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%matplotlib inline
This example demonstrates how to morph an individual subject's
:class:mne.VolSourceEstimate
to a common reference space. We achieve this
using :class: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)
<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.
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# Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
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
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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
sphx_glr_auto_examples_inverse_plot_compute_mne_inverse_volume.py
Load data:
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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)
subject_from
can typically be inferred from
:class:src <mne.SourceSpaces>
,
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 :class: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:
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morph = mne.compute_source_morph(inverse_operator['src'],
subject_from='sample', subject_to='fsaverage',
subjects_dir=subjects_dir)
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stc_fsaverage = morph.apply(stc)
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# Create mri-resolution volume of results
img_fsaverage = morph.apply(stc, mri_resolution=2, output='nifti1')
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# 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
:meth:morph.save <mne.SourceMorph.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
:func: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 :func:mne.compute_source_morph
and
:meth: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:
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stc_fsaverage_new = mne.compute_source_morph(
inverse_operator['src'], subject_from='sample',
subjects_dir=subjects_dir).apply(stc)