Appreciate patience with response. Will try to touch on each topic below:

~Dane

Duration modulation: The auditory stimuli (both "Strong" & "Weak") involved the same number of identical tones which varied only in their patterns and inter beat intervals (IBI - 100s of Milliseconds between tone onsets). Practically speaking these differences meant the stimuli duration ranged from 8.5 to 10.5 seconds (with a 2.5sec TR) depending on which patterns and IBI were utilized in creating the stimuli. Or in other words stimuli that varied by as much as an entire TR (and by as much as 3TRs in instances were movement censoring shortened a given stimuli presentation). The attached infographic should also help visualize.

3dDeconvolve options: Yes they can be pretty confusing. Please let me know if any specifics of protocol indicate a different option is warranted and what that may be. We want to be sure we are approaching this correctly and happy to answer questions. As mentioned, we landed on dmBlock given we had randomly ordered condition's stimuli that varied by 1-3 TRs. The dmBlock_AM1 version was selected since the Strong vs. Weak patterns aren't quantifiably different (rather merely classified by past experimental studies on perception of rhythm) and discrimination performance was above 95%. Thoughts?

stimtimes_IM: If we were to employ IM option rather than AM1 as you mentioned in your email, would 3dDeconvolve be able to handle modeling 180 different stimuli events (ie. 60 different stimuli events * 3 conditions - Strong, Weak, Silence) per subject?

HDR Model length: From what you are saying it sounds like it is a linear equation based on duration of each stimuli presentation as provided in the Stim_times file, correct? Can you provide any other infomarion on that what that equation is or perhaps how to find it (both to better understand mechanics and for citing in publication).

Beta Series: This does sound like a safer approach to do the correlation matrix. Appreciate you mentioned. Unfortunately we are also doing an effective connectivity analysis (specifically a uSEM approach via GIMME software Gates et. al., 2010). Correct me if I misunderstand Beta Series Analysis, it outputs something akin to a Z score measure of how ROI BOLD signals predict one another rather than an r score, and we would not have the isolated BOLD signals from each condition to feed into an effective connectivity analysis to estimate likely directional paths. ie. If we want to do effective connectivity it would require something similar to 3dSynthesize+3dcalc to isolate each condition's BOLD signal to have the necessary input for ecMRI predictions, correct?.

3dSynthesize+3dcalc: Any other options beyond this come to mind for untangling the 3 interleaved conditions?