tedana.model.fit.fitmodels_direct

fitmodels_direct(catd, mmix, mask, t2s, t2s_full, tes, combmode, ref_img, reindex=False, mmixN=None, full_sel=True)[source]

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
  • 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
  • mask ((S [x E]) array_like) – Boolean mask array
  • t2s ((S [x T]) array_like) – Limited T2* map or timeseries.
  • t2s_full ((S [x T]) array_like) – Full T2* map or timeseries. For voxels with good signal in only one echo, which are zeros in the limited T2* map, this map uses the T2* estimate using the first two echoes.
  • tes (list) – List of echo times associated with catd, in milliseconds
  • combmode ({'t2s', 'ste'} str) – How optimal combination of echos should be made, where ‘t2s’ indicates using the method of Posse 1999 and ‘ste’ indicates using the method of Poser 2006
  • ref_img (str or img_like) – Reference image to dictate how outputs are saved to disk
  • reindex (bool, optional) – Default: False
  • mmixN (array_like, optional) – Default: None
  • full_sel (bool, optional) – Whether to perform selection of components based on Rho/Kappa scores. Default: True
Returns:

  • seldict (dict)
  • comptab ((N x 5) pandas.DataFrame) – Array with columns denoting (1) index of component, (2) Kappa score of component, (3) Rho score of component, (4) variance explained by component, and (5) normalized variance explained by component
  • betas (numpy.ndarray)
  • mmix_new (numpy.ndarray)