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%matplotlib inline

Compute MNE inverse solution on evoked data in a mixed source space

Create a mixed source space and compute an MNE inverse solution on an evoked dataset.

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# Author: Annalisa Pascarella <>
# License: BSD (3-clause)

import os.path as op
import matplotlib.pyplot as plt

from nilearn import plotting

import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse

# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
data_dir = op.join(data_path, 'MEG', subject)
subjects_dir = op.join(data_path, 'subjects')
bem_dir = op.join(subjects_dir, subject, 'bem')

# Set file names
fname_mixed_src = op.join(bem_dir, '%s-oct-6-mixed-src.fif' % subject)
fname_aseg = op.join(subjects_dir, subject, 'mri', 'aseg.mgz')

fname_model = op.join(bem_dir, '%s-5120-bem.fif' % subject)
fname_bem = op.join(bem_dir, '%s-5120-bem-sol.fif' % subject)

fname_evoked = data_dir + '/sample_audvis-ave.fif'
fname_trans = data_dir + '/sample_audvis_raw-trans.fif'
fname_fwd = data_dir + '/sample_audvis-meg-oct-6-mixed-fwd.fif'
fname_cov = data_dir + '/sample_audvis-shrunk-cov.fif'

Set up our source space

List substructures we are interested in. We select only the sub structures we want to include in the source space:

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labels_vol = ['Left-Amygdala',

Get a surface-based source space, here with few source points for speed in this demonstration, in general you should use oct6 spacing!

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src = mne.setup_source_space(subject, spacing='oct5',
                             add_dist=False, subjects_dir=subjects_dir)

Now we create a mixed src space by adding the volume regions specified in the list labels_vol. First, read the aseg file and the source space bounds using the inner skull surface (here using 10mm spacing to save time, we recommend something smaller like 5.0 in actual analyses):

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vol_src = mne.setup_volume_source_space(
    subject, mri=fname_aseg, pos=10.0, bem=fname_model,
    volume_label=labels_vol, subjects_dir=subjects_dir,
    add_interpolator=False,  # just for speed, usually this should be True

# Generate the mixed source space
src += vol_src

# Visualize the source space.

n = sum(src[i]['nuse'] for i in range(len(src)))
print('the src space contains %d spaces and %d points' % (len(src), n))

Viewing the source space

We could write the mixed source space with::

write_source_spaces(fname_mixed_src, src, overwrite=True)

We can also export source positions to nifti file and visualize it again:

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nii_fname = op.join(bem_dir, '%s-mixed-src.nii' % subject)
src.export_volume(nii_fname, mri_resolution=True, overwrite=True)
plotting.plot_img(nii_fname, cmap='nipy_spectral')

Compute the fwd matrix

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fwd = mne.make_forward_solution(
    fname_evoked, fname_trans, src, fname_bem,
    mindist=5.0,  # ignore sources<=5mm from innerskull
    meg=True, eeg=False, n_jobs=1)

leadfield = fwd['sol']['data']
print("Leadfield size : %d sensors x %d dipoles" % leadfield.shape)

src_fwd = fwd['src']
n = sum(src_fwd[i]['nuse'] for i in range(len(src_fwd)))
print('the fwd src space contains %d spaces and %d points' % (len(src_fwd), n))

# Load data
condition = 'Left Auditory'
evoked = mne.read_evokeds(fname_evoked, condition=condition,
                          baseline=(None, 0))
noise_cov = mne.read_cov(fname_cov)

Compute inverse solution

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snr = 3.0            # use smaller SNR for raw data
inv_method = 'dSPM'  # sLORETA, MNE, dSPM
parc = 'aparc'       # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'

lambda2 = 1.0 / snr ** 2

inverse_operator = make_inverse_operator(, fwd, noise_cov,
                                         depth=None, fixed=False)

stc = apply_inverse(evoked, inverse_operator, lambda2, inv_method,
src = inverse_operator['src']

Plot the surface

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initial_time = 0.1
brain = stc.surface().plot(initial_time=initial_time,

Plot the volume

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fig = stc.volume().plot(initial_time=initial_time, src=src,

Process labels

Average the source estimates within each label of the cortical parcellation and each sub structure contained in the src space

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# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels_parc = mne.read_labels_from_annot(
    subject, parc=parc, subjects_dir=subjects_dir)

label_ts = mne.extract_label_time_course(
    [stc], labels_parc, src, mode='mean', allow_empty=True)

# plot the times series of 2 labels
fig, axes = plt.subplots(1)
axes.plot(1e3 * stc.times, label_ts[0][0, :], 'k', label='bankssts-lh')
axes.plot(1e3 * stc.times, label_ts[0][71, :].T, 'r', label='Brain-stem')
axes.set(xlabel='Time (ms)', ylabel='MNE current (nAm)')