Hi folks,

I have a few questions regarding interpreting general linear contrast results from 3dLME. Briefly, I have used 3dLME to perform a VBM-style analysis of structural imaging data. My design includes 5 groups of participants, with different numbers of participants per group, and I’ve performed linear contrasts between each pair of groups.

My first question involves the critical t-statistic–I was surprised to see that it was the same for all my contrasts (corresponding to an uncorrected p<0.001), even though the sizes of the two groups differed for each contrast. I suspect that this is because each orthogonal contrast is assigned degrees of freedom=1. Can you confirm this, and is there no way to take into account group sizes when determining the critical t-stat? It seems odd to use the same threshold for two contrasts that involve different total sample sizes.

Additionally, I would appreciate your advice on multiple comparisons correction. My first inclination was to use FDR correction and threshold all my contrasts of interest to correspond to q<0.05. However, I have noticed that for my better-powered contrasts (i.e., those involving larger group sizes), the FDR correction appears very lenient. In a few cases, the t-statistic corresponding to q<0.05 is actually smaller than that corresponding to p<0.05. Do such results indicate that the FDR curves are inappropriately estimated?

As an alternative to FDR, I have opted for correction to a cluster-wise alpha level of p<0.05. To do so, I ran 3dFWHMx on the 4-D residuals from my model and then ran 3dClustSim with the spatial auto-correlation parameters to determine an appropriate combination of cluster threshold and uncorrected p-value. This is appealing in some ways–for example, it is very simple to apply the same critical t-statistic to all my contrast maps. As above, though, I worry that thresholding all my contrasts at the same voxelwise p-value will be more conservative for some contrasts than for others. If you see problems with my approach and/or would recommend alternatives, I’d appreciate the advice.

Thanks,

Jeff