Usage

tedana minimally requires:

1. acquired echo times (in milliseconds), and
2. functional datasets equal to the number of acquired echoes.

But you can supply many other options, viewable with tedana -h or t2smap -h.

Run tedana

This is the full tedana workflow, which runs multi-echo ICA and outputs multi-echo denoised data along with many other derivatives. To see which files are generated by this workflow, check out the workflow documentation: tedana.workflows.tedana_workflow().

usage: tedana [-h] -d FILE [FILE ...] -e TE [TE ...] [--mask FILE]
              [--mix FILE] [--ctab FILE] [--manacc MANACC] [--kdaw KDAW]
              [--rdaw RDAW] [--conv CONV] [--sourceTEs STE]
              [--combmode {t2s,ste}] [--initcost {tanh,pow3,gaus,skew}]
              [--finalcost {tanh,pow3,gaus,skew}] [--denoiseTEs] [--strict]
              [--no_gscontrol] [--stabilize] [--filecsdata] [--wvpca]
              [--label LABEL] [--seed FIXED_SEED]

Named Arguments

-d	Multi-echo dataset for analysis. May be a single file with spatially concatenated data or a set of echo-specific files, in the same order as the TEs are listed in the -e argument.
-e	Echo times (in ms). E.g., 15.0 39.0 63.0
--mask	Binary mask of voxels to include in TE Dependent ANAlysis. Must be in the same space as data.
--mix	File containing mixing matrix. If not provided, ME-PCA & ME-ICA is done.
--ctab	File containing a component table from which to extract pre-computed classifications.
--manacc	Comma separated list of manually accepted components
--kdaw	Dimensionality augmentation weight (Kappa). Default=10. -1 for low-dimensional ICA Default: 10.0
--rdaw	Dimensionality augmentation weight (Rho). Default=1. -1 for low-dimensional ICA Default: 1.0
--conv	Convergence limit. Default 2.5e-5 Default: 2.5e-5
--sourceTEs	Source TEs for models. E.g., 0 for all, -1 for opt. com., and 1,2 for just TEs 1 and 2. Default=-1. Default: -1
--combmode	Possible choices: t2s, ste Combination scheme for TEs: t2s (Posse 1999, default), ste (Poser) Default: “t2s”
--initcost	Possible choices: tanh, pow3, gaus, skew Initial cost function for ICA. Default: “tanh”
--finalcost	Possible choices: tanh, pow3, gaus, skew Final cost function for ICA. Same options as initcost. Default: “tanh”
--denoiseTEs	Denoise each TE dataset separately. Default: False
--strict	Ignore low-variance ambiguous components Default: False
--no_gscontrol	Disable global signal regression. Default: True
--stabilize	Stabilize convergence by reducing dimensionality, for low quality data Default: False
--filecsdata	Save component selection data Default: False
--wvpca	Perform PCA on wavelet-transformed data Default: False
--label	Label for output directory.
--seed	Value passed to repr(mdp.numx_rand.seed()) Set to an integer value for reproducible ICA results; otherwise, set to -1 for varying results across calls. Default: 42

Note

The --mask argument is not intended for use with very conservative region-of-interest analyses. One of the ways by which components are assessed as BOLD or non-BOLD is their spatial pattern, so overly conservative masks will invalidate several steps in the tedana workflow. To examine regions-of-interest with multi-echo data, apply masks after TE Dependent ANAlysis.

Run t2smap

This workflow uses multi-echo data to optimally combine data across echoes andto estimate T2* and S0 maps or time series. To see which files are generated by this workflow, check out the workflow documentation: tedana.workflows.t2smap_workflow().

usage: t2smap [-h] -d FILE [FILE ...] -e TE [TE ...] [--mask FILE]
              [--fitmode {all,ts}] [--combmode {t2s,ste}] [--label LABEL]

Named Arguments

-d	Multi-echo dataset for analysis. May be a single file with spatially concatenated data or a set of echo-specific files, in the same order as the TEs are listed in the -e argument.
-e	Echo times (in ms). E.g., 15.0 39.0 63.0
--mask	Binary mask of voxels to include in TE Dependent ANAlysis. Must be in the same space as data.
--fitmode	Possible choices: all, ts Monoexponential model fitting scheme. “all” means that the model is fit, per voxel, across all timepoints. “ts” means that the model is fit, per voxel and per timepoint. Default: “all”
--combmode	Possible choices: t2s, ste Combination scheme for TEs: t2s (Posse 1999, default), ste (Poser) Default: “t2s”
--label	Label for output directory.