mne.compute_source_morph¶

mne.compute_source_morph(src, subject_from=None, subject_to='fsaverage', subjects_dir=None, zooms=5, niter_affine=(100, 100, 10), niter_sdr=(5, 5, 3), spacing=5, smooth=None, warn=True, xhemi=False, sparse=False, verbose=False)[source]¶

Create a SourceMorph from one subject to another.

Method is based on spherical morphing by FreeSurfer for surface cortical estimates [1] and Symmetric Diffeomorphic Registration for volumic data [2].

Parameters:

Parameters:	src : instance of SourceSpaces \| instance of SourceEstimate The SourceSpaces of subject_from (can be a SourceEstimate if only using a surface source space). subject_from : str \| None Name of the original subject as named in the SUBJECTS_DIR. If None (default), then `src[0]['subject_his_id]'` will be used. subject_to : str Name of the subject to which to morph as named in the SUBJECTS_DIR. subjects_dir : str \| None Path to SUBJECTS_DIR if it is not set in the environment. The default is None. zooms : float \| tuple \| None The voxel size of volume for each spatial dimension in mm. If spacing is None, MRIs won’t be resliced, and both volumes must have the same number of spatial dimensions. 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. Default is niter_affine=(100, 100, 10). 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. Default is niter_sdr=(5, 5, 3). spacing : int \| list \| None The resolution of the icosahedral mesh (typically 5). If None, all vertices will be used (potentially filling the surface). If a list, then values will be morphed to the set of vertices specified in in `spacing[0]` and `spacing[1]`. 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. The default is spacing=None. warn : bool If True, warn if not all vertices were used. The default is warn=True. xhemi : bool Morph across hemisphere. Currently only implemented for `subject_to == subject_from`. See notes below. The default is xhemi=False. sparse : bool Morph as a sparse source estimate. Works only with (Vector) SourceEstimate. If True the only parameters used are subject_to and subject_from, and spacing has to be None. Default is sparse=False. 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.

src : instance of SourceSpaces | instance of SourceEstimate: The SourceSpaces of subject_from (can be a SourceEstimate if only using a surface source space).
subject_from : str | None: Name of the original subject as named in the SUBJECTS_DIR. If None (default), then src[0]['subject_his_id]' will be used.
subject_to : str: Name of the subject to which to morph as named in the SUBJECTS_DIR.
subjects_dir : str | None: Path to SUBJECTS_DIR if it is not set in the environment. The default is None.
zooms : float | tuple | None: The voxel size of volume for each spatial dimension in mm. If spacing is None, MRIs won’t be resliced, and both volumes must have the same number of spatial dimensions.
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. Default is niter_affine=(100, 100, 10).
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. Default is niter_sdr=(5, 5, 3).
spacing : int | list | None: The resolution of the icosahedral mesh (typically 5). If None, all vertices will be used (potentially filling the surface). If a list, then values will be morphed to the set of vertices specified in in spacing[0] and spacing[1].
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. The default is spacing=None.
warn : bool: If True, warn if not all vertices were used. The default is warn=True.
xhemi : bool: Morph across hemisphere. Currently only implemented for subject_to == subject_from. See notes below. The default is xhemi=False.
sparse : bool: Morph as a sparse source estimate. Works only with (Vector) SourceEstimate. If True the only parameters used are subject_to and subject_from, and spacing has to be None. Default is sparse=False.
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.

Notes

This function can be used to morph data between hemispheres by setting xhemi=True. The full cross-hemisphere morph matrix maps left to right and right to left. A matrix for cross-mapping only one hemisphere can be constructed by specifying the appropriate vertices, for example, to map the right hemisphere to the left: vertices_from=[[], vert_rh], vertices_to=[vert_lh, []].

Cross-hemisphere mapping requires appropriate sphere.left_right morph-maps in the subject’s directory. These morph maps are included with the fsaverage_sym FreeSurfer subject, and can be created for other subjects with the mris_left_right_register FreeSurfer command. The fsaverage_sym subject is included with FreeSurfer > 5.1 and can be obtained as described here. For statistical comparisons between hemispheres, use of the symmetric fsaverage_sym model is recommended to minimize bias [1].

New in version 0.17.0.

References

[1]	(1, 2, 3) 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.