Hi AFNI folks,

Thanks for helping me think through this. In standard practice, we use 3dFWHMx to estimate the ACF parameters based on the residuals of the first-level "fixed effects" model. If we are, say, using SPM or FSL, we do this for each participant's first-level model and take the average (or median) ACF parameter to use in the 3dClustSim step. I think I have a tenuous understanding of why this is a good estimate of the spatial smoothness of noise or error in a way that's useful for producing the null spatial distribution, but as you'll see, it's not strong enough for me to reason with confidence about the following situation.

Most of the time, we're interested in a second level model where the brain data is the outcome, i.e., the dependent variable. But what if we do a whole-brain voxel-wise regression with the brain data as the independent variable? Is the spatial smoothness of the residuals at the first-level still a good estimate for the group-level regression: DV ~ BRAIN? In case it needs to be said, in this case DV will be the same for every voxel.

~John