nimare.parcellate.mamp.MAMP

class MAMP(dataset, ids)

Meta-analytic activation modeling-based parcellation (MAMP).

Parameters

• text (list of str) – List of texts to use for parcellation.

• mask (str or nibabel.Nifti1.Nifti1Image) – Mask file or image.

Notes

MAMP works similarly to CBP, but skips the step of performing a MACM for each voxel. Here are the steps:

1. Create an MA map for each study in the dataset.

2. Concatenate MA maps across studies to create a 4D dataset.

3. Extract values across studies for voxels in mask, resulting in n_voxels X n_studies array.

4. Correlate “study series” between voxels to generate n_voxels X n_voxels correlation matrix.

5. Convert correlation coefficients to correlation distance (1 -r) values.

6. Perform clustering on correlation distance matrix.

Warning

This method is not yet implemented.

References

• Yang, Yong, et al. “Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation.” Neuroimage 124 (2016): 300-309. https://doi.org/10.1016/j.neuroimage.2015.08.027

fit(target_mask, n_parcels=2, kernel_transformer=<class 'nimare.meta.cbma.kernel.ALEKernel'>, **kwargs)

Run MAMP parcellation.

Parameters

• target_mask (img_like) – Image with binary mask for region of interest to be parcellated.

• n_parcels (int or array_like of int, optional) – Number of parcels to generate for ROI. If array_like, each parcel number will be evaluated and results for all will be returned. Default is 2.

• n_iters (int, optional) – Number of iterations to run for each parcel number. Default is 10000.

• n_cores (int, optional) – Number of cores to use for model fitting.

Returns

results

get_params(deep=True)

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 (mapping of string to any) – Parameter names mapped to their values.

classmethod load(filename, compressed=True)

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 (class object) – Loaded 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.

Returns

self