Compute cross-talk functions (CTFs) for labels for MNE/dSPM/sLORETAΒΆ

CTFs are computed for four labels in the MNE sample data set for linear inverse operators (MNE, dSPM, sLORETA). CTFs describe the sensitivity of a linear estimator (e.g. for one label) to sources across the cortical surface. Sensitivity to sources outside the label is undesirable, and referred to as “leakage” or “cross-talk”.

  • ../../_images/sphx_glr_plot_mne_crosstalk_function_001.png
  • ../../_images/sphx_glr_plot_mne_crosstalk_function_002.png

Out:

Reading forward solution from /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    MEG and EEG forward solutions combined
    Source spaces transformed to the forward solution coordinate frame
    Cartesian source orientations...
[done]
Reading inverse operator decomposition from /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
    Reading inverse operator info...
    [done]
    Reading inverse operator decomposition...
    [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
    Noise covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 2) found.
    Source covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 6) found.
    Orientation priors read.
    22494 x 22494 diagonal covariance (kind = 5) found.
    Depth priors read.
    Did not find the desired covariance matrix (kind = 3)
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    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
    Changing to fixed-orientation forward solution with surface-based source orientations...
    [done]
About to process 4 labels
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 1
    Created the regularized inverter
    Created an SSP operator (subspace dimension = 3)
    Created the whitener using a full noise covariance matrix (3 small eigenvalues omitted)
Picked 305 channels from the data
Computing inverse...
(eigenleads need to be weighted)...
[done]
Dimension of inverse matrix: (7498, 306)
Computing SVD within labels, using 1 component(s)
First 5 singular values: [ 11650.27808851   7034.33675366   3267.03724057   2762.39609838
   2221.0665056 ]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 63.6% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [ 8512.68820924  7872.78518609  6741.64926914  3353.04131104  2498.07715718]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 35.6% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [ 15630.02296497   7418.15820631   5346.56712701   4258.90971316
   1958.40295249]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 69.4% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [ 18096.44764144   8773.30811442   4831.2396142    3456.55903612
   3129.60231085]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 71.5% variance in label.
    Changing to fixed-orientation forward solution with surface-based source orientations...
    [done]
About to process 4 labels
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 1
    Created the regularized inverter
    Created an SSP operator (subspace dimension = 3)
    Created the whitener using a full noise covariance matrix (3 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Picked 305 channels from the data
Computing inverse...
(eigenleads need to be weighted)...
(dSPM)...
[done]
Dimension of inverse matrix: (7498, 306)
Computing SVD within labels, using 1 component(s)
First 5 singular values: [  7.55262104e+13   5.83910239e+13   3.44075225e+13   2.36505439e+13
   1.94336945e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 48.7% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [  9.07577774e+13   7.61818100e+13   5.75087046e+13   3.39598529e+13
   2.66587446e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 41.4% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [  5.31303675e+13   4.01608741e+13   2.99674179e+13   2.09348683e+13
   1.16746740e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 47.2% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [  9.06302929e+13   5.13491534e+13   3.33843084e+13   3.13749745e+13
   1.90838680e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 59.3% variance in label.
Updating smoothing matrix, be patient..
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colormap: fmin=6.54e-03 fmid=8.97e-03 fmax=4.19e-02 transparent=1
Updating smoothing matrix, be patient..
Smoothing matrix creation, step 1
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colormap: fmin=4.69e+07 fmid=6.23e+07 fmax=2.35e+08 transparent=1

# Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#
# License: BSD (3-clause)

from mayavi import mlab

import mne
from mne.datasets import sample
from mne.minimum_norm import cross_talk_function, read_inverse_operator

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path + '/subjects/'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
fname_label = [data_path + '/MEG/sample/labels/Aud-rh.label',
               data_path + '/MEG/sample/labels/Aud-lh.label',
               data_path + '/MEG/sample/labels/Vis-rh.label',
               data_path + '/MEG/sample/labels/Vis-lh.label']

# read forward solution
forward = mne.read_forward_solution(fname_fwd)

# read label(s)
labels = [mne.read_label(ss) for ss in fname_label]

inverse_operator = read_inverse_operator(fname_inv)

# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr ** 2
mode = 'svd'
n_svd_comp = 1

method = 'MNE'  # can be 'MNE', 'dSPM', or 'sLORETA'
stc_ctf_mne = cross_talk_function(
    inverse_operator, forward, labels, method=method, lambda2=lambda2,
    signed=False, mode=mode, n_svd_comp=n_svd_comp)

method = 'dSPM'
stc_ctf_dspm = cross_talk_function(
    inverse_operator, forward, labels, method=method, lambda2=lambda2,
    signed=False, mode=mode, n_svd_comp=n_svd_comp)

time_label = "MNE %d"
brain_mne = stc_ctf_mne.plot(hemi='rh', subjects_dir=subjects_dir,
                             time_label=time_label,
                             figure=mlab.figure(size=(500, 500)))

time_label = "dSPM %d"
brain_dspm = stc_ctf_dspm.plot(hemi='rh', subjects_dir=subjects_dir,
                               time_label=time_label,
                               figure=mlab.figure(size=(500, 500)))

# Cross-talk functions for MNE and dSPM (and sLORETA) have the same shapes
# (they may still differ in overall amplitude).
# Point-spread functions (PSfs) usually differ significantly.

Total running time of the script: ( 0 minutes 16.114 seconds)

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