It depends what your goal is right now. There are a number of things that most people would want for results they’re interested in publishing that are not yet implemented. For example, thresholding and cluster correction are completely missing, first-level variance estimates are not yet accounted for in second level modeling, and subject-level variables cannot currently be used as confounds.
If you’re looking to explore your data, want to try to write a BIDS model, and potentially want to engage with me to develop the features you need, then FitLins can be useful to you, and you’re likely to learn a fair bit.
But if you want to do standard modeling, it’s probably best to use Nipype, or, if you can find a usable tutorial, one of the standard packages.
I think for now our lab just needs to start with the basic/standard modeling. I will try something in Nipype for now based on the tutorials I’ve seen. Thank you.
As another potential reference, we’ve also put together an example Nipype workflow for post-fMRIPrep first- and second-level models in FSL here. Let us know if there’s anything else that we can help clarify!
However, I just had a question about applying it to my data. I have a task that has two runs. At what point do I combine the two runs in FSL? How does that work? Everything from the notebook above grabs 1 run (I’m assuming the task was one run), and there is nothing about dealing with multiple runs.