1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
|
# -*- coding: utf-8 -*-
"""
============================
Plot a cortical parcellation
============================
In this example, we download the HCP-MMP1.0 parcellation [1]_ and show it
on ``fsaverage``.
.. 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.
"""
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from surfer import Brain
import mne
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_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)
###############################################################################
# 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')
|
