nimare.parcellate.mamp.MAMP

class MAMP(dataset, ids)[source]

Meta-analytic activation modeling-based parcellation (MAMP) [Re636c01f812e-1].

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

[Re636c01f812e-1]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

Methods

fit(self, target_mask[, n_parcels, …]) Run MAMP parcellation.
get_params(self[, deep]) Get parameters for this estimator.
load(filename[, compressed]) Load a pickled class instance from file.
save(self, filename[, compress]) Pickle the class instance to the provided file.
set_params(self, \*\*params) Set the parameters of this estimator.
fit(self, target_mask, n_parcels=2, kernel_estimator=<class 'nimare.meta.cbma.kernel.ALEKernel'>, **kwargs)[source]

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:
Return type:

results
get_params(self, deep=True)[source]

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 – Parameter names mapped to their values.
Return type:mapping of string to any
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(self, 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(self, **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.

Returns:
Return type:self