tedana.workflows.tedana_workflow

tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None, strict=False, gscontrol=True, kdaw=10.0, rdaw=1.0, conv=2.5e-05, ste=-1, combmode='t2s', dne=False, initcost='tanh', finalcost='tanh', stabilize=False, filecsdata=False, wvpca=False, label=None, fixed_seed=42, debug=False, quiet=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.
  • 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 (str, optional) – Comma separated list of manually accepted components in string form. Default is None.
  • strict (bool, optional) – Ignore low-variance ambiguous components. Default is False.
  • gscontrol (bool, optional) – Control global signal using spatial approach. Default is True.
  • kdaw (float, optional) – Dimensionality augmentation weight (Kappa). Default is 10. -1 for low-dimensional ICA.
  • rdaw (float, optional) – Dimensionality augmentation weight (Rho). Default is 1. -1 for low-dimensional ICA.
  • conv (float, optional) – Convergence limit. Default is 2.5e-5.
  • ste (int, optional) – Source TEs for models. 0 for all, -1 for optimal combination. Default is -1.
  • combmode ({'t2s', 'ste'}, optional) – Combination scheme for TEs: ‘t2s’ (Posse 1999, default), ‘ste’ (Poser).
  • dne (bool, optional) – Denoise each TE dataset separately. Default is False.
  • initcost ({'tanh', 'pow3', 'gaus', 'skew'}, optional) – Initial cost function for ICA. Default is ‘tanh’.
  • finalcost ({'tanh', 'pow3', 'gaus', 'skew'}, optional) – Final cost function. Default is ‘tanh’.
  • stabilize (bool, optional) – Stabilize convergence by reducing dimensionality, for low quality data. Default is False.
  • filecsdata (bool, optional) – Save component selection data to file. Default is False.
  • wvpca (bool, optional) – Whether or not to perform PCA on wavelet-transformed data. Default is False.
  • label (str or None, optional) – Label for output directory. 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.
  • 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

PROCEDURE 2 : Computes ME-PCA and ME-ICA

  • Computes T2* map
  • Computes PCA of concatenated ME data, then computes TE-dependence of PCs
  • Computes ICA of TE-dependence PCs
  • Identifies TE-dependent ICs, outputs high-kappa (BOLD) component and denoised time series

or computes TE-dependence of each component of a general linear model specified by input (includes MELODIC FastICA mixing matrix)

PROCEDURE 2a: Model fitting and component selection routines

This workflow writes out several files, which are written out to a folder named TED.[ref_label].[label] if label is provided and TED.[ref_label] if not. ref_label is determined based on the name of the first data file.

Files are listed below:

Filename Content
t2sv.nii Limited estimated T2* 3D map. The difference between the limited and full maps is that, for voxels affected by dropout where only one echo contains good data, the full map uses the single echo’s value while the limited map has a NaN.
s0v.nii Limited S0 3D map. The difference between the limited and full maps is that, for voxels affected by dropout where only one echo contains good data, the full map uses the single echo’s value while the limited map has a NaN.
t2ss.nii ???
s0vs.nii ???
t2svG.nii Full T2* map/timeseries. The difference between the limited and full maps is that, for voxels affected by dropout where only one echo contains good data, the full map uses the single echo’s value while the limited map has a NaN.
s0vG.nii Full S0 map/timeseries.
__meica_mix.1D A mixing matrix
meica_mix.1D Another mixing matrix
ts_OC.nii Optimally combined timeseries.
betas_OC.nii Full ICA coefficient feature set.
betas_hik_OC.nii Denoised ICA coefficient feature set
feats_OC2.nii Z-normalized spatial component maps
comp_table.txt Component table
sphis_hik.nii T1-like effect
hik_ts_OC_T1c.nii T1 corrected time series by regression
dn_ts_OC_T1c.nii ME-DN version of T1 corrected time series
betas_hik_OC_T1c.nii T1-GS corrected components
meica_mix_T1c.1D T1-GS corrected mixing matrix

If dne is set to True:

Filename Content
hik_ts_e[echo].nii High-Kappa timeseries for echo number echo
midk_ts_e[echo].nii Mid-Kappa timeseries for echo number echo
lowk_ts_e[echo].nii Low-Kappa timeseries for echo number echo
dn_ts_e[echo].nii Denoised timeseries for echo number echo