Larry,

I may be oversimplifying your situation, but let me give it a shot. There are two effects of interest, A and B, plus a baseline effect C. You have strong statistical evidences of showing both A > C and B > C, but the statistic evidence for A > B is pretty weak or at least not strong enough to be convincing based on the commonly adopted criterion. For example, suppose that the effects for A, B and C are 0.9%, 0.8%, and 0.2% signal change. You managed to gather statistical evidence for both A > C and B > C; furthermore, you do see a bigger cluster for effect A than B, relative to C, when artificially dichotomizing the evidence with a preset threshold. However, it is no surprise that you have difficulty of showing A > B because the difference between A and B is relatively small, compared to the differences between A and C and between B and C. Is this a more or less accurate description about the situation?

> In our recent experiment, though, with 20 participants, linear contrasts found no difference for
> tasty vs. healthy foods.
...
> The two conditions both activate an area significantly above baseline, each with considerable breadth of activation,
> but there are no differences in signal intensity between them, perhaps because of how the
> BOLD response gets squashed as it reaches asymptotic levels. Thus, no significant clusters emerge.

You probably do see some differences, but the crucial issue here is that the statistical evidence for those differences are not strong enough to reach the commonly accepted comfort zone. In the conventional statistical terminology, the "statistical power" is relatively low. Put it differently, if you set a voxel-wise two-sided p-threshold to 0.1 (and forget about FWE correction), do you see anything about those differences?

Gang



Edited 2 time(s). Last edit at 05/17/2019 06:19AM by Gang.