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%matplotlib inline

Source localization with MNE/dSPM/sLORETA/eLORETA

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



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# sphinx_gallery_thumbnail_number = 10

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

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_l=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'], rank=None, verbose=True)

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

Compute the evoked response

Let's just use MEG channels for simplicity.



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evoked = epochs.average().pick_types(meg=True)
evoked.plot(time_unit='s')
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag',
                    time_unit='s')

# Show whitening
evoked.plot_white(noise_cov, time_unit='s')

del epochs  # to save memory

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)

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

# You can write it to disk with::
#
#     >>> from mne.minimum_norm import write_inverse_operator
#     >>> 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, residual = apply_inverse(evoked, inverse_operator, lambda2,
                              method=method, pick_ori=None,
                              return_residual=True, verbose=True)

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()

Examine the original data and the residual after fitting:



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fig, axes = plt.subplots(2, 1)
evoked.plot(axes=axes)
for ax in axes:
    ax.texts = []
    for line in ax.lines:
        line.set_color('#98df81')
residual.plot(axes=axes)

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'
surfer_kwargs = dict(
    hemi='rh', subjects_dir=subjects_dir,
    clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',
    initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)
brain = stc.plot(**surfer_kwargs)
brain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',
               scale_factor=0.6, alpha=0.5)
brain.add_text(0.1, 0.9, 'dSPM (plus location of maximal activation)', 'title',
               font_size=14)

Morph data to average brain



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# setup source morph
morph = mne.compute_source_morph(
    src=inverse_operator['src'], subject_from=stc.subject,
    subject_to='fsaverage', spacing=5,  # to ico-5
    subjects_dir=subjects_dir)
# morph data
stc_fsaverage = morph.apply(stc)

brain = stc_fsaverage.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Morphed to fsaverage', 'title', font_size=20)
del stc_fsaverage

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')
brain = stc_vec.plot(**surfer_kwargs)
brain.add_text(0.1, 0.9, 'Vector solution', 'title', font_size=20)
del stc_vec

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 sphx_glr_auto_tutorials_plot_dipole_orientations.py.

Now let's look at each solver:



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for mi, (method, lims) in enumerate((('dSPM', [8, 12, 15]),
                                     ('sLORETA', [3, 5, 7]),
                                     ('eLORETA', [0.75, 1.25, 1.75]),)):
    surfer_kwargs['clim']['lims'] = lims
    stc = apply_inverse(evoked, inverse_operator, lambda2,
                        method=method, pick_ori=None)
    brain = stc.plot(figure=mi, **surfer_kwargs)
    brain.add_text(0.1, 0.9, method, 'title', font_size=20)
    del stc