nimare.decode.continuous.corr_dist_decode

corr_dist_decode(dataset, img, features=None, frequency_threshold=0.001, target_image='z')[source]

Builds feature-specific distributions of correlations with input image for image-based meta-analytic functional decoding.

Parameters:
  • dataset (nimare.dataset.Dataset) – A dataset with images.
  • 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.
  • target_image ({'z', 'con'}, optional) – Image type from database to use for decoding.
Returns:

out_df – DataFrame with a row for each feature used for decoding and two columns: mean and std. Values describe the distributions of correlation coefficients (in terms of Fisher-transformed z-values).
Return type:

pandas.DataFrame