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Dear SPMers, I have got a simple data set of ten subjects scanned twice. What I would like to check is if there was common activation between all the subjects (not interested in changes between sessions). I can see three ways of doing this: 1. Pull all the 20 contrast images into a one sample t-test. This, however seems wrong since each subject is measured twice. The inference should be made about the whole population. There is a reason why we did not scan one person twenty times after all. 2. Average volumes within subjects and do a one sample t-test on those averages. I believe this was suggested before in a similar problem by Donald McLaren in this e-mail https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1003&L=SPM&P=R13537&1=SPM&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 3. Do a mixed effect analysis using Flexible Factorial Design. Two factors: subjects and sessions (dependent) with only the main effects (see the attachment). The are two issues with this: 1) if I understand the e-mail cited above it is not statistically valid 2) after fitting the model when I try to estimate contrast [1 1] (I am not interested in between session effects, just the overall activation) I get "invalid contrast" error. What do you think is the best way to do this? Best, Chris