Use source space morphingΒΆ

This example shows how to use source space morphing (as opposed to SourceEstimate morphing) to create data that can be compared between subjects.

Warning

Source space morphing will likely lead to source spaces that are less evenly sampled than source spaces created for individual subjects. Use with caution and check effects on localization before use.

  • ../../_images/sphx_glr_plot_source_space_morphing_000.png
  • ../../_images/sphx_glr_plot_source_space_morphing_001.png

Script output:

Updating smoothing matrix, be patient..
Smoothing matrix creation, step 1
colormap: fmin=2.05e-03 fmid=8.78e-02 fmax=1.74e-01 transparent=1
Updating smoothing matrix, be patient..
Smoothing matrix creation, step 1
colormap: fmin=2.05e-03 fmid=8.78e-02 fmax=1.74e-01 transparent=1
# Authors: Denis A. Engemann <denis.engemann@gmail.com>
#          Eric larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

import os.path as op

import mne

data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_trans = op.join(data_path, 'MEG', 'sample',
                      'sample_audvis_raw-trans.fif')
fname_bem = op.join(subjects_dir, 'sample', 'bem',
                    'sample-5120-bem-sol.fif')
fname_src_fs = op.join(subjects_dir, 'fsaverage', 'bem',
                       'fsaverage-ico-5-src.fif')
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')

# Get relevant channel information
info = mne.io.read_info(raw_fname)
info = mne.pick_info(info, mne.pick_types(info, meg=True, eeg=False,
                                          exclude=[]))

# Morph fsaverage's source space to sample
src_fs = mne.read_source_spaces(fname_src_fs)
src_morph = mne.morph_source_spaces(src_fs, subject_to='sample',
                                    subjects_dir=subjects_dir)

# Compute the forward with our morphed source space
fwd = mne.make_forward_solution(info, trans=fname_trans,
                                src=src_morph, bem=fname_bem)
# fwd = mne.convert_forward_solution(fwd, surf_ori=True, force_fixed=True)
mag_map = mne.sensitivity_map(fwd, ch_type='mag')

# Return this SourceEstimate (on sample's surfaces) to fsaverage's surfaces
mag_map_fs = mag_map.to_original_src(src_fs, subjects_dir=subjects_dir)

# Plot the result, which tracks the sulcal-gyral folding
# outliers may occur, we'll place the cutoff at 99 percent.
kwargs = dict(clim=dict(kind='percent', lims=[0, 50, 99]),
              # no smoothing, let's see the dipoles on the cortex.
              smoothing_steps=1, hemi='rh', views=['lat'])

# Now note that the dipoles on fsaverage are almost equidistant while
# morphing will distribute the dipoles unevenly across the given subject's
# cortical surface to achieve the closest approximation to the average brain.
# Our testing code suggests a correlation of higher than 0.99.

brain_subject = mag_map.plot(  # plot forward in subject source space (morphed)
    time_label=None, subjects_dir=subjects_dir, **kwargs)

brain_fs = mag_map_fs.plot(  # plot forward in original source space (remapped)
    time_label=None, subjects_dir=subjects_dir, **kwargs)

Total running time of the script: (0 minutes 57.968 seconds)

Download Python source code: plot_source_space_morphing.py