Compute spatial resolution metrics to compare MEG with EEG+MEG#

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). It shows that combining MEG with EEG reduces the point-spread function and increases the spatial resolution of source imaging, especially for deeper sources.

# 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.resolution_matrix import make_inverse_resolution_matrix
from mne.minimum_norm.spatial_resolution import resolution_metrics

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path / "subjects/"
meg_path = data_path / "MEG" / "sample"
fname_fwd_emeg = 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 with EEG and MEG
forward_emeg = mne.read_forward_solution(fname_fwd_emeg)
# forward operator with fixed source orientations
forward_emeg = mne.convert_forward_solution(
    forward_emeg, surf_ori=True, force_fixed=True
)

# create a forward solution with MEG only
forward_meg = mne.pick_types_forward(forward_emeg, meg=True, eeg=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 for MEG and EEGMEG
inv_emeg = mne.minimum_norm.make_inverse_operator(
    info=evoked.info, forward=forward_emeg, noise_cov=noise_cov, loose=0.0, depth=None
)

inv_meg = mne.minimum_norm.make_inverse_operator(
    info=evoked.info, forward=forward_meg, noise_cov=noise_cov, loose=0.0, depth=None
)

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

EEGMEG#

Compute resolution matrices, localization error, and spatial deviations for MNE:

rm_emeg = make_inverse_resolution_matrix(
    forward_emeg, inv_emeg, method="MNE", lambda2=lambda2
)
ple_psf_emeg = resolution_metrics(
    rm_emeg, inv_emeg["src"], function="psf", metric="peak_err"
)
sd_psf_emeg = resolution_metrics(
    rm_emeg, inv_emeg["src"], function="psf", metric="sd_ext"
)
del rm_emeg

MEG#

Do the same for MEG:

rm_meg = make_inverse_resolution_matrix(
    forward_meg, inv_meg, method="MNE", lambda2=lambda2
)
ple_psf_meg = resolution_metrics(
    rm_meg, inv_meg["src"], function="psf", metric="peak_err"
)
sd_psf_meg = resolution_metrics(rm_meg, inv_meg["src"], function="psf", metric="sd_ext")
del rm_meg

Visualization#

Look at peak localisation error (PLE) across the whole cortex for PSF:

brain_ple_emeg = ple_psf_emeg.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=1,
    clim=dict(kind="value", lims=(0, 2, 4)),
)

brain_ple_emeg.add_text(0.1, 0.9, "PLE PSF EMEG", "title", font_size=16)

For MEG only:

brain_ple_meg = ple_psf_meg.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=2,
    clim=dict(kind="value", lims=(0, 2, 4)),
)

brain_ple_meg.add_text(0.1, 0.9, "PLE PSF MEG", "title", font_size=16)

Subtract the two distributions and plot this difference:

diff_ple = ple_psf_emeg - ple_psf_meg

brain_ple_diff = diff_ple.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=3,
    clim=dict(kind="value", pos_lims=(0.0, 0.5, 1.0)),
    smoothing_steps=20,
)

brain_ple_diff.add_text(0.1, 0.9, "PLE EMEG-MEG", "title", font_size=16)

These plots show that with respect to peak localization error, adding EEG to MEG does not bring much benefit. Next let’s visualise spatial deviation (SD) across the whole cortex for PSF:

brain_sd_emeg = sd_psf_emeg.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=4,
    clim=dict(kind="value", lims=(0, 2, 4)),
)

brain_sd_emeg.add_text(0.1, 0.9, "SD PSF EMEG", "title", font_size=16)

For MEG only:

brain_sd_meg = sd_psf_meg.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=5,
    clim=dict(kind="value", lims=(0, 2, 4)),
)

brain_sd_meg.add_text(0.1, 0.9, "SD PSF MEG", "title", font_size=16)

Subtract the two distributions and plot this difference:

diff_sd = sd_psf_emeg - sd_psf_meg

brain_sd_diff = diff_sd.plot(
    "sample",
    "inflated",
    "lh",
    subjects_dir=subjects_dir,
    figure=6,
    clim=dict(kind="value", pos_lims=(0.0, 0.5, 1.0)),
    smoothing_steps=20,
)

brain_sd_diff.add_text(0.1, 0.9, "SD EMEG-MEG", "title", font_size=16)

Adding EEG to MEG decreases the spatial extent of point-spread functions (lower spatial deviation, blue colors), thus increasing resolution, especially for deeper source locations.

References#

Estimated memory usage: 0 MB

Gallery generated by Sphinx-Gallery