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%matplotlib inline

.. _tut_inverse_mne_dspm:

Source localization with MNE/dSPM/sLORETA

The aim of this tutorials 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)

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)
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 :ref: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, 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, inverse_operator, epochs  # to save memory

Visualization

View activation time-series



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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_idx = stc.get_peak(hemi='rh', time_as_index=True)

subjects_dir = data_path + '/subjects'
brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir)

brain.set_data_time_index(time_idx)
brain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',
               scale_factor=0.6)
brain.scale_data_colormap(fmin=8, fmid=12, fmax=15, transparent=True)
brain.show_view('lateral')

Morph data to average brain



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stc_fsaverage = stc.morph(subject_to='fsaverage', subjects_dir=subjects_dir)

brain_fsaverage = stc_fsaverage.plot(surface='inflated', hemi='rh',
                                     subjects_dir=subjects_dir)
brain_fsaverage.set_data_time_index(time_idx)
brain_fsaverage.scale_data_colormap(fmin=8, fmid=12, fmax=15, transparent=True)
brain_fsaverage.show_view('lateral')

Exercise

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