Ciao, Paulo!

We must now switch to English, or the conversation would end because I shamefully do not speak Italian. I have to ask you one day where you learned Italian.

Exactly. When I do seed-based functional correlations for one region:
1) I take a region and calculate its mean time series
2) I plop that out to a separate file for later use
3) Only then do I perform spatial smoothing as the final "preprocessing" step
4) Then I take my outputted file and use it to do the correlations

That is my conundrum. I must not do spatial smoothing before calculating the mean time series. And I must do it afterwards and immediately prior to using the calculated mean time series in the correlations. However, the sublime convenience of 3dNetCorr creates a paradox because it performs everything in a single step. Maybe that is just the reality: the convenience of 3dNetCorr may preclude spatial smoothing because everything is done in a single step.

This is gnawing at me a lot lately. Because, the spatial smoothing not only gives us some critical statistical niceness. It obviates the problem of residual geometric distortion present in all fMRI studies if you did not collect a phase image, which I did not collect for later use with epi_b0_correct.py, if the spatial smoothing that you plan to do anyway is larger than the distortion.

Sincerely,

Dante