nimare.parcellate.cbp

Coactivation-based parcellation

Classes

CoordCBP(dataset, ids) Coordinate-based coactivation-based parcellation [R759c29a4323f-1].
ImCBP(dataset, ids) Image-based coactivation-based parcellation
class CoordCBP(dataset, ids)[source]

Coordinate-based coactivation-based parcellation [R759c29a4323f-1].

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

[R759c29a4323f-1](1, 2) 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

Methods

fit(self, target_mask[, method, r, n_exps, …]) Run CBP 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, 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:
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
class ImCBP(dataset, ids)[source]

Image-based coactivation-based parcellation

Warning

This method is not yet implemented.

Methods

fit(self, target_mask[, n_parcels])
param target_mask:
 Image with binary mask for region of interest to be parcellated.
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)[source]
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