Compute MxNE with time-frequency sparse prior#

The TF-MxNE solver is a distributed inverse method (like dSPM or sLORETA) that promotes focal (sparse) sources (such as dipole fitting techniques) [1][2]. The benefit of this approach is that:

it is spatio-temporal without assuming stationarity (sources properties can vary over time)

activations are localized in space, time and frequency in one step.

with a built-in filtering process based on a short time Fourier transform (STFT), data does not need to be low passed (just high pass to make the signals zero mean).

the solver solves a convex optimization problem, hence cannot be trapped in local minima.

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         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 make_stc_from_dipoles, tf_mixed_norm
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.viz import (
    plot_dipole_amplitudes,
    plot_dipole_locations,
    plot_sparse_source_estimates,
)

print(__doc__)

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"
ave_fname = meg_path / "sample_audvis-no-filter-ave.fif"
cov_fname = meg_path / "sample_audvis-shrunk-cov.fif"

# Read noise covariance matrix
cov = mne.read_cov(cov_fname)

# Handling average file
condition = "Left visual"
evoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))
# We make the window slightly larger than what you'll eventually be interested
# in ([-0.05, 0.3]) to avoid edge effects.
evoked.crop(tmin=-0.1, tmax=0.4)

# Handling forward solution
forward = mne.read_forward_solution(fwd_fname)

Run solver

# alpha parameter is between 0 and 100 (100 gives 0 active source)
alpha = 40.0  # general regularization parameter
# l1_ratio parameter between 0 and 1 promotes temporal smoothness
# (0 means no temporal regularization)
l1_ratio = 0.03  # temporal regularization parameter

loose, depth = 0.2, 0.9  # loose orientation & depth weighting

# Compute dSPM solution to be used as weights in MxNE
inverse_operator = make_inverse_operator(
    evoked.info, forward, cov, loose=loose, depth=depth
)
stc_dspm = apply_inverse(evoked, inverse_operator, lambda2=1.0 / 9.0, method="dSPM")

# Compute TF-MxNE inverse solution with dipole output
dipoles, residual = tf_mixed_norm(
    evoked,
    forward,
    cov,
    alpha=alpha,
    l1_ratio=l1_ratio,
    loose=loose,
    depth=depth,
    maxit=200,
    tol=1e-6,
    weights=stc_dspm,
    weights_min=8.0,
    debias=True,
    wsize=16,
    tstep=4,
    window=0.05,
    return_as_dipoles=True,
    return_residual=True,
)

# Crop to remove edges
for dip in dipoles:
    dip.crop(tmin=-0.05, tmax=0.3)
evoked.crop(tmin=-0.05, tmax=0.3)
residual.crop(tmin=-0.05, tmax=0.3)

Plot dipole activations

plot_dipole_amplitudes(dipoles)

Plot 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)

plot_sparse_source_estimates(
    forward["src"],
    stc,
    bgcolor=(1, 1, 1),
    opacity=0.1,
    fig_name=f"TF-MxNE (cond {condition})",
    modes=["sphere"],
    scale_factors=[1.0],
)

time_label = "TF-MxNE time=%0.2f ms"
clim = dict(kind="value", lims=[10e-9, 15e-9, 20e-9])
brain = stc.plot(
    "sample",
    "inflated",
    "rh",
    views="medial",
    clim=clim,
    time_label=time_label,
    smoothing_steps=5,
    subjects_dir=subjects_dir,
    initial_time=150,
    time_unit="ms",
)
brain.add_label("V1", color="yellow", scalar_thresh=0.5, borders=True)
brain.add_label("V2", color="red", scalar_thresh=0.5, borders=True)

References#

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