Dear AFNI experts,
I am new to AFNI and am trying to work out how to do the clustering on group level correctly.
From the handouts related to this topic I learned that afni_proc.py automatically estimates the smoothness of my data. In the $subj.results folder, there is a sub folder “files_ACF” containing an out.3dFWHMx.ACF.errts.r0*.1D file for each of my three runs. All three files have 4 columns, but the number of rows varies between subjects (and sometimes runs). So my first question is: what do rows and columns represent?
Secondly, is there any mask used as default to obtain these smoothness values? If so, what mask would that be?
The handout further states that the average of the 3 ACF parameters across subjects should be computed. Again, I am not entirely sure which of the four columns represents the 3 ACF parameters. Further, given that I currently have values separated for my three runs, would I firstly average within a run (over all rows), then within a subject (over all three runs) and then average across all subjects? Or should I take the values for “blur estimates (ACF)” from the out.ss_reviw.$subj and simply average them? These values seem to be the same as the ones saved in the blur_est.$subj.1D file; however, they don’t match the output saved in blur.errts.1D file.
If I was interested in performing ROI analyses, would I then have to redo the smoothness estimation 3dFWHMx using the errts.$subj.fanaticor file as input and use my ROI (e.g. bilateral hippocampus) as mask to then only estimate the smoothness within this ROI and then also use the same mask in the 3dClustSim specifications? Would I then also have to repeat this procedure for all of my a priori ROIs? In a similar manner, if I use a GM mask in my group-level analysis, would I have to separately estimate the smoothness within my GM mask?
I am looking forward to your clarifications.