Generate a left cerebellum volume source space

Generate a volume source space of the left cerebellum and plot its vertices relative to the left cortical surface source space and the FreeSurfer segmentation file.

# Author: Alan Leggitt <alan.leggitt@ucsf.edu>
#
# License: BSD-3-Clause

import os.path as op

import mne
from mne import setup_source_space, setup_volume_source_space
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
subject = 'sample'
aseg_fname = op.join(subjects_dir, 'sample', 'mri', 'aseg.mgz')

Setup the source spaces

# setup a cortical surface source space and extract left hemisphere
surf = setup_source_space(subject, subjects_dir=subjects_dir, add_dist=False)
lh_surf = surf[0]

# setup a volume source space of the left cerebellum cortex
volume_label = 'Left-Cerebellum-Cortex'
sphere = (0, 0, 0, 0.12)
lh_cereb = setup_volume_source_space(
    subject, mri=aseg_fname, sphere=sphere, volume_label=volume_label,
    subjects_dir=subjects_dir, sphere_units='m')

# Combine the source spaces
src = surf + lh_cereb

Out:

Setting up the source space with the following parameters:

SUBJECTS_DIR = /home/circleci/mne_data/MNE-sample-data/subjects
Subject      = sample
Surface      = white
Octahedron subdivision grade 6

>>> 1. Creating the source space...

Doing the octahedral vertex picking...
Loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/lh.white...
Mapping lh sample -> oct (6) ...
    Triangle neighbors and vertex normals...
Loading geometry from /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/lh.sphere...
Setting up the triangulation for the decimated surface...
loaded lh.white 4098/155407 selected to source space (oct = 6)

Loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/rh.white...
Mapping rh sample -> oct (6) ...
    Triangle neighbors and vertex normals...
Loading geometry from /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/rh.sphere...
Setting up the triangulation for the decimated surface...
loaded rh.white 4098/156866 selected to source space (oct = 6)

You are now one step closer to computing the gain matrix
Sphere                : origin at (0.0 0.0 0.0) mm
              radius  : 120.0 mm
grid                  : 5.0 mm
mindist               : 5.0 mm
MRI volume            : /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz

Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...

Setting up the sphere...
Surface CM = (   0.0    0.0    0.0) mm
Surface fits inside a sphere with radius  120.0 mm
Surface extent:
    x = -120.0 ...  120.0 mm
    y = -120.0 ...  120.0 mm
    z = -120.0 ...  120.0 mm
Grid extent:
    x = -125.0 ...  125.0 mm
    y = -125.0 ...  125.0 mm
    z = -125.0 ...  125.0 mm
132651 sources before omitting any.
57769 sources after omitting infeasible sources not within 0.0 - 120.0 mm.
50733 sources remaining after excluding the sources outside the surface and less than    5.0 mm inside.
    Selected 401 voxels from Left-Cerebellum-Cortex
Adjusting the neighborhood info.
Source space : MRI voxel -> MRI (surface RAS)
     0.005000  0.000000  0.000000    -125.00 mm
     0.000000  0.005000  0.000000    -125.00 mm
     0.000000  0.000000  0.005000    -125.00 mm
     0.000000  0.000000  0.000000       1.00
MRI volume : MRI voxel -> MRI (surface RAS)
    -0.001000  0.000000  0.000000     128.00 mm
     0.000000  0.000000  0.001000    -128.00 mm
     0.000000 -0.001000  0.000000     128.00 mm
     0.000000  0.000000  0.000000       1.00
MRI volume : MRI (surface RAS) -> RAS (non-zero origin)
     1.000000 -0.000000 -0.000000      -5.27 mm
    -0.000000  1.000000 -0.000000       9.04 mm
    -0.000000  0.000000  1.000000     -27.29 mm
     0.000000  0.000000  0.000000       1.00
Setting up volume interpolation ...
    556177/16777216 nonzero values for Left-Cerebellum-Cortex
[done]

Plot the positions of each source space

fig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir,
                             surfaces='white', coord_frame='mri',
                             src=src)
mne.viz.set_3d_view(fig, azimuth=180, elevation=90,
                    distance=0.30, focalpoint=(-0.03, -0.01, 0.03))

You can export source positions to a NIfTI file:

>>> nii_fname = 'mne_sample_lh-cerebellum-cortex.nii'
>>> src.export_volume(nii_fname, mri_resolution=True)

And display source positions in freeview:

>>> from mne.utils import run_subprocess
>>> mri_fname = subjects_dir + '/sample/mri/brain.mgz'
>>> run_subprocess(['freeview', '-v', mri_fname, '-v',
                    '%s:colormap=lut:opacity=0.5' % aseg_fname, '-v',
                    '%s:colormap=jet:colorscale=0,2' % nii_fname,
                    '-slice', '157 75 105'])

Estimated memory usage: 155 MB

previous

Display sensitivity maps for EEG and MEG sensors

next

Use source space morphing