Hi - I am new to nipype and FSL, not great w/ python.
My lab uses a python script as a wrapper for nipype running 1st level analyses, using FSL. We are running SpecifyModel() and we are doing some spatial smoothing from fsl.preprocess.create_susan_smooth()
I am not necessarily certain that it is coming entirely from the smoothing, but at some point in our preprocessing and analysis pipeline, there is some randomness being introduced. If I run the same subjects through the pipeline with the same data, I am ultimately getting slightly different results. Meaning, when I extract univariate responses from an ROI coming from an atlas, the mean magnitudes of GLM betas are different. These results are centered around a difference of 0 between iterations of running through the pipeline.
I am trying to narrow it down to something in fmriprep or the first level analysis, which is where I think it is coming from.
My questions are:
- has someone encountered this before?
- If I want to set a random seed and store the info, then feed the seed into nipype, such that each time I run through the pipeline, the seed is set as the same value, can I do that? Do I have to do this from scratch, or is there some built-in parameter that would allow something like this?
- Since I don’t really know what I’m talking about I might be totally off – is this a problem that I’m trying to resolve in a totally incorrect way?