nimare.meta.cbma.ale¶

CBMA methods from the activation likelihood estimation (ALE) family

Classes

`ALE`([kernel_estimator])	Activation likelihood estimation
`ALESubtraction`([n_iters])	ALE subtraction analysis.
`SCALE`([voxel_thresh, n_iters, n_cores, ijk, …])	Specific coactivation likelihood estimation [Re194cb71ed63-1].

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].

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

`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.

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.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

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

class SCALE(voxel_thresh=0.001, n_iters=10000, n_cores=-1, ijk=None, kernel_estimator=<class 'nimare.meta.cbma.kernel.ALEKernel'>, **kwargs)[source]¶

Specific coactivation likelihood estimation [Re194cb71ed63-1].

Parameters:

voxel_thresh (float, optional) – Uncorrected voxel-level threshold. Default: 0.001
n_iters (int, optional) – Number of iterations for correction. Default: 10000
n_cores (int, optional) – Number of processes to use for meta-analysis. If -1, use all available cores. Default: -1
ijk (str or (N x 3) array_like) – Tab-delimited file of coordinates from database or numpy array with ijk coordinates. Voxels are rows and i, j, k (meaning matrix-space) values are the three columnns.
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.

References

[Re194cb71ed63-1]

(1, 2) Langner, Robert, et al. “Meta-analytic connectivity modeling revisited: controlling for activation base rates.” NeuroImage 99 (2014): 559-570. https://doi.org/10.1016/j.neuroimage.2014.06.007

Methods

`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.

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.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