# 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 mne.VectorSourceEstimate and plotting it with stc.plot, which uses plot_vector_source_estimates() under the hood rather than 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 mne.SourceEstimate.plot()), as it allows you to get a better sense of the underlying source geometry.

# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#

import numpy as np
import mne
from mne.datasets import sample

print(__doc__)

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

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

fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'

# 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')


Out:

Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Found the data of interest:
t =    -199.80 ...     499.49 ms (Left Auditory)
0 CTF compensation matrices available
nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
[done]
[done]
305 x 305 full covariance (kind = 1) found.
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
22494 x 22494 diagonal covariance (kind = 2) found.
22494 x 22494 diagonal covariance (kind = 6) found.
22494 x 22494 diagonal covariance (kind = 5) found.
Did not find the desired covariance matrix (kind = 3)
Computing patch statistics...
[done]
Computing patch statistics...
[done]
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Source spaces transformed to the inverse solution coordinate frame
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained  59.4% variance
dSPM...
[done]


Plot the source estimate:

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

Using control points [ 3.95048065  4.56941314 17.72451438]


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

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']).plot(
initial_time=peak_time, hemi='lh', subjects_dir=subjects_dir,
time_label='Normal')  Out:

Absolute cosine similarity between source normals and directions: 0.975978731472672
Using control points [ 3.90575168  4.52414196 17.71336747]
Using control points [ 3.83607509  4.44726242 17.57923594]


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:

fname_inv_fixed = (
data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif')
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) Out:

Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif...
[done]
[done]
305 x 305 full covariance (kind = 1) found.
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
7498 x 7498 diagonal covariance (kind = 2) found.
Did not find the desired covariance matrix (kind = 6)
7498 x 7498 diagonal covariance (kind = 5) found.
Did not find the desired covariance matrix (kind = 3)
Computing patch statistics...
[done]
Computing patch statistics...
[done]
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Source spaces transformed to the inverse solution coordinate frame
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained  59.3% variance
dSPM...
[done]
Using control points [ 4.00351751  4.62842071 17.43519503]


Total running time of the script: ( 1 minutes 7.592 seconds)

Estimated memory usage: 236 MB

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