I have a dataset consisting of individuals scanned at two time points 7 years apart.  At the first time point, a single MPRAGE scan was collected for each individual; at the second, the same MPRAGE scan was collected, as well as an additional set of 4 shorter MPRAGE scans (collected with the aim of increasing measurement reliability).  I’m looking to determine the best way to model the data with the longitudinal pipeline in order to detect reliable change between the two time points (there is test-retest data in a subset at both time points for assessing this). 

 

I’m specifically wondering whether it makes sense to compute a base “template” using all 6 scans from a given individual at once (i.e. the matching MPRAGE from tp1 and tp2 + the 4 short MPRAGEs from tp2).  My intuition is that doing so would bias the template toward tp2 given that 5/6 images are from that time point, which is undesirable.  I am also not sure how the different acquisition types affect things, as the shorter scans do produce systematically [slightly] different measurements.  The plan is rely primarily on residual change scores for these analyses, to account for the slight differences in the acquisitions.

 

Any thoughts / suggestions are appreciated – thanks!