tedana.metrics.dependence_metrics

dependence_metrics(catd, tsoc, mmix, t2s, tes, ref_img, reindex=False, mmixN=None, algorithm=None, label=None, out_dir='.', verbose=False)

Fit TE-dependence and -independence models to components.

Parameters:

• catd ((S x E x T) array_like) – Input data, where S is samples, E is echos, and T is time
• tsoc ((S x T) array_like) – Optimally combined data
• mmix ((T x C) array_like) – Mixing matrix for converting input data to component space, where C is components and T is the same as in catd
• t2s ((S [x T]) array_like) – Limited T2* map or timeseries.
• tes (list) – List of echo times associated with catd, in milliseconds
• ref_img (str or img_like) – Reference image to dictate how outputs are saved to disk
• reindex (bool, optional) – Whether to sort components in descending order by Kappa. Default: False
• mmixN ((T x C) array_like, optional) – Z-scored mixing matrix. Default: None
• algorithm ({'kundu_v2', 'kundu_v3', None}, optional) – Decision tree to be applied to metrics. Determines which maps will be generated and stored in seldict. Default: None
• label (str or None, optional) – Prefix to apply to generated files. Default is None.
• out_dir (str, optional) – Output directory for generated files. Default is current working directory.
• verbose (bool, optional) – Whether or not to generate additional files. Default is False.

Returns:

• comptable ((C x X) pandas.DataFrame) – Component metric table. One row for each component, with a column for each metric. The index is the component number.
• seldict (dict or None) – Dictionary containing component-specific metric maps to be used for component selection. If algorithm is None, then seldict will be None as well.
• betas (numpy.ndarray)
• mmix_new (numpy.ndarray)