Source localization with equivalent current dipole (ECD) fit#

This shows how to fit a dipole [1] using MNE-Python.

For a comparison of fits between MNE-C and MNE-Python, see this gist.

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt
import numpy as np
from nilearn.datasets import load_mni152_template
from nilearn.plotting import plot_anat

import mne
from mne.evoked import combine_evoked
from mne.forward import make_forward_dipole
from mne.simulation import simulate_evoked

data_path = mne.datasets.sample.data_path()
subjects_dir = data_path / "subjects"
fname_ave = data_path / "MEG" / "sample" / "sample_audvis-ave.fif"
fname_cov = data_path / "MEG" / "sample" / "sample_audvis-cov.fif"
fname_bem = subjects_dir / "sample" / "bem" / "sample-5120-bem-sol.fif"
fname_trans = data_path / "MEG" / "sample" / "sample_audvis_raw-trans.fif"
fname_surf_lh = subjects_dir / "sample" / "surf" / "lh.white"

Let’s localize the N100m (using MEG only)

evoked = mne.read_evokeds(fname_ave, condition="Right Auditory", baseline=(None, 0))
evoked.pick(picks="meg")
evoked_full = evoked.copy()
evoked.crop(0.07, 0.08)

# Fit a dipole
dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]

# Plot the result in 3D brain with the MRI image.
dip.plot_locations(fname_trans, "sample", subjects_dir, mode="orthoview")

We can also plot the result using outlines of the head and brain.

color = ["k"] * len(dip)
color[np.argmax(dip.gof)] = "r"
dip.plot_locations(fname_trans, "sample", subjects_dir, mode="outlines", color=color)

Plot the result in 3D brain with the MRI image using Nilearn In MRI coordinates and in MNI coordinates (template brain)

subject = "sample"
mni_pos = dip.to_mni(subject=subject, trans=fname_trans, subjects_dir=subjects_dir)

mri_pos = dip.to_mri(subject=subject, trans=fname_trans, subjects_dir=subjects_dir)

# Find an anatomical label for the best fitted dipole
best_dip_idx = dip.gof.argmax()
label = dip.to_volume_labels(
    fname_trans, subject=subject, subjects_dir=subjects_dir, aseg="aparc.a2009s+aseg"
)[best_dip_idx]

# Draw dipole position on MRI scan and add anatomical label from parcellation
t1_fname = subjects_dir / subject / "mri" / "T1.mgz"
fig_T1 = plot_anat(t1_fname, cut_coords=mri_pos[0], title=f"Dipole location: {label}")

try:
    template = load_mni152_template(resolution=1)
except TypeError:  # in nilearn < 0.8.1 this did not exist
    template = load_mni152_template()
fig_template = plot_anat(
    template, cut_coords=mni_pos[0], title="Dipole loc. (MNI Space)"
)

Calculate and visualise magnetic field predicted by dipole with maximum GOF and compare to the measured data, highlighting the ipsilateral (right) source

fwd, stc = make_forward_dipole(dip, fname_bem, evoked.info, fname_trans)
pred_evoked = simulate_evoked(fwd, stc, evoked.info, cov=None, nave=np.inf)

# find time point with highest GOF to plot
best_idx = np.argmax(dip.gof)
best_time = dip.times[best_idx]
print(
    f"Highest GOF {dip.gof[best_idx]:0.1f}% at t={best_time * 1000:0.1f} ms with "
    f"confidence volume {dip.conf['vol'][best_idx] * 100**3:0.1f} cm^3"
)
# remember to create a subplot for the colorbar
fig, axes = plt.subplots(
    nrows=1,
    ncols=4,
    figsize=[10.0, 3.4],
    gridspec_kw=dict(width_ratios=[1, 1, 1, 0.1], top=0.85),
    layout="constrained",
)
vmin, vmax = -400, 400  # make sure each plot has same colour range

# first plot the topography at the time of the best fitting (single) dipole
plot_params = dict(times=best_time, ch_type="mag", outlines="head", colorbar=False)
evoked.plot_topomap(time_format="Measured field", axes=axes[0], **plot_params)

# compare this to the predicted field
pred_evoked.plot_topomap(time_format="Predicted field", axes=axes[1], **plot_params)

# Subtract predicted from measured data (apply equal weights)
diff = combine_evoked([evoked, pred_evoked], weights=[1, -1])
plot_params["colorbar"] = True
diff.plot_topomap(time_format="Difference", axes=axes[2:], **plot_params)
fig.suptitle(
    f"Comparison of measured and predicted fields at {best_time * 1000:.0f} ms",
    fontsize=16,
)

Estimate the time course of a single dipole with fixed position and orientation (the one that maximized GOF) over the entire interval

dip_fixed = mne.fit_dipole(
    evoked_full,
    fname_cov,
    fname_bem,
    fname_trans,
    pos=dip.pos[best_idx],
    ori=dip.ori[best_idx],
)[0]
dip_fixed.plot()

References#

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