[tedana] How do I use tedana with fMRIPrepped data?

fMRIPrep outputs the preprocessed, optimally-combined fMRI data, rather than echo-wise data. This means that you cannot use the standard fMRIPrep outputs with tedana for multi-echo denoising.

However, as long as you still have access to fMRIPrep’s working directory, you can extract the partially-preprocessed echo-wise data, along with the necessary transform file to linearly transform that echo-wise data into the native structural scan space. The transform from that structural scan space to standard space will already be available, so you can easily chain these transforms to get your echo-wise data (or, more importantly, the scanner-space multi-echo denoised data) into standard space.

Unfortunately, fMRIPrep’s working directory structure is not stable across versions, so writing code to grab the relevant files from the working directory is a bit of a moving target. Nevertheless, we have some code (thanks to Julio Peraza) that works for version 20.2.1.


We will try to keep the following gist up-to-date, but there is no guarantee that it will work for a given version. Use it with caution!

If you do find that the gist isn’t working for an fMRIPrep version >= 20.2.1, please comment on Issue #717 (even if it’s closed) and we will take a look at the problem.

[tedana] ICA has failed to converge.

The TEDICA step may fail to converge if TEDPCA is either too strict (i.e., there are too few components) or too lenient (there are too many).

In our experience, this may happen when preprocessing has not been applied to the data, or when improper steps have been applied to the data (e.g., distortion correction, rescaling, nuisance regression). If you are confident that your data have been preprocessed correctly prior to applying tedana, and you encounter this problem, please submit a question to NeuroStars.

[tedana] I think that some BOLD ICA components have been misclassified as noise.

tedana allows users to manually specify accepted components when calling the pipeline. You can use the --manacc argument to specify the indices of components to accept.

[tedana] Why isn’t v3.2 of the component selection algorithm supported in tedana?

There is a lot of solid logic behind the updated version of the TEDICA component selection algorithm, first added to the original ME-ICA codebase here by Dr. Prantik Kundu. However, we (the tedana developers) have encountered certain difficulties with this method (e.g., misclassified components) and the method itself has yet to be validated in any papers, posters, etc., which is why we have chosen to archive the v3.2 code, with the goal of revisiting it when tedana is more stable.

Anyone interested in using v3.2 may compile and install an earlier release (<=0.0.4) of tedana.

[tedana] What is the warning about duecredit?

duecredit is a python package that is used, but not required by tedana. These warnings do not affect any of the processing within the tedana. To avoid this warning, you can install duecredit with pip install duecredit. For more information about duecredit and concerns about the citation and visibility of software or methods, visit the duecredit GitHub repository.

[ME-fMRI] Does multi-echo fMRI require more radio frequency pulses?

While multi-echo does lead to collecting more images during each TR (one per echo), there is still only a single radiofrequency pulse per TR. This means that there is no change in the specific absorption rate (SAR) limits for the participant.

[ME-fMRI] Can I combine multiband (simultaneous multislice) with multi-echo fMRI?

Yes, these techniques are complementary. Multiband fMRI leads to collecting multiple slices within a volume simultaneously, while multi-echo fMRI is instead related to collecting multiple unique volumes. These techniques can be combined to reduce the TR in a multi-echo sequence.