nimare.meta.cbma.base.CBMAEstimator

class CBMAEstimator(kernel_transformer, memory=Memory(location=None), memory_level=0, generate_description=True, *, mask=None, mask_coverage='brain', **kwargs)

Bases: Estimator

Base class for coordinate-based meta-analysis methods.

Warning

Support for Dataset inputs is deprecated and will be removed in a future release. Prefer Studyset.

Changed in version 0.12.0:

• Standardize sparse MA maps as 2D study-by-masked-voxel matrices across CBMA methods.

Changed in version 0.0.12:

• Remove low_memory option

• CBMA-specific elements of Estimator excised and moved into CBMAEstimator.

• Generic kwargs and args converted to named kwargs. All remaining kwargs are for kernels.

• Use a 4D sparse array for modeled activation maps.

Changed in version 0.0.8:

• [REF] Use saved MA maps, when available.

• [REF] Add low_memory option.

Added in version 0.0.3.

Parameters:

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

• memory (instance of joblib.Memory, str, or pathlib.Path) – Used to cache the output of a function. By default, no caching is done. If a str is given, it is the path to the caching directory.

• memory_level (int, default=0) – Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.

• mask_coverage ({"gm", "brain"}, optional) – Voxel set from which random null foci are drawn when building Monte Carlo null distributions. "gm" restricts sampling to voxels with mask-image intensity above 0.1 (the ICBM 10 % GM probability map). "brain" uses every non-zero voxel in the mask. Has no effect when null_method="approximate". Default is "brain".

• *args – Optional arguments to the Estimator __init__ (called automatically).

• **kwargs – Optional keyword arguments to the Estimator __init__ (called automatically).

Methods

correct_fwe_montecarlo(result[, ...])	Perform FWE correction using the max-value permutation method.
fit(dataset[, drop_invalid, ma_maps])	Fit Estimator to a collection.
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(result, voxel_thresh=0.001, n_iters=5000, n_cores=1, vfwe_only=False)

Perform FWE correction using the max-value permutation method.

Only call this method from within a Corrector.

Changed in version 0.0.13: Change cluster neighborhood from faces+edges to faces, to match Nilearn.

Changed in version 0.0.12:

• Fix the vfwe_only option.

Changed in version 0.0.11:

• Rename *_level-cluster maps to *_desc-size_level-cluster.

• Add new *_desc-mass_level-cluster maps that use cluster mass-based inference.

Parameters:

• result (MetaResult) – Result object from a CBMA meta-analysis.

• voxel_thresh (float, default=0.001) – Cluster-defining p-value threshold. Default is 0.001.

• n_iters (int, default=5000) – Number of iterations to build the voxel-level, cluster-size, and cluster-mass FWE null distributions. Default is 5000.

• n_cores (int, default=1) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is 1.

• vfwe_only (bool, default=False) – If True, only calculate the voxel-level FWE-corrected maps. Voxel-level correction can be performed very quickly if the Estimator’s null_method was “montecarlo”. Default is False.

Returns:

• images (dict) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method:

  • logp_desc-size_level-cluster: Cluster-level FWE-corrected -log10(p) map based on cluster size. This was previously simply called “logp_level-cluster”. This array is not generated if vfwe_only is True.

  • logp_desc-mass_level-cluster: Cluster-level FWE-corrected -log10(p) map based on cluster mass. According to Bullmore et al.[1] and Zhang et al.[2], cluster mass-based inference is more powerful than cluster size. This array is not generated if vfwe_only is True.

  • logp_level-voxel: Voxel-level FWE-corrected -log10(p) map. Voxel-level correction is generally more conservative than cluster-level correction, so it is only recommended for very large meta-analyses (i.e., hundreds of studies), per Eickhoff et al.[3].

• description_ (str) – A text description of the correction procedure.

Notes

If vfwe_only is False, this method adds three new keys to the null_distributions_ attribute:

• values_level-voxel_corr-fwe_method-montecarlo: The maximum summary statistic value from each Monte Carlo iteration. An array of shape (n_iters,).

• values_desc-size_level-cluster_corr-fwe_method-montecarlo: The maximum cluster size from each Monte Carlo iteration. An array of shape (n_iters,).

• values_desc-mass_level-cluster_corr-fwe_method-montecarlo: The maximum cluster mass from each Monte Carlo iteration. An array of shape (n_iters,).

See also

nimare.correct.FWECorrector

The Corrector from which to call this method.

References

[1]

Edward T Bullmore, John Suckling, Stephan Overmeyer, Sophia Rabe-Hesketh, Eric Taylor, and Michael J Brammer. Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural mr images of the brain. IEEE transactions on medical imaging, 18(1):32–42, 1999. URL: https://doi.org/10.1109/42.750253, doi:10.1109/42.750253.

[2]

Hui Zhang, Thomas E Nichols, and Timothy D Johnson. Cluster mass inference via random field theory. Neuroimage, 44(1):51–61, 2009. URL: https://doi.org/10.1016/j.neuroimage.2008.08.017, doi:10.1016/j.neuroimage.2008.08.017.

[3]

Simon B Eickhoff, Thomas E Nichols, Angela R Laird, Felix Hoffstaedter, Katrin Amunts, Peter T Fox, Danilo Bzdok, and Claudia R Eickhoff. Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation. Neuroimage, 137:70–85, 2016. URL: https://doi.org/10.1016/j.neuroimage.2016.04.072, doi:10.1016/j.neuroimage.2016.04.072.

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, ma_maps=None)

Fit Estimator to a collection.

Parameters:

• dataset (Studyset or Dataset) – Collection object to analyze.

• drop_invalid (bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is True.

• ma_maps (scipy sparse matrix or sparse array, optional) – Precomputed study-wise MA maps aligned to the study ids in dataset. When provided, the estimator will reuse these maps instead of recomputing them from coordinates. These are typically 2D study-by-masked-voxel sparse matrices.

Returns:

Results of Estimator fitting.

Return type:

MetaResult

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – 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:

dict

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

• filename (str) – Name of file containing object.

• compressed (bool, default=True) – 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(filename, compress=True)

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)

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

Examples using nimare.meta.cbma.base.CBMAEstimator

The NiMARE Studyset object

The NiMARE Studyset object

Coordinate-based meta-analysis algorithms

Coordinate-based meta-analysis algorithms

The Estimator class

The Estimator class

The Corrector class

The Corrector class

Compare image and coordinate based meta-analyses

Compare image and coordinate based meta-analyses

Two-sample ALE meta-analysis

Two-sample ALE meta-analysis

Simulate data for coordinate based meta-analysis

Simulate data for coordinate based meta-analysis

Qualitative interpretation of ALE statistic maps

Qualitative interpretation of ALE statistic maps

Comparing ALE-based pairwise contrast strategies

Comparing ALE-based pairwise contrast strategies

Predictive ALE: fast FWE correction without Monte Carlo

Predictive ALE: fast FWE correction without Monte Carlo

Stability diagnostics: Jackknife vs. ResampledStability

Stability diagnostics: Jackknife vs. ResampledStability