Perform RSA in a searchlight pattern on MNE-Python source estimates.
The output is a source estimate where the “signal” at each source point is
the RSA, computed for a patch surrounding the source point. Source estimate
objects can be either defined along a cortical surface (SourceEstimate
objects) or volumetric (VolSourceEstimate
objects). For surface source
estimates, distances between vertices are measured in 2D space, namely as
the length of the path along the surface from one vertex to another. For
volume source estimates, distances are measured in 3D space as a straight
line from one voxel to another.
For each item, a source estimate for the brain activity.
The model DSM, see compute_dsm()
. For efficiency, you can give it
in condensed form, meaning only the upper triangle of the matrix as a
vector. See scipy.spatial.distance.squareform()
. To perform RSA
against multiple models at the same time, supply a list of model DSMs.
Use compute_dsm()
to compute DSMs.
The source space used by the source estimates specified in the stcs parameter.
The spatial radius of the searchlight patch in meters. All source points within this radius will belong to the searchlight patch. Set to None to only perform the searchlight over time, flattening across sensors. Defaults to 0.04.
The temporal radius of the searchlight patch in seconds. Set to None to only perform the searchlight over sensors, flattening across time. Defaults to 0.1.
The metric to use to compute the DSM for the source estimates. This can
be any metric supported by the scipy.distance.pdist function. See also
the stc_dsm_params
parameter to specify and additional parameter
for the distance function. Defaults to ‘correlation’.
Extra arguments for the distance metric used to compute the DSMs.
Refer to scipy.spatial.distance
for a list of all other metrics
and their arguments. Defaults to an empty dictionary.
The RSA metric to use to compare the DSMs. 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’.
Whether to treat NaN’s as missing values and ignore them when computing
the distance metric. Defaults to False
.
New in version 0.8.
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 to None
.
Number of cross-validation folds to use when computing the distance
metric. Folds are created based on the y
parameter. Specify
None
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).
When set, searchlight patches will only be generated for the subset of
vertices/voxels with the given indices. Defaults to None
, in which
case patches for all vertices/voxels are generated.
When set, searchlight patches will only be generated from subsequent
time points starting from this time point. This value is given in
seconds. Defaults to None
, in which case patches are generated
starting from the first time point.
When set, searchlight patches will only be generated up to and
including this time point. This value is given in seconds. Defaults to
None
, in which case patches are generated up to and including the
last time point.
The number of processes (=number of CPU cores) to use. Specify -1 to use all available cores. Defaults to 1.
Whether to display a progress bar. In order for this to work, you need the tqdm python module installed. Defaults to False.
The correlation values for each searchlight patch. When spatial_radius is set to None, there will only be one vertex. When temporal_radius is set to None, there will only be one time point. When multiple models have been supplied, a list will be returned containing the RSA results for each model.
See also