Computing various MNE solutions

This example shows example fixed- and free-orientation source localizations produced by MNE, dSPM, sLORETA, and eLORETA.

# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse

print(__doc__)

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

# Read data
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked = mne.read_evokeds(fname_evoked, condition='Left Auditory',
                          baseline=(None, 0))
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
fwd = mne.read_forward_solution(fname_fwd)
cov = mne.read_cov(fname_cov)

Fixed orientation

First let’s create a fixed-orientation inverse, with the default weighting.

inv = make_inverse_operator(evoked.info, fwd, cov, loose=0., depth=0.8,
                            verbose=True)

Out:

Forward is not surface oriented, converting.
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
Computing inverse operator with 364 channels.
    Created an SSP operator (subspace dimension = 4)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
estimated rank (eeg): 58
Setting small EEG eigenvalues to zero.
Not doing PCA for EEG.
Total rank is 360
Creating the depth weighting matrix...
    203 planar channels
    limit = 7262/7498 = 10.020865
    scale = 2.58122e-08 exp = 0.8
    Picked elements from a free-orientation depth-weighting prior into the fixed-orientation one
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
Computing inverse operator with 364 channels.
Creating the source covariance matrix
Whitening the forward solution.
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
    largest singular value = 5.70263
    scaling factor to adjust the trace = 1.18949e+19

Let’s look at the current estimates using MNE. We’ll take the absolute value of the source estimates to simplify the visualization.

snr = 3.0
lambda2 = 1.0 / snr ** 2
kwargs = dict(initial_time=0.08, hemi='both', subjects_dir=subjects_dir,
              size=(600, 600))

stc = abs(apply_inverse(evoked, inv, lambda2, 'MNE', verbose=True))
brain = stc.plot(figure=1, **kwargs)
brain.add_text(0.1, 0.9, 'MNE', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_001.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
[done]

Next let’s use the default noise normalization, dSPM:

stc = abs(apply_inverse(evoked, inv, lambda2, 'dSPM', verbose=True))
brain = stc.plot(figure=2, **kwargs)
brain.add_text(0.1, 0.9, 'dSPM', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_002.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    dSPM...
[done]

And sLORETA:

stc = abs(apply_inverse(evoked, inv, lambda2, 'sLORETA', verbose=True))
brain = stc.plot(figure=3, **kwargs)
brain.add_text(0.1, 0.9, 'sLORETA', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_003.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
    Computing noise-normalization factors (sLORETA)...
[done]
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    sLORETA...
[done]

And finally eLORETA:

stc = abs(apply_inverse(evoked, inv, lambda2, 'eLORETA', verbose=True))
brain = stc.plot(figure=4, **kwargs)
brain.add_text(0.1, 0.9, 'eLORETA', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_004.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
    Computing noise-normalization factors (eLORETA)...
        Fitting up to 20 iterations...
        Converged on iteration 11 (5.7e-07 < 1e-06)
        Assembling eLORETA kernel and modifying inverse
[done]
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
[done]

Free orientation

Now let’s not constrain the orientation of the dipoles at all by creating a free-orientation inverse.

inv = make_inverse_operator(evoked.info, fwd, cov, loose=1., depth=0.8,
                            verbose=True)

Out:

Forward is not surface oriented, converting.
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
Computing inverse operator with 364 channels.
    Created an SSP operator (subspace dimension = 4)
estimated rank (mag + grad): 302
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
estimated rank (eeg): 58
Setting small EEG eigenvalues to zero.
Not doing PCA for EEG.
Total rank is 360
Creating the depth weighting matrix...
    203 planar channels
    limit = 7262/7498 = 10.020865
    scale = 2.58122e-08 exp = 0.8
Computing inverse operator with 364 channels.
Creating the source covariance matrix
Whitening the forward solution.
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
    largest singular value = 5.2188
    scaling factor to adjust the trace = 3.44205e+19

Let’s look at the current estimates using MNE. We’ll take the absolute value of the source estimates to simplify the visualization.

stc = apply_inverse(evoked, inv, lambda2, 'MNE', verbose=True)
brain = stc.plot(figure=5, **kwargs)
brain.add_text(0.1, 0.9, 'MNE', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_005.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Combining the current components...
[done]

Next let’s use the default noise normalization, dSPM:

stc = apply_inverse(evoked, inv, lambda2, 'dSPM', verbose=True)
brain = stc.plot(figure=6, **kwargs)
brain.add_text(0.1, 0.9, 'dSPM', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_006.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Combining the current components...
    dSPM...
[done]

And sLORETA:

stc = apply_inverse(evoked, inv, lambda2, 'sLORETA', verbose=True)
brain = stc.plot(figure=7, **kwargs)
brain.add_text(0.1, 0.9, 'sLORETA', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_007.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
    Computing noise-normalization factors (sLORETA)...
[done]
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Combining the current components...
    sLORETA...
[done]

And finally eLORETA:

stc = apply_inverse(evoked, inv, lambda2, 'eLORETA', verbose=True)
brain = stc.plot(figure=8, **kwargs)
brain.add_text(0.1, 0.9, 'eLORETA', 'title', font_size=14)
../_images/sphx_glr_plot_mne_solutions_008.png

Out:

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 = 4)
    Created the whitener using a full noise covariance matrix (4 small eigenvalues omitted)
    Computing noise-normalization factors (eLORETA)...
        Using independent orientation weights
        Fitting up to 20 iterations (this make take a while)...
        Converged on iteration 11 (4.6e-07 < 1e-06)
        Assembling eLORETA kernel and modifying inverse
[done]
Applying inverse operator to "Left Auditory"...
    Picked 364 channels from the data
    Computing inverse...
    Eigenleads need to be weighted ...
    Combining the current components...
[done]

Total running time of the script: ( 4 minutes 23.184 seconds)

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