Dear AFNI experts,

I have a question on how to handle inter-session variability in performing a one-sample t-test.

To give more detail, each participant came in twice for scanning experiment, which showed a visual (e.g., face) stimulus for each trial. Basically, I would like to get a map of a main effect (no contrast is involved) of the visual stimulus onset activity from all participants, collapsing across both sessions (that is, I would like to ignore any session effect). Because there are two separate sessions, I preprocess them separately up to scaling.

1. One way that I implemented this issue is to put all data at once in 3dDeconvolve (as if treating separate sessions as separate runs). But I wasn't sure whether this approach can account for the separate scanning sessions.

2. The other way would be deconvolve each session scan separately. But with two dataset showing visual onset activity per participant, how can I test for a main effect of visual stimulus activity against 0?

Thanks in advance!

SJ



Edited 1 time(s). Last edit at 02/12/2016 09:02AM by sjmich.