Hi Alfonso, many thanks for this info. I had a follow up question about the parametric modulator analysis. In my spm.mat file I have two event-related conditions A and B, each of which has a trialwise parametric modulator that codes continuous ratings of confidence. For each condition I extracted the estimated parametric regressor column from the SPM.xX.X and entered it as a first-level covariate, resulting in two additional covariates (param_conf_A, param_conf_B) along with the standard 'SPM covariates'. If the goal is to look at connectivity differences between the confidence-modulated A and B conditions (and if the setup sounds OK up to this point), I was wondering 1) does it make sense to omit the parametric modulator covariates from the Confounds list in the denoising step, or should they be kept in? 2) is it necessary to run the first-level analysis twice with a different modulator selected under 'other temporal-modulation effects' for each. It seems like only one interaction factor can be selected. I was also trying to determine which option under first-level 'Analysis options' makes most sense. Thanks,

erik