Hi,

Concerning Fisher transforms. AFNI proposed that we do it before running seed-based analysis. Why is it so important to have Gaussianity for seed-based analysis?

I have come upon some situations where I think it creates false results. Under the seed, the correlation coefficients should be really high, because the seed should be well connected with itself. Let's say ~1. However, after the Fisher correction, in my case, it becomes 1.6 in one group, and 1.1 in another, and it results in significant difference in the seed region between the two groups. Obviously, I can't state that connection of the seed with itself is different in both groups! Then how do I know that Fisher transforming the data is a good idea?


Thank you very much!