nimare.meta.cbma.ale.ALE

class ALE(kernel_estimator=<class 'nimare.meta.cbma.kernel.ALEKernel'>, **kwargs)[source]

Activation likelihood estimation

Parameters:
  • kernel_estimator (nimare.meta.cbma.base.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.
  • **kwargs – Keyword arguments. Arguments for the kernel_estimator can be assigned here, with the prefix ‘kernel__’ in the variable name.

Notes

The ALE algorithm was originally developed in [Rbfa6233af19f-1], then updated in [Rbfa6233af19f-2] and [Rbfa6233af19f-3].

Available correction methods: ALE.correct_fwe_permutation

References

[Rbfa6233af19f-1]Turkeltaub, Peter E., et al. “Meta-analysis of the functional neuroanatomy of single-word reading: method and validation.” Neuroimage 16.3 (2002): 765-780.
[Rbfa6233af19f-2]Turkeltaub, Peter E., et al. “Minimizing within‐experiment and within‐group effects in activation likelihood estimation meta‐analyses.” Human brain mapping 33.1 (2012): 1-13.
[Rbfa6233af19f-3]Eickhoff, Simon B., et al. “Activation likelihood estimation meta-analysis revisited.” Neuroimage 59.3 (2012): 2349-2361.

Methods

correct_fwe_permutation(self, result[, …]) Perform FWE correction using the max-value permutation method.
fit(self, dataset) Fit Estimator to Dataset.
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.
correct_fwe_permutation(self, result, voxel_thresh=0.001, n_iters=10000, n_cores=-1)[source]

Perform FWE correction using the max-value permutation method. Only call this method from within a Corrector.

Parameters:
  • result (nimare.results.MetaResult) – Result object from an ALE meta-analysis.
  • voxel_thresh (float, optional) – Cluster-defining uncorrected p-value threshold. Default is 0.001.
  • n_iters (int, optional) – Number of iterations to build vFWE and cFWE null distributions. Default is 10000.
  • n_cores (int, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is -1.
Returns:

images – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘z_vthresh’, ‘p_level-voxel’, ‘z_level-voxel’, and ‘logp_level-cluster’.
Return type:

dict

Notes

This method also adds the following arrays to the Estimator’s null distributions attribute (null_distributions): ‘fwe_level-voxel’ and ‘fwe_level-cluster’.

See also

nimare.correct.FWECorrector()
The Corrector from which to call this method.

Examples

>>> meta = ALE()
>>> result = meta.fit(dset)
>>> corrector = FWECorrector(method='permutation', voxel_thresh=0.001,
                             n_iters=5, n_cores=1)
>>> cresult = corrector.transform(result)
fit(self, dataset)[source]

Fit Estimator to Dataset.

Parameters:dataset (nimare.dataset.Dataset) – Dataset object to analyze.
Returns:Results of Estimator fitting.
Return type:nimare.results.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