Dear experts,
In the current state, when combining multi-echo fMRI, fMRIPrep is using opt_comb from Tedana.
However, if Tedana is already installed in the image, why to not also use the denoising of the same Tedana, or at least to have this denoising optional?
Is there’s some interference of fMRIPrep processing and Tedana denoising?
That is a recommendation from the tedana developers, including myself, for a couple of reasons. First, tedana supports multiple different decision trees, dimensionality estimation methods, etc. and we didn’t want to add all of that functionality to the fMRIPrep command line. Second, tedana’s denoising is not a “solved” issue, and it requires that users review the classification results and potentially modify them.
Instead, we have proposed an fMRIPost-tedana BIDS App, which will take in fMRIPrepped data and apply tedana to that. I haven’t had much time to work on it, but now that fMRIPost-AROMA is working pretty well, it should be possible to implement fMRIPost-tedana without too much work.
Hi @nbeliy
Apart from what @tsalo has indicated, I would add that fMRIprep also returns multiple additional nuisance regressors. Ideally, nuisance regression should be done in a single step (e.g. using 3dTproject in AFNI). Otherwise, performing another nuisance regression step with these extra nuisance regressors in the tedana denoised data could reintroduce some spurious effects.
Check these papers:
https://doi.org/10.1016/j.neuroimage.2013.05.116
https://doi.org/10.1002/hbm.24528
https://doi.org/10.1155/2013/935154
Therefore, since fMRIprep is specifically designed not to perform any denoising step, it is better to return the optimally combined dataset.
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