nimare.meta.cbma.ale.ALESubtraction

class ALESubtraction(n_iters=10000)[source]

ALE subtraction analysis.

Parameters

n_iters (int, optional) – Default is 10000.

Notes

This method was originally developed in 1 and refined in 2.

Warning

This implementation contains one key difference from the original version. In the original version, group 1 > group 2 difference values are only evaluated for voxels significant in the group 1 meta-analysis, and group 2 > group 1 difference values are only evaluated for voxels significant in the group 2 meta-analysis. In NiMARE’s implementation, the analysis is run in a two-sided manner for all voxels in the mask.

References

1

Laird, Angela R., et al. “ALE meta‐analysis: Controlling the false discovery rate and performing statistical contrasts.” Human brain mapping 25.1 (2005): 155-164. https://doi.org/10.1002/hbm.20136

2

Eickhoff, Simon B., et al. “Activation likelihood estimation meta-analysis revisited.” Neuroimage 59.3 (2012): 2349-2361. https://doi.org/10.1016/j.neuroimage.2011.09.017

fit(meta1, meta2)[source]

Run a subtraction analysis comparing two groups of experiments from separate meta-analyses.

Parameters

meta1/meta2 (nimare.meta.cbma.ale.ALE) – Fitted ALE Estimators for datasets to compare. These Estimators do not require multiple comparisons correction.

Returns

nimare.results.MetaResult – Results of ALE subtraction analysis, with one map: ‘z_desc-group1MinusGroup2’.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params (mapping of string to any) – Parameter names mapped to their values.

classmethod load(filename, compressed=True)[source]

Load a pickled class instance from file.

Parameters
  • filename (str) – Name of file containing object.

  • compressed (bool, optional) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns

obj (class object) – Loaded class object.

save(filename, compress=True)[source]

Pickle the class instance to the provided file.

Parameters
  • filename (str) – File to which object will be saved.

  • compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

self