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%matplotlib inline

Plotting the full vector-valued MNE solution

The source space that is used for the inverse computation defines a set of dipoles, distributed across the cortex. When visualizing a source estimate, it is sometimes useful to show the dipole directions in addition to their estimated magnitude. This can be accomplished by computing a :class:mne.VectorSourceEstimate and plotting it with :meth:stc.plot <mne.VectorSourceEstimate.plot>, which uses :func:~mne.viz.plot_vector_source_estimates under the hood rather than :func:~mne.viz.plot_source_estimates.

It can also be instructive to visualize the actual dipole/activation locations in 3D space in a glass brain, as opposed to activations imposed on an inflated surface (as typically done in :meth:mne.SourceEstimate.plot), as it allows you to get a better sense of the true underlying source geometry.



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# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD (3-clause)

import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path + '/subjects'

# Read evoked data
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))

# Read inverse solution
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inv = read_inverse_operator(fname_inv)

# Apply inverse solution, set pick_ori='vector' to obtain a
# :class:`mne.VectorSourceEstimate` object
snr = 3.0
lambda2 = 1.0 / snr ** 2
stc = apply_inverse(evoked, inv, lambda2, 'dSPM', pick_ori='vector')

# Use peak getter to move visualization to the time point of the peak magnitude
_, peak_time = stc.magnitude().get_peak(hemi='lh')

Plot the source estimate:



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brain = stc.plot(
    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir)

Plot the activation in the direction of maximal power for this data:



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stc_max, directions = stc.project('pca', src=inv['src'])
# These directions must by design be close to the normals because this
# inverse was computed with loose=0.2:
print('Absolute cosine similarity between source normals and directions: '
      f'{np.abs(np.sum(directions * inv["source_nn"][2::3], axis=-1)).mean()}')
brain_max = stc_max.plot(
    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
    time_label='Max power')
brain_normal = stc.project('normal', inv['src'])[0].plot(
    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
    time_label='Normal')

You can also do this with a fixed-orientation inverse. It looks a lot like the result above because the loose=0.2 orientation constraint keeps sources close to fixed orientation:



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fname_inv_fixed = (
    data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif')
inv_fixed = read_inverse_operator(fname_inv_fixed)
stc_fixed = apply_inverse(
    evoked, inv_fixed, lambda2, 'dSPM', pick_ori='vector')
brain_fixed = stc_fixed.plot(
    initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir)