nimare.parcellate.mapbot
.MAPBOT¶
-
class
MAPBOT
(tfidf_df, coordinates_df, mask)[source]¶ Meta-analytic parcellation based on text (MAPBOT).
- Parameters
tfidf_df (
pandas.DataFrame
) – A DataFrame with feature counts for the model. The index is ‘id’, used for identifying studies. Other columns are features (e.g., unigrams and bigrams from Neurosynth), where each value is the number of times the feature is found in a given article.coordinates_df (
pandas.DataFrame
) – A DataFrame with a list of foci in the dataset. The index is ‘id’, used for identifying studies. Additional columns include ‘i’, ‘j’ and ‘k’ (the matrix indices of the foci in standard space).mask (
str
ornibabel.Nifti1.Nifti1Image
) – Mask file or image.
Notes
MAPBOT uses both the reported foci for studies, as well as associated term weights. Here are the steps:
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).
For each voxel, compute average frequency of each term across selected studies. This results in an n_voxels X n_terms frequency matrix F.
Compute n_voxels X n_voxels value matrix V: - D = (F.T * F) * ones(F) - V = F * D^-.5
Perform non-negative matrix factorization on value matrix.
Warning
This method is not yet implemented.
References
Yuan, Rui, et al. “MAPBOT: Meta-analytic parcellation based on text, and its application to the human thalamus.” NeuroImage 157 (2017): 716-732. https://doi.org/10.1016/j.neuroimage.2017.06.032
-
fit
(target_mask, method='min_distance', r=5, n_exps=50, n_parcels=2)[source]¶ Run MAPBOT parcellation.
-
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.