I hope this isn’t too simple a question - I am using nistats.second_level_model.SecondLevelModel, and want to model both confounds across participants (e.g. age, sex…) and group regressors of interest (e.g. look for regions whose betas correlate with some trait-score (e.g. mindfulness)).
Looking into the .fit() documentation (https://nistats.github.io/modules/generated/nistats.second_level_model.SecondLevelModel.html#nistats.second_level_model.SecondLevelModel.fit)
I see that there is both a confounds argument and a design matrix argument. However, under the description of the arguments, the docs state of the confound argument: “If design_matrix is provided then this argument is ignored.”
Does this mean that we can only model confound regressors, and no group regressors of interest, or have I missed some other chunk of nistats that would simplify this greatly? What is the difference between the two arguments?