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].
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: 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)
- 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: Returns: obj – Loaded class object.
Return type: class object
-
save
(self, filename, compress=True)[source]¶ Pickle the class instance to the provided file.
Parameters:
-
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
- kernel_estimator (
-
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: - ale1/ale2 (
nimare.meta.cbma.ale.ALE
) – Fitted ALE models for datasets to compare. - image1/image2 (img_like or array_like) – 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: - ale1/ale2 (
-
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: Returns: obj – Loaded class object.
Return type: class object
-
save
(self, filename, compress=True)[source]¶ Pickle the class instance to the provided file.
Parameters:
-
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.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: Returns: obj – Loaded class object.
Return type: class object
-
save
(self, filename, compress=True)[source]¶ Pickle the class instance to the provided file.
Parameters:
-
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