Compute spatial resolution metrics in source space

Compute peak localisation error and spatial deviation for the point-spread functions of dSPM and MNE. Plot their distributions and difference of distributions. This example mimics some results from [1], namely Figure 3 (peak localisation error for PSFs, L2-MNE vs dSPM) and Figure 4 (spatial deviation for PSFs, L2-MNE vs dSPM).

# Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_resolution_matrix, resolution_metrics

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path / "subjects"
meg_path = data_path / "MEG" / "sample"
fname_fwd = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
fname_cov = meg_path / "sample_audvis-cov.fif"
fname_evo = meg_path / "sample_audvis-ave.fif"

# read forward solution
forward = mne.read_forward_solution(fname_fwd)
# forward operator with fixed source orientations
mne.convert_forward_solution(forward, surf_ori=True, force_fixed=True, copy=False)

# noise covariance matrix
noise_cov = mne.read_cov(fname_cov)

# evoked data for info
evoked = mne.read_evokeds(fname_evo, 0)

# make inverse operator from forward solution
# free source orientation
inverse_operator = mne.minimum_norm.make_inverse_operator(
    info=evoked.info, forward=forward, noise_cov=noise_cov, loose=0.0, depth=None
)

# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr**2

MNE

Compute resolution matrices, peak localisation error (PLE) for point spread functions (PSFs), spatial deviation (SD) for PSFs:

rm_mne = make_inverse_resolution_matrix(
    forward, inverse_operator, method="MNE", lambda2=lambda2
)
ple_mne_psf = resolution_metrics(
    rm_mne, inverse_operator["src"], function="psf", metric="peak_err"
)
sd_mne_psf = resolution_metrics(
    rm_mne, inverse_operator["src"], function="psf", metric="sd_ext"
)
del rm_mne

dSPM

Do the same for dSPM:

rm_dspm = make_inverse_resolution_matrix(
    forward, inverse_operator, method="dSPM", lambda2=lambda2
)
ple_dspm_psf = resolution_metrics(
    rm_dspm, inverse_operator["src"], function="psf", metric="peak_err"
)
sd_dspm_psf = resolution_metrics(
    rm_dspm, inverse_operator["src"], function="psf", metric="sd_ext"
)
del rm_dspm, forward

Visualize results

Visualise peak localisation error (PLE) across the whole cortex for MNE PSF:

brain_ple_mne = ple_mne_psf.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=1,
    clim=dict(kind="value", lims=(0, 2, 4)),
)
brain_ple_mne.add_text(0.1, 0.9, "PLE MNE", "title", font_size=16)

And dSPM:

brain_ple_dspm = ple_dspm_psf.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=2,
    clim=dict(kind="value", lims=(0, 2, 4)),
)
brain_ple_dspm.add_text(0.1, 0.9, "PLE dSPM", "title", font_size=16)

Subtract the two distributions and plot this difference

diff_ple = ple_mne_psf - ple_dspm_psf

brain_ple_diff = diff_ple.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=3,
    clim=dict(kind="value", pos_lims=(0.0, 1.0, 2.0)),
)
brain_ple_diff.add_text(0.1, 0.9, "PLE MNE-dSPM", "title", font_size=16)

These plots show that dSPM has generally lower peak localization error (red color) than MNE in deeper brain areas, but higher error (blue color) in more superficial areas.

Next we’ll visualise spatial deviation (SD) across the whole cortex for MNE PSF:

brain_sd_mne = sd_mne_psf.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=4,
    clim=dict(kind="value", lims=(0, 2, 4)),
)
brain_sd_mne.add_text(0.1, 0.9, "SD MNE", "title", font_size=16)

And dSPM:

brain_sd_dspm = sd_dspm_psf.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=5,
    clim=dict(kind="value", lims=(0, 2, 4)),
)
brain_sd_dspm.add_text(0.1, 0.9, "SD dSPM", "title", font_size=16)

Subtract the two distributions and plot this difference:

diff_sd = sd_mne_psf - sd_dspm_psf

brain_sd_diff = diff_sd.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=6,
    clim=dict(kind="value", pos_lims=(0.0, 1.0, 2.0)),
)
brain_sd_diff.add_text(0.1, 0.9, "SD MNE-dSPM", "title", font_size=16)

These plots show that dSPM has generally higher spatial deviation than MNE (blue color), i.e. worse performance to distinguish different sources.

References

[1]

Olaf Hauk, Matti Stenroos, and Matthias Treder. Towards an objective evaluation of EEG/MEG source estimation methods: the linear tool kit. bioRxiv, 2019. doi:10.1101/672956.

Estimated memory usage: 0 MB