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Hi Todd, I'm a bit confused about temp derivatives as well. Please do correct me if i'm wrong but this is what i understood correctly or incorrectly from current and previous posts. Using temporal derivatives will increase the statistics of the main EV by correcting SLIGHT mismatches. the derivative will regress out mismatch so that the unexplained data doesnt creep into the residuals and decrease the z-stats. it wont affect the PE value of the main EV. However,your PE value and hence z-scores will be reduced if there is a quite a bit of shift between the model and response. So , we can take the information from the temporal derivative to construct a better model. if there is a mismatch , then you can see it in the timeseries and peristimulus plots in the 1st level FEAT report. i.e look at the difference in shape of the full model fit and Main EV model fit. You can also , as Vince suggested, combine the Main EV and derivative in the form of an f-stat. The plots should again show you an adjusted fit. But you cant do a group analysis in GLM using f-test copes, which is what Vince and Eugene stated. As an alternative you can create Latency maps as in the henson paper. You can also specify the derivative contrast to be calculated along with the Main EV. This can show the regions where there is a mismatch(latency maps can show you where and how much) ,which might be interesting eg: differences in timing between primary and secondary areas of the brain. You can also feed the individual temp derivative images to the 2nd level. I'm not sure about the information that the group mean derivative image gives but may be it represents the consistent mismatch between model and stimulus timing. -Vish