Hello all. I have a fairly simple resting-state functional connectivity analysis that I am doing, where the contrast at the first-level analysis is just a type of stimulation being off or on, let's call these OFF and ON, which alternates several times over the course of each scan. There are several types of this stimulation, which is the group distinction, let's call them A, B, C, D, etc., that I use for second-level group analysis. There are 100 scans in total, comprising the total of A, B, C, etc.

My problem is that I have far fewer than 100 subjects, because many of them contributed multiple scans. Some only contributed one, some may have contributed two to A and one to B, some may have contributed three to C but none to any other condition, etc. How many scans and which conditions they were vary widely subject to subject.

So this sounds like it needs a mixed effects analysis, with subject coded as a random effect, correct? For simplicity in setting up the CONN analysis, I just have each scan as a new "subject" and each "subject" only has one "session". But to do this correctly, would I simply code each scan from the same subject as a new session for that subject, and keep everything else the same? Is it ok that the number of sessions is very unequal from subject to subject, and many (most) have not participated in all conditions A, B, C, etc.? I have all this batch scripted and would like to keep it that way, and I think I see how to do this from the documentation. Thanks,

Karl