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%matplotlib inline

Visualize Source time courses

This tutorial focuses on visualization of stcs.

Surface Source Estimates

First, we get the paths for the evoked data and the time courses (stcs).



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import os

import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from mne import read_evokeds

data_path = sample.data_path()
sample_dir = os.path.join(data_path, 'MEG', 'sample')
subjects_dir = os.path.join(data_path, 'subjects')

fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
fname_stc = os.path.join(sample_dir, 'sample_audvis-meg')

Then, we read the stc from file



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stc = mne.read_source_estimate(fname_stc, subject='sample')

This is a :class:SourceEstimate <mne.SourceEstimate> object



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print(stc)

The SourceEstimate object is in fact a surface source estimate. MNE also supports volume-based source estimates but more on that later.

We can plot the source estimate using the :func:stc.plot <mne.SourceEstimate.plot> just as in other MNE objects. Note that for this visualization to work, you must have mayavi and pysurfer installed on your machine.



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initial_time = 0.1
stc.plot(subjects_dir=subjects_dir, initial_time=initial_time)

Note that here we used initial_time=0.1, but we can also browse through time using time_viewer=True.

In case mayavi is not available, we also offer a matplotlib backend. Here we use verbose='error' to ignore a warning that not all vertices were used in plotting.



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stc.plot(subjects_dir=subjects_dir, initial_time=initial_time,
         backend='matplotlib', verbose='error')

Volume Source Estimates

We can also visualize volume source estimates (used for deep structures).

Let us load the sensor-level evoked data. We select the MEG channels to keep things simple.



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evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
evoked.pick_types(meg=True, eeg=False)

Then, we can load the precomputed inverse operator from a file.



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fname_inv = data_path + '/MEG/sample/sample_audvis-meg-vol-7-meg-inv.fif'
inv = read_inverse_operator(fname_inv)
src = inv['src']

The source estimate is computed using the inverse operator and the sensor-space data.



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snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM"  # use dSPM method (could also be MNE or sLORETA)
stc = apply_inverse(evoked, inv, lambda2, method)
stc.crop(0.0, 0.2)

This time, we have a different container (:class:VolSourceEstimate <mne.VolSourceEstimate>) for the source time course.



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print(stc)

This too comes with a convenient plot method.



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stc.plot(src, subject='sample', subjects_dir=subjects_dir)

For this visualization, nilearn must be installed. This visualization is interactive. Click on any of the anatomical slices to explore the time series. Clicking on any time point will bring up the corresponding anatomical map.

We could visualize the source estimate on a glass brain. Unlike the previous visualization, a glass brain does not show us one slice but what we would see if the brain was transparent like glass.



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stc.plot(src, subject='sample', subjects_dir=subjects_dir, mode='glass_brain')

Vector Source Estimates

If we choose to use pick_ori='vector' in :func:apply_inverse <mne.minimum_norm.apply_inverse>



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fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'

inv = read_inverse_operator(fname_inv)
stc = apply_inverse(evoked, inv, lambda2, 'dSPM', pick_ori='vector')
stc.plot(subject='sample', subjects_dir=subjects_dir,
         initial_time=initial_time)

Dipole fits

For computing a dipole fit, we need to load the noise covariance, the BEM solution, and the coregistration transformation files. Note that for the other methods, these were already used to generate the inverse operator.



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fname_cov = os.path.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_bem = os.path.join(subjects_dir, 'sample', 'bem',
                         'sample-5120-bem-sol.fif')
fname_trans = os.path.join(data_path, 'MEG', 'sample',
                           'sample_audvis_raw-trans.fif')

Dipoles are fit independently for each time point, so let us crop our time series to visualize the dipole fit for the time point of interest.



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evoked.crop(0.1, 0.1)
dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]

Finally, we can visualize the dipole.



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dip.plot_locations(fname_trans, 'sample', subjects_dir)