How would you recommend estimating smoothness in fALFF data for plugging smoothness parameters into 3dClustSim? Specifically, what kind of images should we apply 3dFWHMx to in order to get smoothness params? If we were using seed-based connectivity maps, the appropriate images would be the residual images.What is the most appropriate analogy to residual images for fALFF maps?
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The smoothness estimation of relevance here is the spatial scale of noise autocorrelation. For FMRI data, this typically means looking at how the autocorrelation of FMRI time series residuals drops off with distance (inherently, this estimation ignores directionality, so it is performed as a spherically symmetric operation, for better or worse).
It is hard to see how this could be done with anything other than modeling residuals from FMRI time series.
The perplexing thing for resting state FMRI is: the residuals are also the signal of interest! So this creates quite a conundrum IMHO, and I don’t know why more people don’t chat about this… At the moment, I think people just still use the residuals for both spatial noise ACF estimation (as a precursor to cluster size estimation) and then use that same data in their analyses.
We actually recommend using the non-residual data for smoothness estimation of rest data for this reason, and afni_proc.py examples generally show getting both estimates (even just for education). But fortunately, the estimates tend to be pretty similar, so it isn’t usually the end of the world to use the wrong one (occasionally, it does end the world).
The smoothness of the regression inputs tends to be just a touch higher than that of the residuals, for whatever it is worth.