Morph volumetric source estimate

This example demonstrates how to morph an individual subject’s mne.VolSourceEstimate to a common reference space. We achieve this using mne.SourceMorph. Data will be morphed based on an affine transformation and a nonlinear registration method known as Symmetric Diffeomorphic Registration (SDR) by 1.

Transformation is estimated from the subject’s anatomical T1 weighted MRI (brain) to FreeSurfer’s ‘fsaverage’ T1 weighted MRI (brain).

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.

# Author: Tommy Clausner <>
# License: BSD (3-clause)
import os

import nibabel as nib
import mne
from mne.datasets import sample, fetch_fsaverage
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain


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',
fetch_fsaverage(subjects_dir)  # ensure fsaverage src exists
fname_src_fsaverage = subjects_dir + '/fsaverage/bem/fsaverage-vol-5-src.fif'


0 files missing from root.txt in /home/circleci/mne_data/MNE-sample-data/subjects
0 files missing from bem.txt in /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage

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)


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...
    Reading inverse operator decomposition...
    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...
    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)...
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...

Get a SourceMorph object for VolSourceEstimate

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 zooms 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.

The recommended way to use this is to morph to a specific destination source space so that different subject_from morphs will go to the same space.` A standard usage for volumetric data reads:

src_fs = mne.read_source_spaces(fname_src_fsaverage)
morph = mne.compute_source_morph(
    inverse_operator['src'], subject_from='sample', subjects_dir=subjects_dir,
    niter_affine=[10, 10, 5], niter_sdr=[10, 10, 5],  # just for speed
    src_to=src_fs, verbose=True)


    Reading a source space...
    1 source spaces read
Volume source space(s) present...
    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 translation:
    Optimizing level 2 [max iter: 10]
    Optimizing level 1 [max iter: 10]
    Optimizing level 0 [max iter: 5]
Optimizing rigid-body:
    Optimizing level 2 [max iter: 10]
    Optimizing level 1 [max iter: 10]
    Optimizing level 0 [max iter: 5]
    Translation:   22.7 mm
    Rotation:      20.7°
    R²:            96.5%
Optimizing full affine:
    Optimizing level 2 [max iter: 10]
    Optimizing level 1 [max iter: 10]
    Optimizing level 0 [max iter: 5]
    R²:            96.9%
Optimizing SDR:
    R²:            99.0%

Apply morph to VolSourceEstimate

The morph can be applied to the source estimate data, by giving it as the first argument to the morph.apply() method.


Volumetric morphing is much slower than surface morphing because the volume for each time point is individually resampled and SDR morphed. The mne.SourceMorph.compute_vol_morph_mat() method can be used to compute an equivalent sparse matrix representation by computing the transformation for each source point individually. This generally takes a few minutes to compute, but can be saved to disk and be reused. The resulting sparse matrix operation is very fast (about 400× faster) to apply. This approach is more efficient when the number of time points to be morphed exceeds the number of source space points, which is generally in the thousands. This can easily occur when morphing many time points and multiple conditions.


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Convert morphed VolSourceEstimate into NIfTI

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')


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Plot results

# 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',

# Add functional data as overlay
display.add_overlay(img_fsaverage, alpha=0.75)
morph volume stc

Reading and writing SourceMorph from and to disk

An instance of SourceMorph can be saved, by calling

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:


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, e.g.:



Brian B. Avants, Charles L. Epstein, Murray C. Grossman, and James C. Gee. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26–41, 2008. doi:10.1016/

Total running time of the script: ( 0 minutes 24.296 seconds)

Estimated memory usage: 779 MB

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