Hi Paul,

the thing is that I get much shorter autocorrelation values when running "-regress_bandpass 0.01 0.23" (0.23 Hz is the Nyquist frequency in my case) compared to the automatically computed "-regress_polort" option by AFNI. The latter option is automatically computed because I did not include this option with a specific value, such as "-regress_polort 2" in my former AFNI proc script. The run only has 144 TRs with a TR of 2.16s.

So I was wondering if the automatic polort option "fails" in my case. What do I mean by failing? A colleague of mine had the idea that potentially slow frequencies drive or impact the higher autocorrelation across the subjects (in the absence of running bandpassing), whereas the autocorrelation massively drops when running bandpassing in AFNI proc (due to exluding everything below 0.01 Hz).

I am not sure where exactly impacts from the scanner cause pseudocorrelation that are not really related to the brain. I know that the lower cut-off is often 0.007 - 0.01 Hz. So when exluding everything below 0.01 Hz with bandpassing, I am not sure if I basically exclude real autocorrelation that comes from the brain, or if I correctly remove scanner noise etc.

As I understand it now, the general approach is as follows:
-regress_polort DEGREE: generally the better approach to better preserve DOF. It also takes care of slow scanner drift impacts etc.
-regress_bandpass x y: might be required in certain cases, like in my case when computing the autocorrelation.

Would you agree with the above?



Edited 2 time(s). Last edit at 02/28/2023 11:58AM by Philipp.