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
# Copyright the MNE-Python contributors.

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.

importmatplotlib.pyplotasplt
frommatplotlib.cmimport ScalarMappable
frommatplotlib.colorsimport Normalize
importmne
frommne.datasetsimport sample
print(__doc__)
data_path = sample.data_path ()
subjects_dir = data_path / "subjects"
sample_dir = 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 (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 = mne.viz.Brain ("sample", subjects_dir =subjects_dir , **brain_kwargs )
brain.add_head (alpha=0.5)

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 (sample_dir / "sample_audvis-ave.fif")[0]
trans = mne.read_trans (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 (sample_dir / "sample_audvis_set1.dip")
cmap = plt.colormaps ["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])
norm = Normalize (vmin=0, vmax=dip.gof.max ())
fig.colorbar (ScalarMappable (norm =norm , cmap =cmap ), cax =cax )
fig.suptitle ("Dipole Fits Scaled by Amplitude and Colored by GOF")