nimare.meta.cbma.ale
.ALESubtraction
- class ALESubtraction(kernel_transformer=<class 'nimare.meta.kernel.ALEKernel'>, n_iters=10000, n_cores=1, **kwargs)[source]
Bases:
nimare.meta.cbma.base.PairwiseCBMAEstimator
ALE subtraction analysis.
Changed in version 0.0.12:
Use memmapped array for null distribution and remove
memory_limit
parameter.Support parallelization and add progress bar.
Add ALE-difference (stat) and -log10(p) (logp) maps to results.
Changed in version 0.0.8:
[FIX] Assume non-symmetric null distribution.
Changed in version 0.0.7:
[FIX] Assume a zero-centered and symmetric null distribution.
- Parameters
kernel_transformer (
KernelTransformer
, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.n_iters (
int
, optional) – Default is 10000.n_cores (
int
, optional) –Number of processes to use for meta-analysis. If -1, use all available cores. Default is 1.
New in version 0.0.12.
**kwargs – Keyword arguments. Arguments for the kernel_transformer can be assigned here, with the prefix
kernel__
in the variable name. Another optional argument ismask
.
- Variables
~ALESubtraction.masker (
NiftiMasker
or similar) – Masker object.~ALESubtraction.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.
Notes
This method was originally developed in 1 and refined in 2.
The ALE subtraction algorithm is also implemented as part of the GingerALE app provided by the BrainMap organization (https://www.brainmap.org/ale/).
The voxel-wise null distributions used by this Estimator are very large, so they are not retained as Estimator attributes.
Warning
This implementation contains one key difference from the original version.
In the original version, group 1 > group 2 difference values are only evaluated for voxels significant in the group 1 meta-analysis, and group 2 > group 1 difference values are only evaluated for voxels significant in the group 2 meta-analysis.
In NiMARE’s implementation, the analysis is run in a two-sided manner for all voxels in the mask.
References
- 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
- 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
Perform Monte Carlo-based FWE correction.
fit
(dataset1, dataset2[, drop_invalid])Fit Estimator to two Datasets.
get_params
([deep])Get parameters for this estimator.
load
(filename[, compressed])Load a pickled class instance from file.
save
(filename[, compress])Pickle the class instance to the provided file.
set_params
(**params)Set the parameters of this estimator.
- correct_fwe_montecarlo()[source]
Perform Monte Carlo-based FWE correction.
Warning
This method is not implemented for this class.
- fit(dataset1, dataset2, drop_invalid=True)[source]
Fit Estimator to two Datasets.
- Parameters
dataset1/dataset2 (
Dataset
) – Dataset objects to analyze.- Returns
Results of Estimator fitting.
- Return type
- classmethod load(filename, compressed=True)[source]
Load a pickled class instance from file.
- Parameters
- Returns
obj – Loaded class object.
- Return type
class object
- 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.- Return type
self