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)[source]¶

Morph source space data from one subject to another.

Note

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

New in version 0.17.

Parameters:

Parameters:	subject_from : str \| None Name of the subject from which to morph as named in the SUBJECTS_DIR. subject_to : str \| array \| list of two arrays 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. kind : str \| None Kind of source estimate. E.g. ‘volume’ or ‘surface’. zooms : float \| tuple See `mne.compute_source_morph()`. niter_affine : tuple 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_sdr : tuple 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 [2]. spacing : int \| list \| None See `mne.compute_source_morph()`. smooth : int \| None Number of iterations for the smoothing of the surface data. If None, smooth is automatically defined to fill the surface with non-zero values. xhemi : bool Morph across hemisphere. morph_mat : scipy.sparse.csr_matrix The sparse surface morphing matrix for spherical surface based morphing [1]. vertices_to : list of ndarray The destination surface vertices. shape : tuple The volume MRI shape. affine : ndarray The volume MRI affine. pre_affine : instance of dipy.align.imaffine.AffineMap The `dipy.align.imaffine.AffineMap` transformation that is applied before the before `sdr_morph`. sdr_morph : instance of dipy.align.imwarp.DiffeomorphicMap The `dipy.align.imwarp.DiffeomorphicMap` that applies the the symmetric diffeomorphic registration (SDR) morph. src_data : dict Additional source data necessary to perform morphing.

subject_from : str | None: Name of the subject from which to morph as named in the SUBJECTS_DIR.
subject_to : str | array | list of two arrays: 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.
kind : str | None: Kind of source estimate. E.g. ‘volume’ or ‘surface’.
zooms : float | tuple: See mne.compute_source_morph().
niter_affine : tuple 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_sdr : tuple 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 [2].
spacing : int | list | None: See mne.compute_source_morph().
smooth : int | None: Number of iterations for the smoothing of the surface data. If None, smooth is automatically defined to fill the surface with non-zero values.
xhemi : bool: Morph across hemisphere.
morph_mat : scipy.sparse.csr_matrix: The sparse surface morphing matrix for spherical surface based morphing [1].
vertices_to : list of ndarray: The destination surface vertices.
shape : tuple: The volume MRI shape.
affine : ndarray: The volume MRI affine.
pre_affine : instance of dipy.align.imaffine.AffineMap: The dipy.align.imaffine.AffineMap transformation that is applied before the before sdr_morph.
sdr_morph : instance of dipy.align.imwarp.DiffeomorphicMap: The dipy.align.imwarp.DiffeomorphicMap that applies the the symmetric diffeomorphic registration (SDR) morph.
src_data : dict: Additional source data necessary to perform morphing.

References

[1]	(1, 2) Greve D. N., Van der Haegen L., Cai Q., Stufflebeam S., Sabuncu M. R., Fischl B., Brysbaert M. A Surface-based Analysis of Language Lateralization and Cortical Asymmetry. Journal of Cognitive Neuroscience 25(9), 1477-1492, 2013.

[2]	(1, 2) 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.

Methods

`__hash__`($self, /)	Return hash(self).
`apply`(stc_from[, output, mri_resolution, …])	Morph source space data.
`save`(fname[, overwrite, verbose])	Save the morph for source estimates to a file.

__hash__($self, /)¶: Return hash(self).

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

Morph source space data.

Parameters:

Parameters:	stc_from : VolSourceEstimate \| SourceEstimate \| VectorSourceEstimate The source estimate to morph. output : str Can be ‘stc’ (default), ‘nifti1’, or ‘nifti2’. mri_resolution: bool \| 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_space : bool Whether the image to world registration should be in mri space. The default is mri_space=mri_resolution. verbose : bool \| str \| int \| None If not None, override default verbose level (see `mne.verbose()` and Logging documentation for more). The default is verbose=None.
Returns:	stc_to : VolSourceEstimate \| SourceEstimate \| VectorSourceEstimate \| Nifti1Image \| Nifti2Image The morphed source estimates.

stc_from : VolSourceEstimate | SourceEstimate | VectorSourceEstimate: The source estimate to morph.
output : str: Can be ‘stc’ (default), ‘nifti1’, or ‘nifti2’.
mri_resolution: bool | 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_space : bool: Whether the image to world registration should be in mri space. The default is mri_space=mri_resolution.
verbose : bool | str | int | None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more). The default is verbose=None.

Returns:

stc_to : VolSourceEstimate | SourceEstimate | VectorSourceEstimate | Nifti1Image | Nifti2Image: The morphed source estimates.

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

Save the morph for source estimates to a file.

Parameters:	fname : str The stem of the file name. ‘-morph.h5’ will be added if fname does not end with ‘.h5’ overwrite : bool If True, overwrite existing file. verbose : bool \| str \| int \| None If not None, override default verbose level (see `mne.verbose()` and Logging documentation for more).