Hello AFNI experts -

I am developing a rapid event event related design with jittered ITI and random stimulus presentation. The experiment is as follows:

Cue(variable time) - task stimulus (fixed time) - ITI (variable time). There are 2 cue conditions (C1, C2) and 2 task conditions (T1, T2). T1 always follows C1 and T2 always follows C2. There will also be catch trials of C1 and C2. Random stimulus presentation may therefore look like the following:

C1T1... C2T2......C1T1...C2.....C2T2....C2....C1.......C1T2

Based on the examples listed here I believe the "Catch Trial" example is most appropriate. I've included my code below.

I am having a few issues:
1. First, is my interpretation of this example appropriate for the task I've described?
2. I need my stimuli to be TR locked and I must have a minimum TR of 2.5s. Ideally this would be facilitated through a combination of jittered cue and jittered ITI, with fixed task. For example...
Full trial: 1.25 cue + 1.5 task + 2.25 ITI = 5 s
Full trial: 1.75 cue + 1.5 task + 1.75 ITI = 7.5 s
Catch trial: 1.75 cue + 3.25 ITI = 5 s
However, I've been unable to incorporate this type of variable jitter into a stimulus, and so the math never quite works out to allow me to have appropriate timing for both my catch trials+rest and my full trials+rest while fitting within the bounds of a multiple of my 2.5s TR. Are there any suggestions for workarounds?

Thank you for your assistance,
Kathryn




#!/bin/tcsh

# try to find reasonable random event related timing given the experimental
# parameters

# ---------------------------------------------------------------------------
#num_stims: 4 (C1, C2, T1, T2)
#stim_dur: C1: 1.25 C2: 1.25 / T1: 1.25 T2: 1.25 (for initial analysis T1, T2 = 1.25*2 = 2500)
#run_time: [(1.25*9 + 1.25*9 + 2.5*16 + 2.5*16 + (9+9+16+16)*2.5)] * 2 = 705
#tr_locked: TR = 2.5
# ---------------------------------------------------------------------------

# ---------------------------------------------------------------------------
#generate stimulus timing for 4 classes
# ---------------------------------------------------------------------------
make_random_timing.py -num_stim 4 -num_runs 4 -run_time 705 \
-stim_dur 1.25 1.25 2.5 2.5 -num_reps 9 9 16 16 \
-stim_labels C1 C2 T1 T2 -min_rest 2.5 -tr_locked -tr 2.5 -seed 54321 \
-prefix stimesG

# ---------------------------------------------------------------------------
#separate 'catch' trials from the main events and sort
# ---------------------------------------------------------------------------
1dcat stimesG_C1.1D stimesG_T1.1D > stimesG_C1T1_all.1D
1dcat stimesG_C2.1D stimesG_T2.1D > stimesG_C2T2_all.1D

timing_tool.py -timing stimesG_C1T1_all.1D -sort \
-write_timing stimesG_C1T1_sorted.1D
timing_tool.py -timing stimesG_C2T2_all.1D -sort \
-write_timing stimesG_C2T2_sorted.1D

# ---------------------------------------------------------------------------
#get stim times for 'main' regressors by adding 1.25 seconds to every time
#run make_random_timing.py again, adding an offset of 1.25 seconds to all times
#save 3dDeconvolve command to run with -nodata
# ---------------------------------------------------------------------------
make_random_timing.py -num_stim 4 -num_runs 4 -run_time 705 \
-stim_dur 1.25 1.25 2.5 2.5 -num_reps 9 9 16 16 \
-stim_labels C1 C2 T1 T2 -min_rest 2500 -tr_locked -tr 2.5 -seed 54321 \
-offset 1.25 - save_3dd_cmd @cmd.3dd.G -prefix stimesG

# ---------------------------------------------------------------------------
#Fix the 3dDeconvolve command in @cmd.3dd.G
#Use timing files for stimesG_C1T1_sorted.1D and stimesG_C2T2_sorted.1D
# ---------------------------------------------------------------------------