Note
Click here to download the full example code
This example demonstrates how to morph an individual subject’s
mne.VolSourceEstimate to a common reference space. We achieve this
using mne.SourceMorph. Pre-computed data will be morphed based on
an affine transformation and a nonlinear registration method
known as Symmetric Diffeomorphic Registration (SDR) by Avants et al. [1].
Transformation is estimated from the subject’s anatomical T1 weighted MRI (brain) to FreeSurfer’s ‘fsaverage’ T1 weighted MRI (brain). See https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage .
Afterwards the transformation will be applied to the volumetric source estimate. The result will be plotted, showing the fsaverage T1 weighted anatomical MRI, overlaid with the morphed volumetric source estimate.
| [1] | Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2009). Symmetric Diffeomorphic Image Registration with Cross- Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain, 12(1), 26-41. |
Note
For a tutorial about morphing see: Morphing source estimates: Moving data from one brain to another.
# Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import matplotlib.pyplot as plt
import nibabel as nib
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain
print(__doc__)
Setup paths
sample_dir_raw = sample.data_path()
sample_dir = os.path.join(sample_dir_raw, 'MEG', 'sample')
subjects_dir = os.path.join(sample_dir_raw, 'subjects')
fname_evoked = os.path.join(sample_dir, 'sample_audvis-ave.fif')
fname_inv = os.path.join(sample_dir, 'sample_audvis-meg-vol-7-meg-inv.fif')
fname_t1_fsaverage = os.path.join(subjects_dir, 'fsaverage', 'mri',
'brain.mgz')
Compute example data. For reference see Compute MNE-dSPM inverse solution on evoked data in volume source space
Load data:
evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
inverse_operator = read_inverse_operator(fname_inv)
# Apply inverse operator
stc = apply_inverse(evoked, inverse_operator, 1.0 / 3.0 ** 2, "dSPM")
# To save time
stc.crop(0.09, 0.09)
Out:
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.
Applying baseline correction (mode: mean)
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-vol-7-meg-inv.fif...
Reading inverse operator info...
[done]
Reading inverse operator decomposition...
[done]
305 x 305 full covariance (kind = 1) found.
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
Noise covariance matrix read.
11271 x 11271 diagonal covariance (kind = 2) found.
Source covariance matrix read.
Did not find the desired covariance matrix (kind = 6)
11271 x 11271 diagonal covariance (kind = 5) found.
Depth priors read.
Did not find the desired covariance matrix (kind = 3)
Reading a source space...
[done]
1 source spaces read
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
Source spaces transformed to the inverse solution coordinate frame
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 59.7% variance
Combining the current components...
dSPM...
[done]
subject_from can typically be inferred from
src,
and subject_to is set to ‘fsaverage’ by default. subjects_dir can be
None when set in the environment. In that case SourceMorph can be initialized
taking src as only argument. See mne.SourceMorph for more
details.
The default parameter setting for spacing will cause the reference volumes to be resliced before computing the transform. A value of ‘5’ would cause the function to reslice to an isotropic voxel size of 5 mm. The higher this value the less accurate but faster the computation will be.
A standard usage for volumetric data reads:
morph = mne.compute_source_morph(inverse_operator['src'],
subject_from='sample', subject_to='fsaverage',
subjects_dir=subjects_dir)
Out:
volume source space inferred...
loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/brain.mgz as "from" volume
loading /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/mri/brain.mgz as "to" volume
Computing nonlinear Symmetric Diffeomorphic Registration...
Optimizing level 2 [max iter: 100]
Optimizing level 1 [max iter: 100]
Optimizing level 0 [max iter: 10]
Optimizing level 2 [max iter: 100]
Optimizing level 1 [max iter: 100]
Optimizing level 0 [max iter: 10]
Optimizing level 2 [max iter: 100]
Optimizing level 1 [max iter: 100]
Optimizing level 0 [max iter: 10]
Creating scale space from the moving image. Levels: 3. Sigma factor: 0.200000.
Creating scale space from the static image. Levels: 3. Sigma factor: 0.200000.
Optimizing level 2
Optimizing level 1
Optimizing level 0
done.
[done]
The morph can be applied to the source estimate data, by giving it as the
first argument to the morph.apply() method:
stc_fsaverage = morph.apply(stc)
We can convert our morphed source estimate into a NIfTI volume using
morph.apply(..., output='nifti1').
# Create mri-resolution volume of results
img_fsaverage = morph.apply(stc, mri_resolution=2, output='nifti1')
# Load fsaverage anatomical image
t1_fsaverage = nib.load(fname_t1_fsaverage)
# Plot glass brain (change to plot_anat to display an overlaid anatomical T1)
display = plot_glass_brain(t1_fsaverage,
title='subject results to fsaverage',
draw_cross=False,
annotate=True)
# Add functional data as overlay
display.add_overlay(img_fsaverage, alpha=0.75)
An instance of SourceMorph can be saved, by calling
morph.save.
This methods allows for specification of a filename under which the morph
will be save in “.h5” format. If no file extension is provided, “-morph.h5”
will be appended to the respective defined filename:
>>> morph.save('my-file-name')
Reading a saved source morph can be achieved by using
mne.read_source_morph():
>>> morph = mne.read_source_morph('my-file-name-morph.h5')
Once the environment is set up correctly, no information such as
subject_from or subjects_dir must be provided, since it can be
inferred from the data and used morph to ‘fsaverage’ by default. SourceMorph
can further be used without creating an instance and assigning it to a
variable. Instead mne.compute_source_morph() and
mne.SourceMorph.apply() can be
easily chained into a handy one-liner. Taking this together the shortest
possible way to morph data directly would be:
stc_fsaverage = mne.compute_source_morph(inverse_operator['src'],
subject_from='sample',
subjects_dir=subjects_dir).apply(stc)
plt.show()
Out:
volume source space inferred...
loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/brain.mgz as "from" volume
loading /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/mri/brain.mgz as "to" volume
Computing nonlinear Symmetric Diffeomorphic Registration...
Optimizing level 2 [max iter: 100]
Optimizing level 1 [max iter: 100]
Optimizing level 0 [max iter: 10]
Optimizing level 2 [max iter: 100]
Optimizing level 1 [max iter: 100]
Optimizing level 0 [max iter: 10]
Optimizing level 2 [max iter: 100]
Optimizing level 1 [max iter: 100]
Optimizing level 0 [max iter: 10]
Creating scale space from the moving image. Levels: 3. Sigma factor: 0.200000.
Creating scale space from the static image. Levels: 3. Sigma factor: 0.200000.
Optimizing level 2
Optimizing level 1
Optimizing level 0
done.
[done]
Total running time of the script: ( 0 minutes 31.685 seconds)
Estimated memory usage: 613 MB