Hello AFNI Experts,

I am planning on doing a dynamic resting state functional connectivity analysis. I have a number of questions related to this, bear with me!

1.) The papers that I have read regress CSF and WM as well as bandpass, so I plan on doing example # 11 with afni proc py adding back in the bandpass. Some papers have used used the CONN tool box, which does the bandpass as a second step after regressing out CSF, WM, motion, outliers, etc. Is there an advantage to doing bandpass as a second step or am I okay with keeping it in the first-level regression model?
2.) Is there a way/possible pipeline you might propose for conducting the sliding-window dynamic resting state connectivity analysis using AFNI tools? For example, lets say I wanted to segment the timecourse into 36 s windows, sliding the onset of each window by 18 secs (not sure how to go about this step in AFNI), then make whole brain correlation maps for each time course (which I know there are a number of tools I could use, 3dfim+, 3dTcorr, etc.), Fischers Z transform (easy enough to do with AFNI), then estimate dynamic connectivity by taking the standard deviation in beta values at each voxel (unsure, perhaps a 3dcalc function?)
3.) I am combining two data sets with resting state data. One data set has a 6 minute scan and the other has a 5 minute scan. In the group analysis, I will be conducting a regression analysis, examining whether resting state connectivity is predictive of psychiatric symptoms. Will I run into issues with the data sets having a different length? Is it necessary to truncate the longer scan to make them equal length?

Thanks and sorry for the long message!

Emily