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.”

# Author: Eric Larson <larson.eric.d@gmail.com>
#         Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
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)
parcellation
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")
parcellation

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")
parcellation

References#

Total running time of the script: (0 minutes 8.961 seconds)

Estimated memory usage: 22 MB

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