Hi Andrew,

I am possibly missing something about the general context of this question but I believe that in general one requires the two contrasts to be orthogonal in order to have valid results. In your example, the a priori contrast (Y>0, or [0 1]) is not orthogonal to the main contrast (Y-X>0, or [-1 1]) so that is going to result in inflated false positives in your main contrast (i.e. the null hypothesis distribution of "Y-X" values is going to be biased towards positive values when selecting only those connections that survive any "Y > threshold" value). So in this example I imagine that strictly speaking only an a priori contrast [1 1] (looking at average effect across the two groups) would be valid when then testing for between-group differences. That said, in practice this "double-dipping" false positive inflation effect may be really small, particularly given that typically you want to choose a liberal a priori threshold in order not to incurr in excessive false negatives, and in some other cases the false positives may even be reduced instead -e.g. if your a priori contrast selects X>0 instead of Y>0 connections in the example above-, so this might be what you are referring to. On the other hand perhaps all of this discussion is framed in the context of independent samples for the a priori and main contrast tests (and in that case all of the above is irrelevant), or perhaps the NBS measures in general or the permutation tests in particular are somehow more robust to this sort of departures from the a priori assumptions and I have not really thought this through, so please feel free to correct me if I am missing something obvious. 

Best
Alfonso 
Originally posted by Andrew Zalesky: