mne.SourceMorph#

class mne.SourceMorph(subject_from, subject_to, kind, zooms, niter_affine, niter_sdr, spacing, smooth, xhemi, morph_mat, vertices_to, shape, affine, pre_affine, sdr_morph, src_data, vol_morph_mat, *, verbose=None)[source]#

Morph source space data from one subject to another.

Note

This class should not be instantiated directly. Use mne.compute_source_morph() instead.

Parameters

subject_fromstr | None: Name of the subject from which to morph as named in the SUBJECTS_DIR.
subject_tostr | array | list of array: Name of the subject on which to morph as named in the SUBJECTS_DIR. The default is ‘fsaverage’. If morphing a volume source space, subject_to can be the path to a MRI volume. Can also be a list of two arrays if morphing to hemisphere surfaces.
kindstr | None: Kind of source estimate. E.g. ‘volume’ or ‘surface’.
zoomsfloat | tuple: See mne.compute_source_morph().
niter_affinetuple of int: Number of levels (len(niter_affine)) and number of iterations per level - for each successive stage of iterative refinement - to perform the affine transform.
niter_sdrtuple of int: Number of levels (len(niter_sdr)) and number of iterations per level - for each successive stage of iterative refinement - to perform the Symmetric Diffeomorphic Registration (sdr) transform 1.
spacingint | list | None: See mne.compute_source_morph().
smoothint | str | None: See mne.compute_source_morph().
xhemibool: Morph across hemisphere.
morph_matscipy.sparse.csr_matrix: The sparse surface morphing matrix for spherical surface based morphing 2.
vertices_tolist of ndarray: The destination surface vertices.
shapetuple: The volume MRI shape.
affinendarray: The volume MRI affine.
pre_affineinstance of dipy.align.AffineMap: The transformation that is applied before the before sdr_morph.
sdr_morphinstance of dipy.align.DiffeomorphicMap: The class that applies the the symmetric diffeomorphic registration (SDR) morph.
src_datadict: Additional source data necessary to perform morphing.
vol_morph_matscipy.sparse.csr_matrix | None: The volumetric morph matrix, if compute_vol_morph_mat() was used.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Notes

New in version 0.17.

References

1: 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/j.media.2007.06.004.
2: Douglas N. Greve, Lise Van der Haegen, Qing Cai, Steven Stufflebeam, Mert R. Sabuncu, Bruce Fischl, and Marc Brysbaert. A surface-based analysis of language lateralization and cortical asymmetry. Journal of Cognitive Neuroscience, 25(9):1477–1492, 2013. doi:10.1162/jocn_a_00405.

Attributes

verbose

Methods

`apply`(stc_from[, output, mri_resolution, ...])	Morph source space data.
`compute_vol_morph_mat`(*[, verbose])	Compute the sparse matrix representation of the volumetric morph.
`save`(fname[, overwrite, verbose])	Save the morph for source estimates to a file.

apply(stc_from, output='stc', mri_resolution=False, mri_space=None, verbose=None)[source]#

Morph source space data.

Parameters

stc_fromVolSourceEstimate | VolVectorSourceEstimate | SourceEstimate | VectorSourceEstimate: The source estimate to morph.
outputstr: Can be ‘stc’ (default) or possibly ‘nifti1’, or ‘nifti2’ when working with a volume source space defined on a regular grid.
mri_resolutionbool | tuple | int | float: If True the image is saved in MRI resolution. Default False. WARNING: if you have many time points the file produced can be huge. The default is mri_resolution=False.
mri_spacebool | None: Whether the image to world registration should be in mri space. The default (None) is mri_space=mri_resolution.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns

stc_toVolSourceEstimate | SourceEstimate | VectorSourceEstimate | Nifti1Image | Nifti2Image: The morphed source estimates.

Examples using apply:

Source reconstruction using an LCMV beamformer

2 samples permutation test on source data with spatio-temporal clustering

Cross-hemisphere comparison

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Morph surface source estimate

Morph volumetric source estimate

compute_vol_morph_mat(*, verbose=None)[source]#

Compute the sparse matrix representation of the volumetric morph.

Parameters

verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns

morphinstance of SourceMorph: The instance (modified in-place).

Notes

For a volumetric morph, this will compute the morph for an identity source volume, i.e., with one source vertex active at a time, and store the result as a sparse morphing matrix. This takes a long time (minutes) to compute initially, but drastically speeds up apply() for STCs, so it can be beneficial when many time points or many morphs (i.e., greater than the number of volumetric src_from vertices) will be performed.

When calling save(), this sparse morphing matrix is saved with the instance, so this only needs to be called once. This function does nothing if the morph matrix has already been computed, or if there is no volume morphing necessary.

New in version 0.22.

Examples using compute_vol_morph_mat:

Morph volumetric source estimate

save(fname, overwrite=False, verbose=None)[source]#

Save the morph for source estimates to a file.

Parameters

fnamestr: The stem of the file name. ‘-morph.h5’ will be added if fname does not end with ‘.h5’.
overwritebool: If True (default False), overwrite the destination file if it exists.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.