Hi AFNI wizards,

I am returning to an old dataset I previously undertook an activation analysis on to run some ROI-to-ROI FC network analyses and have a few questions (further task context below if needed):

1a. How does 3dDeconvolve determine the duration(#of TRs) of its modeled HDR function from an stim-times_AM(1) 'dmBLOCK(1)' set of stim-times in the output .xmat stimulus columns? e.g. is it a function or set number of TRs or something else to account for the long tail of the HDR?

2a. Is it possible to use a variation of 3dDevonvolve -fitts output to isolate a single condition's relevant BOLD signal from 1 or more other task conditions, or is 3dSynthesize & 3dCalc (per Gang's [afni.nimh.nih.gov]) to remove the effects/conditions of no interest from the original signal, the correct approach? In other words: can you select the best fit estimated BOLD data of a single condition out from the full model, or can you only model the effects of no interest to remove and use what is left behind?

2b. I recall reading somewhere in a help file or powerpoint that 3dSynthesize & 3dCalc script approach might be "out of date". Is there an updated process or does this merely refer to a newer python composite script?

Further context: The task is a variable-duration event-related auditory discrimination task. There are 3 randomly interleaved conditions (Strong, Weak, Silence) I would like to separate and compare the various ROI-to-ROI correlations within the network, across the group, yet between the conditions. Ex: Is FC stronger or weaker between ROI-1 & ROI-5 during strong stimuli or weak stimuli? Current 3dDeconvolve script and example stim-times file attached.

Thank you for tackling several different questions. You guys really provide a wonderful resource and support system to the community here.

~Dane



Edited 2 time(s). Last edit at 01/08/2020 12:57AM by daanderson.