Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

See [1] for details.

# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#         Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne
from mne.datasets import sample
from mne.inverse_sparse import gamma_map, make_stc_from_dipoles
from mne.viz import (
    plot_dipole_amplitudes,
    plot_dipole_locations,
    plot_sparse_source_estimates,
)

data_path = sample.data_path()
subjects_dir = data_path / "subjects"
meg_path = data_path / "MEG" / "sample"
fwd_fname = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
evoked_fname = meg_path / "sample_audvis-ave.fif"
cov_fname = meg_path / "sample_audvis-cov.fif"

# Read the evoked response and crop it
condition = "Left visual"
evoked = mne.read_evokeds(evoked_fname, condition=condition, baseline=(None, 0))
evoked.crop(tmin=-50e-3, tmax=300e-3)

# Read the forward solution
forward = mne.read_forward_solution(fwd_fname)

# Read noise noise covariance matrix and regularize it
cov = mne.read_cov(cov_fname)
cov = mne.cov.regularize(cov, evoked.info, rank=None)

# Run the Gamma-MAP method with dipole output
alpha = 0.5
dipoles, residual = gamma_map(
    evoked,
    forward,
    cov,
    alpha,
    xyz_same_gamma=True,
    return_residual=True,
    return_as_dipoles=True,
)

Plot dipole activations

plot_dipole_amplitudes(dipoles)

# Plot dipole location of the strongest dipole with MRI slices
idx = np.argmax([np.max(np.abs(dip.amplitude)) for dip in dipoles])
plot_dipole_locations(
    dipoles[idx],
    forward["mri_head_t"],
    "sample",
    subjects_dir=subjects_dir,
    mode="orthoview",
    idx="amplitude",
)

# # Plot dipole locations of all dipoles with MRI slices
# for dip in dipoles:
#     plot_dipole_locations(dip, forward['mri_head_t'], 'sample',
#                           subjects_dir=subjects_dir, mode='orthoview',
#                           idx='amplitude')

Show the evoked response and the residual for gradiometers

ylim = dict(grad=[-120, 120])
evoked.pick(picks="grad", exclude="bads")
evoked.plot(
    titles=dict(grad="Evoked Response Gradiometers"),
    ylim=ylim,
    proj=True,
    time_unit="s",
)

residual.pick(picks="grad", exclude="bads")
residual.plot(
    titles=dict(grad="Residuals Gradiometers"), ylim=ylim, proj=True, time_unit="s"
)

Generate stc from dipoles

stc = make_stc_from_dipoles(dipoles, forward["src"])

View in 2D and 3D (“glass” brain like 3D plot) Show the sources as spheres scaled by their strength

scale_factors = np.max(np.abs(stc.data), axis=1)
scale_factors = 0.5 * (1 + scale_factors / np.max(scale_factors))

plot_sparse_source_estimates(
    forward["src"],
    stc,
    bgcolor=(1, 1, 1),
    modes=["sphere"],
    opacity=0.1,
    scale_factors=(scale_factors, None),
    fig_name="Gamma-MAP",
)

References

[1]

David Wipf and Srikantan Nagarajan. A unified Bayesian framework for MEG/EEG source imaging. NeuroImage, 44(3):947–966, 2009. doi:10.1016/j.neuroimage.2008.02.059.

Estimated memory usage: 0 MB