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%matplotlib inline

Source localization with MNE/dSPM/sLORETA

The aim of this tutorial is to teach you how to compute and apply a linear inverse method such as MNE/dSPM/sLORETA on evoked/raw/epochs data.



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import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets import sample
from mne.minimum_norm import (make_inverse_operator, apply_inverse,
                              write_inverse_operator)

# sphinx_gallery_thumbnail_number = 9

Process MEG data



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data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

raw = mne.io.read_raw_fif(raw_fname)  # already has an average reference
events = mne.find_events(raw, stim_channel='STI 014')

event_id = dict(aud_r=1)  # event trigger and conditions
tmin = -0.2  # start of each epoch (200ms before the trigger)
tmax = 0.5  # end of each epoch (500ms after the trigger)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                       exclude='bads')
baseline = (None, 0)  # means from the first instant to t = 0
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)

epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
                    baseline=baseline, reject=reject)

Compute regularized noise covariance

For more details see tut_compute_covariance.



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noise_cov = mne.compute_covariance(
    epochs, tmax=0., method=['shrunk', 'empirical'])

fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)

Compute the evoked response



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evoked = epochs.average()
evoked.plot()
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag')

# Show whitening
evoked.plot_white(noise_cov)

Inverse modeling: MNE/dSPM on evoked and raw data



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# Read the forward solution and compute the inverse operator

fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fname_fwd)
fwd = mne.convert_forward_solution(fwd, surf_ori=True)

# Restrict forward solution as necessary for MEG
fwd = mne.pick_types_forward(fwd, meg=True, eeg=False)

# make an MEG inverse operator
info = evoked.info
inverse_operator = make_inverse_operator(info, fwd, noise_cov,
                                         loose=0.2, depth=0.8)

write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',
                       inverse_operator)

Compute inverse solution



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method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc = apply_inverse(evoked, inverse_operator, lambda2,
                    method=method, pick_ori=None)

del fwd, epochs  # to save memory

Visualization

View activation time-series



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plt.figure()
plt.plot(1e3 * stc.times, stc.data[::100, :].T)
plt.xlabel('time (ms)')
plt.ylabel('%s value' % method)
plt.show()

Here we use peak getter to move visualization to the time point of the peak and draw a marker at the maximum peak vertex.



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vertno_max, time_max = stc.get_peak(hemi='rh')

subjects_dir = data_path + '/subjects'
brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir,
                 clim=dict(kind='value', lims=[8, 12, 15]),
                 initial_time=time_max, time_unit='s')
brain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',
               scale_factor=0.6)
brain.show_view('lateral')

Morph data to average brain



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fs_vertices = [np.arange(10242)] * 2  # fsaverage is special this way
morph_mat = mne.compute_morph_matrix('sample', 'fsaverage', stc.vertices,
                                     fs_vertices, smooth=None,
                                     subjects_dir=subjects_dir)
stc_fsaverage = stc.morph_precomputed('fsaverage', fs_vertices, morph_mat)
brain_fsaverage = stc_fsaverage.plot(
    surface='inflated', hemi='rh', subjects_dir=subjects_dir,
    clim=dict(kind='value', lims=[8, 12, 15]), initial_time=time_max,
    time_unit='s', size=(800, 800), smoothing_steps=5)
brain_fsaverage.show_view('lateral')

Dipole orientations

The pick_ori parameter of the :func:mne.minimum_norm.apply_inverse function controls the orientation of the dipoles. One useful setting is pick_ori='vector', which will return an estimate that does not only contain the source power at each dipole, but also the orientation of the dipoles.



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stc_vec = apply_inverse(evoked, inverse_operator, lambda2,
                        method=method, pick_ori='vector')
stc_vec.plot(hemi='rh', subjects_dir=subjects_dir,
             clim=dict(kind='value', lims=[8, 12, 15]),
             initial_time=time_max, time_unit='s')

Note that there is a relationship between the orientation of the dipoles and the surface of the cortex. For this reason, we do not use an inflated cortical surface for visualization, but the original surface used to define the source space.

For more information about dipole orientations, see tut_dipole_orentiations.

Exercise

By changing the method parameter to 'sloreta' recompute the source estimates using the sLORETA method.