`nimare.meta.cbma.mkda`.MKDADensity

class MKDADensity(kernel_transformer=<class 'nimare.meta.kernel.MKDAKernel'>, null_method='approximate', n_iters=10000, n_cores=1, **kwargs)[source]

Bases: nimare.meta.cbma.base.CBMAEstimator

Multilevel kernel density analysis- Density analysis.

Parameters

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

null_method ({"approximate", "montecarlo"}, optional) –

Method by which to determine uncorrected p-values. The available options are

”approximate” (default)

Build a histogram of summary-statistic values and their expected frequencies under the assumption of random spatial associated between studies, via a weighted convolution.

This method is much faster, but slightly less accurate.

”montecarlo”

Perform a large number of permutations, in which the coordinates in the studies are randomly drawn from the Estimator’s brain mask and the full set of resulting summary-statistic values are incorporated into a null distribution (stored as a histogram for memory reasons).

This method is must 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 if null_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.

Variables

~MKDADensity.masker (NiftiMasker or similar) – Masker object.
~MKDADensity.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.
~MKDADensity.null_distributions_ (dict of numpy.ndarray) –
Null distributions for the uncorrected summary-statistic-to-p-value conversion and any multiple-comparisons correction methods. Entries are added to this attribute if and when the corresponding method is applied.

If null_method == "approximate":
- histogram_bins: Array of bin centers for the null distribution histogram, ranging from zero to the maximum possible summary statistic value for the Dataset.
- histweights_corr-none_method-approximate: Array of weights for the null distribution histogram, with one value for each bin in histogram_bins.
If null_method == "montecarlo":
- histogram_bins: Array of bin centers for the null distribution histogram, ranging from zero to the maximum possible summary statistic value for the Dataset.
- histweights_corr-none_method-montecarlo: Array of weights for the null distribution histogram, with one value for each bin in histogram_bins. These values are derived from the full set of summary statistics from each iteration of the Monte Carlo procedure.
- histweights_level-voxel_corr-fwe_method-montecarlo: Array of weights for the voxel-level FWE-correction null distribution, with one value for each bin in histogram_bins. These values are derived from the maximum summary statistic from each iteration of the Monte Carlo procedure.
If correct_fwe_montecarlo() is applied:
- 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,).

Notes

The MKDA density algorithm is also implemented in MATLAB at https://github.com/canlab/Canlab_MKDA_MetaAnalysis.

Available correction methods: MKDADensity.correct_fwe_montecarlo()

References

Wager, Tor D., Martin Lindquist, and Lauren Kaplan. “Meta-analysis of functional neuroimaging data: current and future directions.” Social cognitive and affective neuroscience 2.2 (2007): 150-158. https://doi.org/10.1093/scan/nsm015

Methods

`correct_fwe_montecarlo`(result[, ...])	Perform FWE correction using the max-value permutation method.
`fit`(dataset[, drop_invalid])	Fit Estimator to Dataset.
`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=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.

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, optional) – Cluster-defining p-value threshold. Default is 0.001.
n_iters (int, optional) – Number of iterations to build the voxel-level, cluster-size, and cluster-mass FWE 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.
vfwe_only (bool, optional) – 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”. If this is set to True and the original null method was not montecarlo, an exception will be raised. Default is False.

Returns

images – 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 4 and 5, 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 6.

Return type

dict

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

4: Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999). 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. doi: 10.1109/42.750253
5: Zhang, H., Nichols, T. E., & Johnson, T. D. (2009). Cluster mass inference via random field theory. Neuroimage, 44(1), 51-61. doi: 10.1016/j.neuroimage.2008.08.017
6: Eickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., … & Eickhoff, C. R. (2016). Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation. Neuroimage, 137, 70-85. 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)[source]

Fit Estimator to Dataset.

Parameters

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

Results of Estimator fitting.

Return type

MetaResult

Variables

~Estimator.fit.inputs_ (dict) – Inputs used in _fit.

Notes

The fit method 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 call fit.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters: deep (bool, 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: dict

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(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(**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

nimare.meta.cbma.mkda.MKDADensity

`nimare.meta.cbma.mkda`.MKDADensity