Hello!

I'm trying to run some analyses that are conceptually similar to a PPI and parametric modulation. In a nutshell, I'm going to be using the trial-by-trial beta parameter from my anticipatory (cue) epoch as a parametric regressor for my feedback (FB) epoch. So the first step is to run individual level analyses to get the trial-by-trial coefficients for my cue epoch.

My task has an anticipatory (cue) epoch, then a feedback (FB) epoch. It has 72 trials and 3 runs. I need to run an individual level analysis that gives me a coefficient for the cue epoch for each of the 72 trials, so essentially i'd have analysis with 72 sub-briks, one for each cue onset of each trial.

Hopefully that makes sense.

Where I'm getting stuck is how best to model the regressors- I think what I want is 72 separate 1D files, each filled with the onset:duration of the cue epoch for that trial (1-72). However, I'm unsure what these should look like. For example, do I want for the first trial something like this?

9:2
*
*
This would account for the fact that this cue onset:duration trial is in run 1. Or do I just want something like this

9:2

which would simply be the onset:duration for the cue event for trial.

the next tricky thing is figuring out how to code this, but that I can figure out on my own. Unless you know of a nice tool that's already programmed to split out your 1D files into trial-by-trial regressors!

I'm more than happy to provide any other information that would be helpful. Thank you so much!!