Dear AFNI experts,
I am interested in looking at the resting-state component of the time series during task-based fMRI data. So for task-based fMRI, we are trying to find what parts of the time-series match the task design, and we are ignoring all of the residual error which in this case actually is the resting-state component of the time series. I understand how can we sort out task-related activation using proc.py's regress function, but I was wondering if it is possible to investigate the residual error (non task-related time-series) portion of the time series using AFNI. If it is possible, could you please let me know how can we do that?
Best,
JW
