Dear Jiri

Regarding (1), if by "Iron * Amyloid" you are referring to the interaction between those two measures (e.g. you hypothesized that connectivity may be weaker than expected when both measures are low) then, in addition to your "Iron" and "Amyloid" covariates you would first also define another second-level covariate as "IronAmyloid" and enter in the 'values' field the expression "Iron .* Amyloid" (without the quotes) so that this new covariate contains the product of the other two covariate values. Then, in your second-level model you would select AllSubjects, Iron, Amyloid, and IronAmyloid and enter a [0 0 0 1] between-subjects contrast. If, in addition, you would like to control for potential differences between MRI systems, then simply select instead AllSubjects, Iron, Amyloid, IronAmyloid, and ScannerType and enter a [0 0 0 1 0] contrast.

Regarding (2), yes, if you want to perform separate analyses that include only a subset of subjects, there are several exactly-equivalent ways to do this. The first, and the one we typically recommend, is by creating a new set of second-level covariates that only have values for these subjects and contain 0 values for all other subjects. When all of the subject-effect variables selected in your second-level model contain zeros for some subjects those subjects are automatically eliminated from the analysis (you can check that this is happening by looking at the design matrix). In your case, for example, you would perform the analyses above by selecting 'SubjectsOld', 'IronOld', 'AmyloidOld', and 'IronAmyloidOld' and entering a [0 0 0 1] contrast (where all of these covariates contain some values for the older subjects, and zero's for the younger subjects). This is the way we typically recommend performing these analyses, but there is an alternative that is simpler in many cases (but in can be a bit confusing to new users), which is to use the "missing-values" capabilities in CONN. Basically, CONN can automatically eliminate subjects from some second-level analyses if some of the entered subject-effects in your second-level model contain "missing-values" (which are indicated by NaN values). This can be used to your advantage when you want to perform separate analyses only over a subset of subjects by creating a single new covariate, e.g. named GroupOldOnly, that includes 1's for the subjects that you want to include, and "NaN" (without the quotes), for the subjects that you do not want to include. Then, when you define your second-level analysis that includes the GroupOldOnly, Iron, Amyloid, and IronAmyloid effects (contrast [0 0 0 1]), the younger subjects will be automatically eliminated from these analyses without having to create new IronOld, AmyloidOld, etc. variables.  

Hope this helps
Alfonso 

Originally posted by Jiri van Bergen: