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 [Rfe11a1766f4a-1] and refined in [Rfe11a1766f4a-2].

References

[Rfe11a1766f4a-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
[Rfe11a1766f4a-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

Methods

fit(self, ale1, ale2[, image1, image2, …]) Run a subtraction analysis comparing two groups of experiments from separate meta-analyses.
get_params(self[, deep]) Get parameters for this estimator.
load(filename[, compressed]) Load a pickled class instance from file.
save(self, filename[, compress]) Pickle the class instance to the provided file.
set_params(self, \*\*params) Set the parameters of this estimator.
fit(self, ale1, ale2, image1=None, image2=None, ma_maps1=None, ma_maps2=None)[source]

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

Parameters:
  • ale2 (ale1,) – Fitted ALE models for datasets to compare.
  • image2 (image1,) – Cluster-level FWE-corrected z-statistic maps associated with the respective models.
  • ma_maps1 ((E x V) array_like or None, optional) – Experiments by voxels array of modeled activation values. If not provided, MA maps will be generated from dataset1.
  • ma_maps2 ((E x V) array_like or None, optional) – Experiments by voxels array of modeled activation values. If not provided, MA maps will be generated from dataset2.
Returns:

Results of ALE subtraction analysis.
Return type:

nimare.base.base.MetaResult
get_params(self, 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 – Parameter names mapped to their values.
Return type:mapping of string to any
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 – Loaded class object.
Return type:

class object
save(self, 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(self, **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:
Return type:self