nimare.decode.continuous.corr_decode

corr_decode(dataset, img, features=None, frequency_threshold=0.001, meta_estimator=None, target_image='specificity_z')[source]
Parameters:
  • dataset (nimare.dataset.Dataset) – A dataset with coordinates.
  • img (nibabel.Nifti1.Nifti1Image) – Input image to decode. Must have same affine/dimensions as dataset mask.
  • features (list, optional) – List of features in dataset annotations to use for decoding. Default is None, which uses all features available.
  • frequency_threshold (float, optional) – Threshold to apply to dataset annotations. Values greater than or equal to the threshold as assigned as label+, while values below the threshold are considered label-. Default is 0.001.
  • meta_estimator (initialized nimare.meta.cbma.base.CBMAEstimator, optional) – Defaults to MKDAChi2.
  • target_image (str, optional) – Image from meta_estimator’s results to use for decoding. Dependent on estimator.
Returns:

out_df – A DataFrame with two columns: ‘feature’ (label) and ‘r’ (correlation coefficient). There will be one row for each feature.
Return type:

pandas.DataFrame