nimare.parcellate.cbp.CoordCBP

class CoordCBP(dataset, ids)[source]

Coordinate-based coactivation-based parcellation.

Notes

Here are the steps:

  1. For each voxel in the mask, identify studies in dataset corresponding to that voxel. Selection criteria can be either based on a distance threshold (e.g., all studies with foci within 5mm of voxel) or based on a minimum number of studies (e.g., the 50 studies reporting foci closest to the voxel).

  2. For each voxel, perform MACM (meta-analysis) using the identified studies.

  3. Correlate statistical maps between voxel MACMs to generate n_voxels X n_voxels correlation matrix.

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

  5. Perform clustering on correlation distance matrix.

Warning

This method is not yet implemented.

References

  • Bzdok, D., Laird, A. R., Zilles, K., Fox, P. T., & Eickhoff, S. B. (2013). An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Human brain mapping, 34(12), 3247-3266. https://doi.org/10.1002/hbm.22138

fit(target_mask, method='min_distance', r=5, n_exps=50, n_parcels=2, meta_estimator=<class 'nimare.meta.cbma.ale.SCALE'>, **kwargs)[source]

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

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 (class object) – Loaded 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.

Returns

self