mne_rsa.rsa_nifti#
- mne_rsa.rsa_nifti(image, rdm_model, spatial_radius=0.01, image_rdm_metric='correlation', image_rdm_params={}, rsa_metric='spearman', ignore_nan=False, y=None, n_folds=1, roi_mask=None, brain_mask=None, n_jobs=1, verbose=False)[source]#
Perform RSA in a searchlight pattern on Nibabel Nifti-like images.
The output is a 3D Nifti image where the data at each voxel is is the RSA, computed for a patch surrounding the voxel.
New in version 0.4.
- Parameters:
- image4D Nifti-like image
The Nitfi image data. The 4th dimension must contain the images for each item.
- rdm_modelndarray, shape (n, n) | (n * (n - 1) // 2,) | list of ndarray
The model RDM, see
compute_rdm()
. For efficiency, you can give it in condensed form, meaning only the upper triangle of the matrix as a vector. Seescipy.spatial.distance.squareform()
. To perform RSA against multiple models at the same time, supply a list of model RDMs.Use
compute_rdm()
to compute RDMs.- spatial_radiusfloat
The spatial radius of the searchlight patch in meters. All source points within this radius will belong to the searchlight patch. Defaults to 0.01.
- image_rdm_metricstr
The metric to use to compute the RDM for the data. This can be any metric supported by the scipy.distance.pdist function. See also the
image_rdm_params
parameter to specify and additional parameter for the distance function. Defaults to ‘correlation’.- image_rdm_paramsdict
Extra arguments for the distance metric used to compute the RDMs. Refer to
scipy.spatial.distance
for a list of all other metrics and their arguments. Defaults to an empty dictionary.- rsa_metricstr
The RSA metric to use to compare the RDMs. Valid options are:
‘spearman’ for Spearman’s correlation (the default)
‘pearson’ for Pearson’s correlation
‘kendall-tau-a’ for Kendall’s Tau (alpha variant)
‘partial’ for partial Pearson correlations
‘partial-spearman’ for partial Spearman correlations
‘regression’ for linear regression weights
Defaults to ‘spearman’.
- ignore_nanbool
Whether to treat NaN’s as missing values and ignore them when computing the distance metric. Defaults to
False
.New in version 0.8.
- yndarray of int, shape (n_items,) | None
For each source estimate, a number indicating the item to which it belongs. When
None
, each source estimate is assumed to belong to a different item. Defaults toNone
.- n_foldsint | sklearn.model_selection.BaseCrollValidator | None
Number of cross-validation folds to use when computing the distance metric. Folds are created based on the
y
parameter. SpecifyNone
to use the maximum number of folds possible, given the data. Alternatively, you can pass a Scikit-Learn cross validator object (e.g.sklearn.model_selection.KFold
) to assert fine-grained control over how folds are created. Defaults to 1 (no cross-validation).- roi_mask3D Nifti-like image | None
When set, searchlight patches will only be generated for the subset of voxels with non-zero values in the given mask. This is useful for restricting the analysis to a region of interest (ROI). Note that while the center of the patches are all within the ROI, the patch itself may extend beyond the ROI boundaries. Defaults to
None
, in which case patches for all voxels are generated.- brain_mask3D Nifti-like image | None
When set, searchlight patches are restricted to only contain voxels with non-zero values in the given mask. This is useful for make sure only information from inside the brain is used. In contrast to the roi_mask, searchlight patches will not use data outside of this mask. Defaults to
None
, in which case all voxels are included in the analysis.- n_jobsint
The number of processes (=number of CPU cores) to use. Specify -1 to use all available cores. Defaults to 1.
- verbosebool
Whether to display a progress bar. In order for this to work, you need the tqdm python module installed. Defaults to False.
- Returns:
- rsa_results3D Nifti1Image | list of 3D Nifti1Image
The correlation values for each searchlight patch. When multiple models have been supplied, a list will be returned containing the RSA results for each model.
See also