Hi, Jiaxu-

(I'm going to rearrange my order of questions, but the lettering matches the previous.)

(A) OK, good.

(C1) So, if you use 3dNetCorr to calculate a wholebrain correlation map, that is done by averaging the time series within each ROI and calculating each map. That is fine---is that what you want?
(C2) And did you make the perform an analogous calculation in Conn, or did you put the output from C1 into the Conn clustering, as well?
(C3) If you did calculate the wholebrain correlation map separately in Conn, were those pre-threshold results the exact same?

(B) Well, if you use different methods to estimate the clustering threshold, you will likely get different estimates; in theory, they should be close. If you are using different software, there might be other differences involved, some of which are controllable.
(B1) Is that threshold the p-value threshold for voxelwise thresholding? If so, that isn't very small... I would expect larger differences between software for larger p-values.
(B2) I would guess that Conn uses a different neighborhood definition than AFNI's default, like described here:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/FATCAT/MakingROIs.html#getting-to-know-your-neighbors
--but it is up to you to check this to be sure. This can make a difference of 10-20% in cluster size. But if the way clusters/neighborhoods are formed is different, then this is not really an "apples to apples" comparison.
(B3) The sidedness of testing with thresholding matters. In AFNI, you can use 1sided, 2sided or bisided thresholding; by default, we use 2sided tests and 2sided or bisided thresholding as default. If you use a pair of 1sided tests without correction (which is unfortunately the default in many software packages---I am not sure about Conn), then you are artificially doubling your false positive rate, at a minimum. See:
https://pubmed.ncbi.nlm.nih.gov/30265768/
for more details.
(B4) What is your exact 3dClustSim command? Are you using the mixed ACF model, described here:
https://pubmed.ncbi.nlm.nih.gov/28420798/
https://pubmed.ncbi.nlm.nih.gov/28398812/
Based on a useful point in a paper on clustering, we did adjust the assumption made for clustering that the spatial autocorrelation function of noise in FMRI is well-approximated by a Gaussian (this assumption was basically made by all softwares for a long time, and it might still be assumed in some). We now estimate a more suitable "mixed ACF" function, that allows for the heavier tails that seem to be present. This could be another notable difference between the two programs, even on top of the different underlying methods.

And again, seeing the AFNI commands used will clarify a lot here.

--pt