Hi AFNI experts,

We are using afni_proc.py to pre-process our resting state fMRI data by following the example 11. We set the motion limit as 0.2 as recommended in the note. We acquired a total of 5 fMRI runs for investigating the dynamic functional connectivity changes across the time, and therefor we don’t want to concatenate the runs together. But we found that the motion artifact was getting greater across the time, and even worse for the last run. The number of TRs per run survived after censoring can be sometimes less than half of the total number of TRs per run. This will seriously affect the later calculation of correlation matrix using 3dNetCorr. We would like to ask a few questions of how to save these data:

- We know that we can increase the motion limit to 0.3 or above to preserve more TRs from censoring. But what is the appropriate limit that we should set? Are there any criteria to set that limit, e.g. up to 0.5 or any value?
- Apart from censoring (strictly reset all corrupted volumes as 0), is there any other correction method (e.g. interpolation calculation) implemented in afni to interpolate the corrupted time points?
- If we use the non-censored dataset for the calculation of correlation matrix, will it affect the result interpretation? Maybe the correlation coefficients are due to the noise residuals but not from the spontaneous brain activity?

We would like to save as much data as we can. Please feel free to give us any advice or suggestion.

Thanks & regards,

Angel