Hi-

Sooo, this is an inherently tricky thing for multiple reasons.

The traditional way to prepare to do clusterwise correction is to estimate the spatial extent of correlations among the FMRI noise: the â€śnoiseâ€ť signal in task FMRI is the residuals from modeling (in AFNI, often called â€śerrtsâ€ť= â€śerror time seriesâ€ť). In olden times, one estimated the spatial extent of noise as Gaussian; in modern times, we use the â€śACFâ€ť (=autocorrelation function) parameter fitting, which is done using 3dFWHMx (or, even easier, using -regress_est_blur_errts as an afni_proc.py optionâ€”hopefully you are using afni_proc.py to set up your single subject processing!). Once you have the ACF parameters for each subject, you can typically average these across a group (for a given site/acquisition protocol these tend to be quite similar across a group), take the group mask and use 3dClustSim to estimate the size of clusters in simulated noise with those spatial characteristics-- for your desired sidedness of testing (see Chen et al., 2018!), voxelwise p-value threshold and FPR/alpha level, you can see what cluster size the noise-only simulations produced, which becomes your minimum cluster size for your task data. There are still subtleties to this (the residuals are not pure noise; they contain structure from our inability to model the signal perfectly, for example, but such is life).

Now, some subtlety comes in when you have **non-task** FMRI data, such as resting state or naturalistic scans: your output time series of interest after the modeling/regression stage **is** your residual time series! So, we are in the odd situation of not having a separate â€śnoiseâ€ť and â€śsignalâ€ť estimate. What do we do about clustering? Well, we actually default to the above paradigm, the same programs on the same residuals to estimate the clustersize of â€śresidual-onlyâ€ť data for the group. This is somewhat rooted in practicality and in the empirical fact that the spatial estimates of structure in the residuals of resting/naturalistic data are quite similar to those of task data (likely due to our continued inability to make detailed FMRI models; sigh). Anyways, this should still provide a pretty good estimate of the spatial extent of noise-only (or "uncontrolled) structure in the time series; if anything, it may be a conservative estimate of that, because having *real* structure in there would tend to bump up the apparent size of noise-only clusters, making cluster size thresholds more conservative.

For your ISC data, where your actual analysis is on the paired correlation maps, the above seems like a reasonable way to approach clustering, as well. You are basically trying to set a clustersize threshold to ask the question: how big should a cluster be to be likely not due to chance/noise alone? Looking for the spatial extent of â€śnoise-onlyâ€ť in your acquired time series seems a reasonable way to approach that-- this is helped by the practical considerations noted above, including the fact that subjects in the same protocol tend to have similar spatial-extent-of-residual-structure characteristics.

For some explicit code related to these things, you might want to check out these pages:

AFNI code for Taylor et al., 2018

AFNI code for Chen et al., 2018

â€“pt