Plot a cortical parcellation

In this example, we download the HCP-MMP1.0 parcellation 1 and show it on fsaverage. We will also download the customized 448-label aparc parcellation from 2

Note

The HCP-MMP dataset has license terms restricting its use. Of particular relevance:

“I will acknowledge the use of WU-Minn HCP data and data derived from WU-Minn HCP data when publicly presenting any results or algorithms that benefitted from their use.”

References

1

Glasser MF et al. (2016) A multi-modal parcellation of human cerebral cortex. Nature 536:171-178.

2

Khan S et al. (2018) Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. Neuroimage 174 57-68.

# Author: Eric Larson <larson.eric.d@gmail.com>
#         Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)

import mne
Brain = mne.viz.get_brain_class()

subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
                                        verbose=True)

mne.datasets.fetch_aparc_sub_parcellation(subjects_dir=subjects_dir,
                                          verbose=True)

labels = mne.read_labels_from_annot(
    'fsaverage', 'HCPMMP1', 'lh', subjects_dir=subjects_dir)

brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
              cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1')
aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
brain.add_label(aud_label, borders=False)

Out:

Reading labels from parcellation...
   read 181 labels from /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/label/lh.HCPMMP1.annot

We can also plot a combined set of labels (23 per hemisphere).

brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
              cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1_combined')

We can add another custom parcellation

brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
              cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('aparc_sub')

Estimated memory usage: 16 MB