Cross-hemisphere comparison

This example illustrates how to visualize the difference between activity in the left and the right hemisphere. The data from the right hemisphere is mapped to the left hemisphere, and then the difference is plotted. For more information see mne.compute_morph_matrix().



Morphing data...
    Left-hemisphere map read.
    Right-hemisphere map read.
    5 smooth iterations done.
    5 smooth iterations done.
Computing morph matrix...
Morph map "/home/circleci/mne_data/MNE-sample-data/subjects/morph-maps/fsaverage_sym-fsaverage_sym-xhemi-morph.fif" does not exist, creating it and saving it to disk (this may take a few minutes)
Creating morph map fsaverage_sym -> fsaverage_sym xhemi
    2 smooth iterations done.

# Author: Christian Brodbeck <>
# License: BSD (3-clause)

import mne

data_dir = mne.datasets.sample.data_path()
subjects_dir = data_dir + '/subjects'
stc_path = data_dir + '/MEG/sample/sample_audvis-meg-eeg'

stc = mne.read_source_estimate(stc_path, 'sample')

# First, morph the data to fsaverage_sym, for which we have left_right
# registrations:
stc = stc.morph('fsaverage_sym', subjects_dir=subjects_dir, smooth=5)

# Compute a morph-matrix mapping the right to the left hemisphere. Use the
# vertices parameters to determine source and target hemisphere:
mm = mne.compute_morph_matrix(
    'fsaverage_sym', 'fsaverage_sym', xhemi=True,  # cross-hemisphere morphing
    vertices_from=[[], stc.vertices[1]],  # from the right hemisphere
    vertices_to=[stc.vertices[0], []],  # to the left hemisphere

# SourceEstimate on the left hemisphere:
stc_lh = mne.SourceEstimate(stc.lh_data, [stc.vertices[0], []], stc.tmin,
                            stc.tstep, stc.subject)
# SourceEstimate of the right hemisphere, morphed to the left:
stc_rh_on_lh = mne.SourceEstimate(mm * stc.rh_data, [stc.vertices[0], []],
                                  stc.tmin, stc.tstep, stc.subject)
# Since both STCs are now on the same hemisphere we can subtract them:
diff = stc_lh - stc_rh_on_lh

diff.plot(hemi='lh', subjects_dir=subjects_dir, initial_time=0.07,
          size=(800, 600))

Total running time of the script: ( 0 minutes 53.530 seconds)

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