Hi,

I am not exactly sure what you mean. Different sessions always use different regressors, that is the normal scenario, they are just "named" in the same way. For example, in the Setup tab covariates (1st-level) you would enter as a "realignment" 1st-level covariate a separate file per subject and session containing the estimated position of that subject in the scanner during that session (e.g. a different rp_*.txt file for each run/session estimated during the realignment preprocessing step), and then during denoising you would indicate "realignment" as one of your potential confounding effects so that CONN will remove (again, separately for each subject and for each session) the effect of that covariate from the BOLD signal.

Hope this helps

Alfonso

Originally posted by hkeglovi:

Hello, Thank you for any assistance you can provide. We have a single-subject design where different sessions use different regressors which we would like to analyze. However, I am not sure how to go about setting up denoising, as it seems the code only ever looks at session 1 for what covariate names are possible (see screenshot of line in question from conn_process). Has anyone worked around this / have any suggestions for best denoising practices? Thank you in advance!