Tedana

Tedana stands for TE Dependency ANAlysis and is the main workhorse of the MEICA package.

Usage: tedana.py [options]

Options:

 -h, --help            show this help message and exit
 -d DATA, --orig_data=DATA
                       Spatially Concatenated Multi-Echo Dataset
 -e TES, --TEs=TES     Echo times (in ms) ex: 15,39,63
 --mix=MIXM            Mixing matrix. If not provided, ME-PCA & ME-ICA (MDP)
                       is done.
 --manacc=MANACC       Comma separated list of manually accepted components
 --kdaw=KDAW           Dimensionality augmentation weight (Kappa). Default
                       10. -1 for low-dimensional ICA
 --rdaw=RDAW           Dimensionality augmentation weight (Rho). Default 1.
                       -1 for low-dimensional ICA
 --conv=CONV           Convergence limit. Default 2.5e-5
 --sourceTEs=STE       Source TEs for models. ex: -ste 2,3 ; -ste 0 for all,
                       -1 for opt. com. Default -1.
 --denoiseTE=E2D       TE to denoise. Default middle
 --initcost=INITCOST   Initial cost func. for ICA:
                       pow3,tanh(default),gaus,skew
 --finalcost=FINALCOST
                       Final cost func, same opts. as initial
 --stabilize           Stabilize convergence by reducing dimensionality, for
                       low quality data
 --noignored           Remove ignored components from denoised timeseries as
                       well.
 --fout                Output TE-dependence Kappa/Rho SPMs
 --label=LABEL         Label for output directory.
 --seed=SEED           Seed used for ICA. Default 42.