Thank you for swinging back around, and especially patience/perspective with the follow up questions below.

~Dane

Regarding dmUBlock: My understanding was that this was a variant used for PPI analyses where you had a psychological measure/performance value that varied and could be applied as a potential moderating factor. Unfortunately while our task actually has "strong" vs. "weak" stimuli the performance data was so good we had a ceiling effect and insufficient variance to utilize such an approach.

Re dmUBLOCK(-X): Our stimuli does not have a consistent duration as mentioned. The nature of the audio stim mean they vary between 8 and 10 seconds yet contain the same number of auditory tones (an intended control) depending time between tones. Is there a significant reason/benefit to treat all stimuli a single length (potentially ignoring TRs of stimuli), rather than use dmBlock_AM1 and instruct AFNI to fit the duration of each event using the "Onset:duration" formatting?

Re Original 3dDeconvolve HDR length inquiry: In the attached image you can see a visualization using excel of TRs (rows) broken down by stimuli condition along side the 3 HDR models from 3dDeconcolve's .xmat.1D output column's. My mentor noticed that the columnar models both tend to combine trials that are close together and also append 7 or 8 TRs to the end of each stimuli period, and wanted to know how these are computed since length varies so much.

Re 3dSynthesize: Thank you for the warning and recommendation. It is noted and will be discussed. If we decide to proceed, than I believe our process would be the pipeline below. Please let me know if you see any holes or issues, and if '-cenfill none' option sounds correct.

A. Remove effects of no interest via your 3dSythn "-cenfill none" +3dCalc with for each condition separately (ex: [Strong] - [Weak + Silence + 6 degree Movement + Censors])
B. 3dMaskave our ROIs
C. Use the .xmat.1D column TR periods for each condition (as in excel above) to create 0/1 condition time masks for trimming the .1D signals
D. Remove all 0's to Combine condition periods into contiguous blocks.
E. Compare the ROI-to-ROI correlations between condition's blocks.