Thanks, I think my questions weren't quite clear:

1 - I didn't mean for different thresholds or alpha levels, but for different comparisons. For example, if I have a 2x2 design, I might want to look at a main effect for each factor separately, and for the interaction. Each one of these would be a different beta map, so I would to run 3dttest++ on each main effect and on the interaction separately?

2 - As I understand it, the idea of cluster thresholding is using spatial permutation in order to only accept clusters that contiguous enough to be unlikely to appear given the spatial distribution of effects (or, in other words, how likely are we to receive a cluster of a given size, under the null hypothesis that all variance in the data is noise). Thus, the 3dClustSim -acf method just took the measure of the spatial variance, and used that to compute spatial permutations with the right level of smoothness.
But if we are looking at the residuals of the 3dttest on beta maps, then the spatial variation in the residuals should be dependent on how much individual difference there is in the betas--that is, an effect which is similar across all subjects should have smaller residuals that an effect which shows more variation. Does this not mean that the permutation is mixing up cross-subject variance in activation (in betas) with spatial variance in activation? These seem to be two separate sources of variance, and intuitively it's not clear to my why it makes sense to mix the cross-subject variance into the spatial filtering.

Thanks again!