Thanks, Rick.

Our overall objective is to perform a laterality index on homologous cortical regions on a subject-by-subject basis (e.g. Left M1 and Right M1). In order to do this, we have developed a GUI that lets us read in the 3dclusterize output tables for multiple ROI's and subjects at a time. We use afni_proc.py, but since every participant has a different ACF value, it's more efficient for us at the moment to set the cluster size to 2 at voxel P = .05 (regardless of alpha) and then use the alpha values within the table to determine the significance of the cluster. Otherwise, bash scripting 3dclusterize on all of our ROI's and subjects will become difficult because we would have to put in unique cluster size values for each results file.

Is there another approach we might be able to use to account for each individual ACF without having to change the voxel_thresh for each participant and condition? Is it appropriate to average all of the ACF values and run 3dClustsim on the average?

Thanks