Hi, forgive me if I have done a poor job searching, but I couldn’t find an example of using afni_proc for a connectivity analysis during a task. I think at one point I had tried conducting a complete task analysis and then using the .errt output from 3dDeconvolve. Then I extracted the timecourse of a seed and conducted a second 3dDeconvolve using the timecourse as the regressor. Is something like that still advisable?
To follow up on my own message, I found the following:
-write_ppi_3dD_scripts : flag: write 3dD scripts for PPI analysis
e.g. -write_ppi_3dD_scripts \
-regress_ppi_stim_files PPI_*.1D some_seed.1D \
-regress_ppi_stim_labels PPI_A PPI_B PPI_C seed
This seems like a useful pathway for me to follow (although I am open to other ideas). Is there more information on how to populate the “some_seed.1D” file? Should I do something akin to 3dmaskdump on the pb04 timecourse from a mask I create and use that as the .1D?
Here is a sample set of scripts for doing a complete gPPI analysis, after a basic analysis has already been run.
See the AFNI_data6/FT_analysis/PPI directory, and start with the main README.txt file/script.
By the way, that afni_proc.py -write_ppi_3dD_scripts option is newer than the PPI scripts in the example.
Thanks, Rick! That’s a helpful example. Just to be sure I understand, in the example it looks like the seed timeseries comes from the .errts file, not from the pb04 scaled timeseries (it’s usually pb04 for me, but could be another value for others . Is that correct?
That is right, it uses the errts. The point is to prevent known BOLD effects from the first level model bleeding into the PPI regressors.