tedana.workflows.tedana_workflow

tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None, tedort=False, gscontrol=None, tedpca='mle', source_tes=-1, combmode='t2s', verbose=False, stabilize=False, out_dir='.', fixed_seed=42, maxit=500, maxrestart=10, debug=False, quiet=False, png=False, png_cmap='coolwarm', low_mem=False)[source]

Run the “canonical” TE-Dependent ANAlysis workflow.

Parameters:
  • data (str or list of str) – Either a single z-concatenated file (single-entry list or str) or a list of echo-specific files, in ascending order.
  • tes (list) – List of echo times associated with data in milliseconds.
  • mask (str, optional) – Binary mask of voxels to include in TE Dependent ANAlysis. Must be spatially aligned with data. If an explicit mask is not provided, then Nilearn’s compute_epi_mask function will be used to derive a mask from the first echo’s data.
  • mixm (str, optional) – File containing mixing matrix. If not provided, ME-PCA and ME-ICA are done.
  • ctab (str, optional) – File containing component table from which to extract pre-computed classifications.
  • manacc (list, str, or None, optional) – List of manually accepted components. Can be a list of the components, a comma-separated string with component numbers, or None. Default is None.
  • tedort (bool, optional) – Orthogonalize rejected components w.r.t. accepted ones prior to denoising. Default is False.
  • gscontrol ({None, ‘t1c’, ‘gsr’} or list, optional) – Perform additional denoising to remove spatially diffuse noise. Default is None.
  • tedpca ({'mle', 'kundu', 'kundu-stabilize'}, optional) – Method with which to select components in TEDPCA. Default is ‘mle’.
  • source_tes (int, optional) – Source TEs for models. 0 for all, -1 for optimal combination. Default is -1.
  • combmode ({'t2s'}, optional) – Combination scheme for TEs: ‘t2s’ (Posse 1999, default).
  • verbose (bool, optional) – Generate intermediate and additional files. Default is False.
  • png (obj:'bool', optional) – Generate simple plots and figures. Default is false.
  • png_cmap (obj:'str', optional) – Name of a matplotlib colormap to be used when generating figures. –png must still be used to request figures. Default is ‘coolwarm’
  • out_dir (str, optional) – Output directory.
Other Parameters:
 
  • fixed_seed (int, optional) – Value passed to mdp.numx_rand.seed(). Set to a positive integer value for reproducible ICA results; otherwise, set to -1 for varying results across calls.
  • maxit (int, optional) – Maximum number of iterations for ICA. Default is 500.
  • maxrestart (int, optional) – Maximum number of attempts for ICA. If ICA fails to converge, the fixed seed will be updated and ICA will be run again. If convergence is achieved before maxrestart attempts, ICA will finish early. Default is 10.
  • low_mem (bool, optional) – Enables low-memory processing, including the use of IncrementalPCA. May increase workflow duration. Default is False.
  • debug (bool, optional) – Whether to run in debugging mode or not. Default is False.
  • quiet (bool, optional) – If True, suppresses logging/printing of messages. Default is False.

Notes

This workflow writes out several files. For a complete list of the files generated by this workflow, please visit https://tedana.readthedocs.io/en/latest/outputs.html