Create a MEG informed cortical parcellation

This example shows how to generate and visualize a lead-field based MEG informed cortical parcellation.

Import the required

from megicparc import (compute_distance_matrix, compute_parcellation,
                       store_flame_labels)
from megicparc.viz import (plot_flame_labels, plot_flame_centroids)

from mne import (read_forward_solution, read_source_spaces,
                 read_labels_from_annot, pick_types_forward,
                 convert_forward_solution)
from mne.datasets import sample

import os.path as op

Define input parameters for the flame algorithm running in megicperc

parc = 'aparc'
gamma = 0.8
theta = 0.05
sensors_meg = 'grad'
knn = 30

Load lead-field matrix and source-space

data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
subject = 'sample'
fwd_file = op.join(data_path, 'MEG', subject, 'sample_audvis-meg-eeg-oct-6-fwd.fif')
src_file = op.join('..', 'data', 'data_mne_sample', 'source_space_distance-src.fif')

fwd = read_forward_solution(fwd_file)
fwd = pick_types_forward(fwd, meg=sensors_meg, eeg=False,
                         ref_meg=False, exclude='bads')
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True,
                               use_cps=True)
src = read_source_spaces(src_file)
# Inter-source distances along the cortical surface has been added
# to the source-space as follows
#from mne import add_source_space_distances
#src = add_source_space_distances(fwd['src'])
#src.save(op.join('..', 'data', 'data_mne_sample', 'source_space_distance-src.fif'))

fwd['src'] = src

Reading forward solution from /u/29/sommars1/unix/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    Forward solutions combined: MEG, EEG
    Source spaces transformed to the forward solution coordinate frame
    203 out of 366 channels remain after picking
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read

Load the cortical atlas and run flame algorithm

label_lh = read_labels_from_annot(subject=subject, parc=parc, hemi='lh',
                                  subjects_dir=subjects_dir)
label_rh = read_labels_from_annot(subject=subject, parc=parc, hemi='rh',
                                  subjects_dir=subjects_dir)
label = label_lh + label_rh
sort_dist = compute_distance_matrix(fwd, gamma=gamma,
                                    theta=theta, labels=label)
sample_parc = compute_parcellation(sort_dist, k_nn=knn)
# Store megic parcels as mne-python labels for visualization purpose.
sample_parc_labels = store_flame_labels(sample_parc, src, subject)

""
plot_flame_centroids(sample_parc, fwd['src'], subject, subjects_dir,
                     brain=None, surf='inflated', scale_factor=0.5,
                     color='white')


""
brain_parc = plot_flame_labels([87], sample_parc_labels, src, subject,
                  subjects_dir, surf='inflated', brain=None,
                  color=None, plot_region=True,
                  plot_points=False, plot_borders=False)
plot_flame_labels([80], sample_parc_labels, src, subject,
                  subjects_dir, surf='inflated', brain=brain_parc,
                  color=None, plot_region=False,
                  plot_points=False, plot_borders=True)
plot_flame_labels([52], sample_parc_labels, src, subject,
                  subjects_dir, surf='inflated', brain=brain_parc,
                  color=None, plot_region=False,
                  plot_points=True, plot_borders=False)


""
# sphinx_gallery_thumbnail_number = 2

• 
• 

Reading labels from parcellation...
   read 34 labels from /u/29/sommars1/unix/mne_data/MNE-sample-data/subjects/sample/label/lh.aparc.annot
Reading labels from parcellation...
   read 34 labels from /u/29/sommars1/unix/mne_data/MNE-sample-data/subjects/sample/label/rh.aparc.annot
Using leadfield of size = (203, 7498)
Computing distance matrix for gamma=0.80 theta=0.05
Reading cortical distance from src
Adding anatomical constraints
Computing cosine distance
Normalizing leadfield for grad sensors
Check n_vert = 7498
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Using pyvistaqt 3d backend.


''