I am wondering which tools should I use for the post-processing of resting-state fMRI after preprocessing with fMRIPrep. I would like to be able to calculate several representative indices like functional connectivity, ALFF, and ReHo.
I know there are several tools for post-processing such as XCP-D, Nilearn, ENIGMA HALFpipe, and so on. Of course, which tools we use will depend on our research objectives. I don’t think it’s easy to say what is recommended, but I’d be happy to hear your expert opinion on what advantages and disadvantages each one has.
I think which ever tool you find easiest to use is the one you should go with (provided they are actively maintained software with no critical bugs).
I’d personally recommend XCP_D for ALFF and ReHo. If you are interested in atlas-based connectivity, XCP_D also provides this. If seed-based whole brain connectivity is your goal, you can use Nilearn (e.g., Seed-based connectivity on the surface - Nilearn)
I haven’t used ENIGMA. I go to Nilearn or Fitlins for first-level task-based fMRI GLMs, and then Nilearn for second-level models.