Hello Paul,

Sorry for the late reply. I was on a longer break.

Regarding your questions:

A) Yes. I would like to evaluate the spikiness in the HIP before any data processing
B) I guess I am not sure. My thoughts are that I would like to have a QC measure that reflects how 'bad' the HIP as an ROI is affected by motion artifacts. From what I understand motion can affect different locations of the brain to a different degree (e.g worse in PFC than in the center of the brain). For my analysis I can not censor any volumes because it requires a temporal continuity of the scans. I am also only interested in the HIP for now. So I would hate to exclude subjects that based on a whole brain motion measure like norm or srms have to be excluded when maybe the motion in the HIP itself is not as bad and the signal in the HIP could be still used for my analysis. Does that make sense to you? Now when you are saying that a temporal synchronization indicates most likely a motion artifact then this is probably something I would want to be considered in my summary measure.
C) I had no idea about the relationship you describe between a low baseline variability in the time series and high spikiness. But it does make a lot of sense to me. In this context I understand better why we typically use the outlier fraction count.
D) As mentioned in B, I can not censor my time series because I need temporal continuity for my analysis. So I would like to use this spikiness measure only as an indicator of how bad the motion artifacts are in the HIP and then to exclude entire subjects when the motion is too bad.

Best
Carolin