tedana minimally requires:
- Acquired echo times (in milliseconds)
- Functional datasets equal to the number of acquired echoes
But you can supply many other options, viewable with tedana -h, ica_reclassify -h, or t2smap -h.
For most use cases, we recommend that users call tedana from within existing fMRI preprocessing pipelines such as fMRIPrep or afni_proc.py. fMRIPrep currently supports optimal combination through tedana, but not the full multi-echo denoising pipeline, although there are plans underway to integrate it. In the meantime, if you plan to use fMRIPrep and tedana together, please see collecting fMRIPrepped data.
Users can also construct their own preprocessing pipelines from which to call tedana; for recommendations on doing so, see our general guidelines for constructing ME-EPI pipelines.
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 outputs page: https://tedana.readthedocs.io/en/latest/outputs.html
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.
ica_reclassify takes the output of tedana and can be used to manually reclassify components, re-save denoised classifications following the new classifications, and log the changes in all relevant output files. The output files are the same as for tedana: https://tedana.readthedocs.io/en/latest/outputs.html
This workflow uses multi-echo data to optimally combine data across echoes and to estimate T2* and S0 maps or time series. To see which files are generated by this workflow, check out the workflow documentation: :pytedana.workflows.t2smap_workflow.