Assuming multiple sessions or runs- the common practice is either to concatenate the runs (or sessions) or adding them to one design matrix, with a regressor for the run.
Another approach would be to build a hierarchial model, with a layer for the sessions.
Would this better implemented with FSL? AFNI? SPM?
Why is this less commonly used than the mentioned practices?
The answer, from FSL doc:
A mixed-effects analysis would assume that the sessions are randomly sampled from a “population” of sessions that that subject could produce. This includes estimation of each subject’s session-to-session variance. However, it is common for only a small number of sessions to be collected for each subject, making estimation of each subject’s session-to-session variance impractical. One solution to this is to assume a common session-to-session variance for all subjects, thereby providing enough data for the session-to-session variance to be estimated. However, this has a downside in that you lose information about which subjects are good (i.e. low variance) and which subjects are bad (i.e. high variance). Hence, when only a small number of sessions has been collected for each subject (say, less than 10), it is recommended that you use a fixed-effects analysis at the mid-level. This in effect treats the multiple first-level sessions (for each subject) as if they were one long session. Although this does ignore the session-session variability, it is arguable that this is not of interest anyway (this is a somewhat philosophical debate). In short, fixed-effects is favoured as it avoids practical problems associated with esimating the session-to-session variance (when there are not many sessions per subject), at the same time as maintaining information about which subjects are good and bad.
(Sorry for the delay, @ChrisGorgolewski )