Hi,
I am trying to do a general functional connectivity analysis as described in:
Elliott, Maxwell L., et al. "General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks." NeuroImage 189 (2019): 516-532.

However, I had questions when I was doing 3dDeconvolve and bandpassing. I have seen two methods, one generating bandpass regressors via 1dBport and using these regressors in 3dDeconvolve, and then running anaticor, as below:
"Method 1"
3dDeconvolve -input pb00.$subj.r*.tcat+tlrc.HEAD \
-censor motion_${subj}_censor.1D \
-ortvec bandpass_rall.1D bandpass \
-polort A -float

The other method ("Method 2") involves using 3dDeconvolve without the bandpass regressors, running anaticor, and then using 3drsfc with nodetrend, as below:
3dRSFC -prefix gfcanalysis_output -nodetrend 0.01 0.1 errts.test.$subj.fanaticor+tlrc

Using these two analyses steps results in somewhat different results that appear to cover similar regions when subsequently calculating seed-based RSFC, but are certainly not identical. Is there rationale for choosing one method over the other? I thought that doing Method 1 might be more sound given that there is only one regression step, but I am unsure and don't have too much experience in conducting RSFC analyses.

Any help would be greatly appreciated, thank you !!