Hi,
I have multi-echo (1 run, 3 echoes) resting state fMRI data collected in an older-adult, movement-prone, clinical population. I am wondering if anybody has insight on whether fmriprep or AFNI is preferred for multi-echo processing.
I am not aware of any head-to-head comparisons between AFNI and fMRIPrep for multi-echo fMRI processing, but I think the developers of both tools have put a lot of effort into processing multi-echo data specifically, so I doubt there are any major issues with how either tool handles multi-echo data.
Both afni_proc.py and fMRIPrep rely on tedana (for which I am one of the developers) for the multi-echo-specific steps (i.e., T2* estimation and optimal combination), and I think both pipelines apply (or at least have the option to apply) the same transforms to all echoes, so either one should work fine for your data.
Use the one where you are more likely to have access to experienced or semi-experienced users. All analysis packages have quirks. While neurostars and https://discuss.afni.nimh.nih.gov/ are great places to get answers, having someone you can show your analysis script to for a sanity check is invaluable.
I can personally sing the praises of AFNI and tell you why it’s superior, but I also work down the hall from the lead AFNI developers (see my first recommendation).
That all said, there is a philosophical difference between FMRIPrep and AFNI. The goal of fMRIPrep is to provide processing pipelines that the developers deem state-of-the-art and that reserachers can run with minimal user input. This means, if you want to do one of the types of analysis fMRIPrep was designed to do, it can be very convenient.
AFNI is a suite of tools that are designed to help users stay close to their data while processing it. There are wrapper scripts that are similar in scope to fMRIPREP (see
Processing, evaluating, and understanding FMRI data with afni_proc.py | Imaging Neuroscience | MIT Press ), but many more options are available to the user. This means a user can do nearly anything with AFNI. In fact a non-trivial portion of fMRIPrep runs AFNI commands under-the-hood, but it also means a new user without support might have a bit more of a learning curve.
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Thanks for the help! I’ve decided to use AFNI since tedana is more easily integrated into afniproc.py.
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