Hi all,
I have a fMRI task comprising 4 conditions and 6 sessions.
I am trying to build a first-level design matrix for each subject in which distinct regressors encode correct responses and errors in the different conditions.
The problem is that for some subjects and some sessions, no errors are committed. This means that my error regressors are empty for some sessions (e.g., there may be events for sessions 1, 3, 4 and 5 but not 2 and 6).
It seems like there are two ways of dealing with this:
1 – don’t specify a regressor if the error condition contains no events in a given session
2 – create a dummy regressor in which there is a single value for the last timepoint (as suggested on another post)
I have two questions:
1 – is one approach better than the other? Seems to me like option 1 is preferable.
2 – what is the best way of handling contrasts in this case? I could specify separate contrast estimates for each session, then create an average contrast across only the sessions in which the contrast is valid. However, if there are data for 3 sessions in one subject and data for 6 sessions in another subject, will this create scaling problems or does SPM8 already account for this?
Thanks for your help,
Ari
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