Plotting with mne.viz.Brain#

In this example, we’ll show how to use mne.viz.Brain.

# Author: Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause

Load data#

In this example we use the sample data which is data from a subject being presented auditory and visual stimuli to display the functionality of mne.viz.Brain for plotting data on a brain.

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

import mne
from mne.datasets import sample

print(__doc__)

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

Add source information#

Plot source information.

brain_kwargs = dict(alpha=0.1, background='white', cortex='low_contrast')
brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)

stc = mne.read_source_estimate(op.join(sample_dir, 'sample_audvis-meg'))
stc.crop(0.09, 0.1)

kwargs = dict(fmin=stc.data.min(), fmax=stc.data.max(), alpha=0.25,
              smoothing_steps='nearest', time=stc.times)
brain.add_data(stc.lh_data, hemi='lh', vertices=stc.lh_vertno, **kwargs)
brain.add_data(stc.rh_data, hemi='rh', vertices=stc.rh_vertno, **kwargs)
brain

Modify the view of the brain#

You can adjust the view of the brain using show_view method.

brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)
brain.show_view(azimuth=190, elevation=70, distance=350, focalpoint=(0, 0, 20))
brain

Highlight a region on the brain#

It can be useful to highlight a region of the brain for analyses. To highlight a region on the brain you can use the add_label method. Labels are stored in the Freesurfer label directory from the recon-all for that subject. Labels can also be made following the Freesurfer instructions Here we will show Brodmann Area 44.

Note

The MNE sample dataset contains only a subselection of the Freesurfer labels created during the recon-all.

brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)
brain.add_label('BA44', hemi='lh', color='green', borders=True)
brain.show_view(azimuth=190, elevation=70, distance=350, focalpoint=(0, 0, 20))
brain

Include the head in the image#

Add a head image using the add_head method.

brain
Using lh.seghead for head surface.

Add sensors positions#

To put into context the data that generated the source time course, the sensor positions can be displayed as well.

brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)
evoked = mne.read_evokeds(op.join(sample_dir, 'sample_audvis-ave.fif'))[0]
trans = mne.read_trans(op.join(sample_dir, 'sample_audvis_raw-trans.fif'))
brain.add_sensors(evoked.info, trans)
brain.show_view(distance=500)  # move back to show sensors
brain
Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right Auditory)
        0 CTF compensation matrices available
        nave = 61 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left visual)
        0 CTF compensation matrices available
        nave = 67 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Right visual)
        0 CTF compensation matrices available
        nave = 58 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
Channel types:: grad: 203, mag: 102, eeg: 59
Getting helmet for system 306m

Add current dipoles#

Dipole modeling as in The role of dipole orientations in distributed source localization can be plotted on the brain as well.

brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)
dip = mne.read_dipole(op.join(sample_dir, 'sample_audvis_set1.dip'))
cmap = plt.get_cmap('YlOrRd')
colors = [cmap(gof / dip.gof.max()) for gof in dip.gof]
brain.add_dipole(dip, trans, colors=colors, scales=list(dip.amplitude * 1e8))
brain.show_view(azimuth=-20, elevation=60, distance=300)
img = brain.screenshot()  # for next section
brain
34 dipole(s) found

Create a screenshot for exporting the brain image#

Also, we can a static image of the brain using screenshot (above), which will allow us to add a colorbar. This is useful for figures in publications.

fig, ax = plt.subplots()
ax.imshow(img)
ax.axis('off')
cax = fig.add_axes([0.9, 0.1, 0.05, 0.8])
fig.colorbar(mpl.cm.ScalarMappable(
    norm=mpl.colors.Normalize(vmin=0, vmax=dip.gof.max()), cmap=cmap), cax=cax)
fig.suptitle('Dipole Fits Scaled by Amplitude and Colored by GOF')
Dipole Fits Scaled by Amplitude and Colored by GOF

Total running time of the script: ( 1 minutes 7.128 seconds)

Estimated memory usage: 28 MB

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