tedana_workflow(data, tes, out_dir='.', mask=None, convention='bids', prefix='', fittype='loglin', combmode='t2s', tedpca='aic', fixed_seed=42, maxit=500, maxrestart=10, tedort=False, gscontrol=None, no_reports=False, png_cmap='coolwarm', verbose=False, low_mem=False, debug=False, quiet=False, t2smap=None, mixm=None, ctab=None, manacc=None)[source]

Run the “canonical” TE-Dependent ANAlysis workflow.

Please remember to cite 1.

  • 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.

  • out_dir (str, optional) – Output directory.

  • mask (str or None, 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.

  • fittype ({‘loglin’, ‘curvefit’}, optional) – Monoexponential fitting method. ‘loglin’ uses the the default linear fit to the log of the data. ‘curvefit’ uses a monoexponential fit to the raw data, which is slightly slower but may be more accurate. Default is ‘loglin’.

  • combmode ({‘t2s’}, optional) – Combination scheme for TEs: ‘t2s’ (Posse 1999, default).

  • tedpca ({‘mdl’, ‘aic’, ‘kic’, ‘kundu’, ‘kundu-stabilize’, float}, optional) – Method with which to select components in TEDPCA. If a float is provided, then it is assumed to represent percentage of variance explained (0-1) to retain from PCA. Default is ‘aic’.

  • tedort (bool, optional) – Orthogonalize rejected components w.r.t. accepted ones prior to denoising. Default is False.

  • gscontrol ({None, ‘mir’, ‘gsr’} or list, optional) – Perform additional denoising to remove spatially diffuse noise. Default is None.

  • verbose (bool, optional) – Generate intermediate and additional files. Default is False.

  • no_reports (obj:’bool’, optional) – Do not generate .html reports and .png plots. Default is false such that reports are generated.

  • png_cmap (obj:’str’, optional) – Name of a matplotlib colormap to be used when generating figures. Cannot be used with –no-png. Default is ‘coolwarm’.

  • t2smap (str, optional) – Precalculated T2* map in the same space as the input data. Values in the map must be in seconds.

  • mixm (str or None, optional) – File containing mixing matrix, to be used when re-running the workflow. If not provided, ME-PCA and ME-ICA are done. Default is None.

  • ctab (str or None, optional) – File containing component table from which to extract pre-computed classifications, to be used with ‘mixm’ when re-running the workflow. Default is None.

  • manacc (list of int or None, optional) – List of manually accepted components. Can be a list of the components numbers or None. If provided, this parameter requires mixm and ctab to be provided as well. Default is None.

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.


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



DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S., Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S., Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M., Whitaker, K., & Handwerker, D. A. (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.