HI Gang, Yes - that is exactly what I would like to do. Here is the specific outline.

For each subject:
1.I create artificial 'time-courses' for each condition of interest. Each beta series will contain the coef sub-bricks corresponding to individual stimuli within a specific condition. So if I have two conditions, I would have two separate time course files
2.For each condtion beta series, I run 3dNetCorr with an roi atlas and output the pearson and fisher z correlation matrices.
3.Then i will run additional analysis on subject-specific matrices (potentially network analysis), as well as group analysis on the matrices.
(BTW I just read your MBA paper, very cool!!)

Here are some of my questions
1. So my main needs are to figure out the best way to handle beta values that are outside the normal range (you said [-2, +2]). When I did a visual inspection on- one subject's beta series, there were two obvious spurious/giant betas, that corresponded to trials that had over half of their TRs removed. So those were easy to remove. However 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. Since you had suggested masking out larger beta-values, I am trying to figure out what you mean exactly... For instance, some voxels might have a large beta value for one stimulus/ ("timepoint") but for all other timepoints the beta value might be fine. Would you still get rid of such a voxel?
If so, what is the correct command to create such a mask?

2. How exactly do you interpret scaled betas that are greater than +/-2 ? The goal of beta series analysis is to exploit the variability in one beta series to see if there are other series throughout the brain that it is correlated with, ie covaries with. So is there an argument for not masking out those beta values? (I apologize, i still don't have a strong enough understanding of collinearity and the size of betas, to know whether this is a ridiculous thought.)

3.An alternative analysis approach I would be interested in running is to use gPPI, (rather than or in addition to the beta series method) to assess condition-specific connectivity. However, 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?

Thank you guys!