nimare.meta.cbma.ale.ALE

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

Activation likelihood estimation

Parameters

  • kernel_transformer (nimare.meta.cbma.kernel.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.

  • **kwargs – Keyword arguments. Arguments for the kernel_transformer can be assigned here, with the prefix ‘kernel__’ in the variable name. Another optional argument is mask.

Variables

  • masker –

  • inputs_ (dict) – Inputs to the Estimator. For CBMA estimators, there is only one key: coordinates. This is an edited version of the dataset’s coordinates DataFrame.

  • null_distributions_ (dict or numpy.ndarray) – Null distributions for ALE and any multiple-comparisons correction methods. Entries are added to this attribute if and when the corresponding method is fit.

Notes

The ALE algorithm was originally developed in 1, then updated in 2 and 3.

Available correction methods: ALE.correct_fwe_montecarlo

References

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.

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.

3

Eickhoff, Simon B., et al. “Activation likelihood estimation meta-analysis revisited.” Neuroimage 59.3 (2012): 2349-2361.

correct_fwe_montecarlo(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 (dict) – 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’.

Notes

This method also adds the following arrays to the CBMAEstimator’s null distributions attribute (null_distributions_): ‘fwe_level-voxel_method-montecarlo’ and ‘fwe_level-cluster_method-montecarlo’.

See also

nimare.correct.FWECorrector()

The Corrector from which to call this method.

Examples

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

Fit Estimator to Dataset.

Parameters

dataset (nimare.dataset.Dataset) – Dataset object to analyze.

Returns

nimare.results.MetaResult – Results of Estimator fitting.

Notes

The fit method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Estimators’ individual “fitting” methods are implemented as _fit, although users should call fit.

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