caching mapper...
Found 100.00% of EBA
Cutting 0 overlapping voxels (should be < ~50)
Generating a flatmap cache
Failed to get connection
** (inkscape:5543): CRITICAL **: 01:33:54.463: dbus_g_proxy_new_for_name: assertion 'connection != NULL' failed
** (inkscape:5543): CRITICAL **: 01:33:54.463: dbus_g_proxy_call: assertion 'DBUS_IS_G_PROXY (proxy)' failed
** (inkscape:5543): CRITICAL **: 01:33:54.464: dbus_g_connection_register_g_object: assertion 'connection != NULL' failed

import cortex
import matplotlib.pyplot as plt

subject = "S1"
xfm = "fullhead"
roi = "EBA"

# Get the map of which voxels are inside of our ROI
roi_masks = cortex.utils.get_roi_masks(subject, xfm,
                                       roi_list=[roi],
                                       gm_sampler='cortical-conservative', # Select only voxels mostly within cortex
                                       split_lr=False, # No separate left/right ROIs
                                       threshold=None, # Leave roi mask values as probabilites / fractions
                                       return_dict=True
                                       )

# Plot the mask for one ROI onto a flatmap
roi_data = cortex.Volume(roi_masks[roi], subject, xfm,
                         vmin=0, # This is a probability mask, so only
                         vmax=1, # so scale btw zero and one
                         cmap="inferno", # For pretty
                         )

cortex.quickflat.make_figure(roi_data,
                             thick=1, # select a single depth (btw white matter & pia)
                             sampler='nearest', # no interpolation
                             with_curvature=True,
                             with_colorbar=True,
                             )

plt.show()

Total running time of the script: ( 0 minutes 7.399 seconds)

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