Hi, Jiaxu-

Great, that clarifies things.

I think C3 is the first priority: comparing that the seedbased maps from average ROIs, as calculated in AFNI and Conn, really do match. This *should* be something that matches really quite closely, voxel-per-voxel, to something like floating point precision. If that is different... we need to investigate. You can load the analogous volumes of Pearson correlation (or Z-transformed) maps as overlay and underlay, and click around. Or, use 3dcalc to subtract the volumes, and check the size of difference:

3dcalc -a DSET_CORR_AFNI -b DSET_CORR_CONN -expr 'a-b' -prefix DIFF_CORR

Thanks for clarifying the thresholding values in B1.

For B4: that seems fine for 3dClustSim, yes, assuming that the same mask is used from what was applied to calculate those smoothness values with 3dFWHMx. The ACF parameters represent the average spatial smoothness of noise in the data throughout a masked region (e.g., the brain or a GM mask). You can then use 3dClustSim to take that smoothness information to estimate what kinds of clusters a "noise-only" set of data would make in that same region; you then use your voxelwise p-value and desired FPR to pick what cluster threshold you want---but for consistency, you have to maintain the same region from which the ACF parameters were estimated.

So, I hope the voxelwise correlation maps from the two softwares are essentially the same. At the next stage---that of clustering---I suspect there will be differences if the smoothness distribution assumptions are different (again, everyone used to assume Gaussian, but AFNI changed to the mixed ACF to be more general because that seemed to be a necessary/better way to approach this), as well as due to the fact that RFT and 3dClustSim's brute-force simulations are different techniques. While the difference of techniques will surely create *some* difference, I might expect the assumptions of noise distributions in each to be a larger factor (if they are different), as well as the features of how a neighborhood is defined (not: in AFNI you can set this to be whatever you want, so you can match whatever the other toolbox is using, but I suspect they might not be the same ab initio---this is not an error on either's part, just a different but reasonable choice by either). Importantly, verifying that the sidedness of testing is equivalent is important. In AFNI, the default will be 2sided at the voxelwise level, and it probably makes the most sense to use bisided at the clusterwise level---these are appropriate for most hypotheses in the field, but surprisingly not so widely adopted as defaults (the reasons for the former and the surprise about the latter are elucidated in the "A tail of two sides..." paper, cited above).

--pt