Hi, Kausar-

Sorry for the delay. Answering in order of increasing difficulty:

Re. Q2:
It's a good question. Indeed, adding things with units of [deg]^2 and [mm]^2 should make any physicist/engineer's stomach churn. However, in this case, another factor is that: for an object about the size of a human head, rotation by 1 deg ~ translation by 1 mm at the edge. So, if we are considering "largest motion" estimates, which would be at the edge, this equation works out pretty well for human adults. For macaques, mice, etc. it might be a less good approximation-- basically, rotation is perhaps a bit "underweighted" -- but it is still probably pretty reasonable. At the end of the day, we want a parameter that jumps up big if any of the 6 motion parameters is big, while also small if they are all pretty small, and the L2-norm form of the "enorm" parameter does well with that.
Bonus point: the enorm plots in the "mot" block of the QC HTML actually does acknowledge this funny units combination: the units of the plot are "~mm", meaning "approx. mm", because of the above considerations.

Re. Q3:
Pretty big voxels (but not unheard of for human FMRI). One consideration with anaticor is that you *don't* want the tissue-based masks for ANATICOR regressors overlapping with GM, because then you will crush your GM response locally. This is one reason we recommend using eroded white matter (WMe) masks, rather than pure WM, and similarly for ventricles (if using those). So, I hope you are using those? Partial voluming with such large voxels will be a concern. Actually, if you erode the masks, how much of a WM skeleton do you have left for the ANATICOR procedure?

Re. Q1:
That is an interesting way to think about "checking" to see how much anaticor is doing. I have to think about this a bit (drat). But just to be more clear-- could you please describe what exactly you are showing in the matrix plot and in the Pearson correlation value plots? For example, in the matrix plot, did you take the same set of connectomic ROIs and calculate the average time series in each while processing with *just* anaticor (set A) and then separately calculate the average time series in each while processing with anaticor+PCA (set B), and then is this matrix the correlation matrix of set A with set B?

In general, I think you could combine PCA with ANATICOR; I thought some afni_proc.py examples use PCA from ventricles and anaticor from WM? Just remember that as you add more regressors, you use up more degrees of freedom (DF). If you also add in bandpassing (are you suuuuure you need to?), that will use up a HUGE amount. I'm curious how many degrees of freedom you have left after that (esp. if there is any subject motion)? Of course, your friend the afni_proc.py QC HTML can tell you that (and might even warn you about DF usage...).

--pt



Edited 2 time(s). Last edit at 10/15/2020 09:18PM by ptaylor.