For outliers, probably better to deal with them at the ROI level after you average the voxels within each ROI. As for computing the correlations, I don't have a perfect solution, but maybe censor out those trials with outlying values for such an ROI (and its pairing ones)? I'm not sure if there is an easy mechanism in 3dNetCorr to achieve this.

> there are other voxel timeseries that sometimes have beta values outside of the +/-2 interval, but not so extreme. Like beta values of 3,4,5,6...etc.

I was just using [-2, 2] as an example, you could decide as to what range would be empirically reasonable.

> is there an argument for not masking out those beta values?

At the moment I cannot come up with a decent solution for correlation matrix computation. As a modeler I definitely would prefer a more principled way to handle this than hard threholding, and it would be a much larger undertaking.

> I need to be able to run gPPI with a brain roi/parcellation scheme (ie. ROI to ROI connectivity), rather than seed ROI to all other voxels. Is there a way to do this in AFNI?

How about running the conventional seed-based PPI at the whole brain level, and then extracting the ROIs you are interested?

Gang