nimare.meta.cbma.ale.ALE
- class ALE(kernel_transformer=<class 'nimare.meta.kernel.ALEKernel'>, null_method='approximate', n_iters=10000, n_cores=1, **kwargs)[source]
Bases:
nimare.meta.cbma.base.CBMAEstimatorActivation likelihood estimation.
- Parameters
kernel_transformer (
nimare.meta.kernel.KernelTransformer, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.null_method ({“approximate”, “montecarlo”}, optional) – Method by which to determine uncorrected p-values. “approximate” is faster, but slightly less accurate. “montecarlo” can be much slower, and is only slightly more accurate.
n_iters (int, optional) – Number of iterations to use to define the null distribution. This is only used if
null_method=="montecarlo". Default is 10000.n_cores (
int, optional) – Number of cores to use for parallelization. This is only used ifnull_method=="montecarlo". If <=0, defaults to using all available cores. Default is 1.**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_ (
dictornumpy.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.
The ALE algorithm is also implemented as part of the GingerALE app provided by the BrainMap organization (https://www.brainmap.org/ale/).
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.
- compute_summarystat(data)[source]
Compute summary statistics from data.
The actual summary statistic varies across Estimators. For ALE and SCALE, the values are known as ALE values. For (M)KDA, they are “OF” scores.
- Parameters
data (array, pandas.DataFrame, or list of img_like) – Data from which to estimate summary statistics. The data can be: (1) a 1d contrast-len or 2d contrast-by-voxel array of MA values, (2) a DataFrame containing coordinates to produce MA values, or (3) a list of imgs containing MA values.
- Returns
stat_values (1d array) – Summary statistic values. One value per voxel.
- correct_fwe_montecarlo(result, voxel_thresh=0.001, n_iters=10000, n_cores=- 1, vfwe_only=False)[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 a KDA meta-analysis.voxel_thresh (
float, optional) – Cluster-defining p-value threshold. Default is 0.001.n_iters (
int, optional) – Number of iterations to build the 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: ‘vthresh’, ‘logp_level-cluster’, and ‘logp_level-voxel’.
See also
nimare.correct.FWECorrectorThe Corrector from which to call this method.
Examples
>>> meta = MKDADensity() >>> result = meta.fit(dset) >>> corrector = FWECorrector(method='montecarlo', voxel_thresh=0.01, n_iters=5, n_cores=1) >>> cresult = corrector.transform(result)
- fit(dataset, drop_invalid=True)[source]
Fit Estimator to Dataset.
- Parameters
dataset (
nimare.dataset.Dataset) – Dataset object to analyze.drop_invalid (
bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is False.
- Returns
nimare.results.MetaResult– Results of Estimator fitting.- Variables
inputs_ (
dict) – Inputs used in _fit.
Notes
The
fitmethod 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 callfit.