Hello Paul,

Thank you very much for your reply.

I apologize if I repeat my question but I want to make sure that I made clear that I am trying to summarize the amount of spikes in the HIP based on the spikiness measure ‘s’ (output from 3dDespike). I am wondering if using 3dToutcount is appropriate to do so in this case.
So I am not looking at the raw signal of the time series and try to determine outliers but rather I am looking at the spikiness measure ‘s’ from 3dDespike that was calculated by already fitting a smooth-ish curve to the raw signal in each voxel and determining the degree of deviation of each time point under consideration of MAD and standard deviation.
I am not sure conceptually if it makes sense to determine outliers (in this case spikes) in this signal based on calculating again a trend over this spikiness measure and the deviations from this trend. I thought that an outlier (in this case spike) is determined based on the value of s (default in afni >2.5) so it would make more sense to me to now just count the fraction of voxels at each time point where s exceeds lets say 2.5.
Or are you saying that I should detrend the raw time series before running 3dDespike?
Or that what I am trying to do is not a good idea and I should just use 3dToutcount on the raw signal with the -mask option and limit the analysis to the HIP?

I love the idea of also looking and comparing the BOLD % signal change in the HIP voxels as an additional measure for QC. And looking at ReHo. I have not heard of this measure before. Thank you very much for these suggestions!

Carolin