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Dear SPM experts, Basically a repost, but as I didn't get any answer so far... Assume several conditions, each of them parametrically modulated (same objective values across the whole experiment for all these conditions with a range of, say 0 - 10 and a mean of 5). Now some of the trials might have to be excluded due to wrong responses, so some of the values might be missing / underrepresentated. Thus the average value might be different between conditions, say a mean of 5 for condition 1 (as planned) and maybe only 4 for condition 2. One runs into the same problem when having several runs with the same conditions. The mean of condition 1 / run 1 might be 5, but for the 2nd run it might be something else. Now, when comparing the standard regressors (or averaging across runs) it seems to me that one should adjust them = bring them to "the same level" by adding the betas of the parametric modulator to the betas of the standard regressor till they correspond to the same mean value. What do you think? Best, Helmut