Hi Rick,

Thank you very much for the very quick and helpful response! The script works like a charm now, but I still have a couple of questions.

A bit of background concerning the study:

- four conditions NMA NMS HMA HMS for a total of 160 stimuli
- 4 functional runs with 40 stimuli each + 56 null events of 10 secs (14 x run)
- TR = 2
- each stimulus comprises a video clip of 10 s + a response time (2 s)
- each stimulus starts with a fixation cross (500 - 4000m, jittered)

I am trying to employ @stim_analyze to maximize the efficiency of my design, but I have some questions I have been unable to solve:

1) What is the best way to model the response time (2s) after each video? Shall I add this as a separate condition? (at the moment I included this in the stimuli duration -12s total- but I really do not think this is optimal)

2) At the moment I did not model explicitly the null events or the fixation cross, but I simply "added" their duration in the run length. Is this correct?

3) I am a bit at loss in interpreting the outcome of 1dplot X.xmat.1D (attached); I understand that the first 4 rows are the 4 conditions, and then there are six rows for each run, but I do not understand what the x axis measures and why six rows per run?

4) out of curiosity: what exactly is a seed (i.e., how is the sequence randomization built from it)?

Thanks in advance for your valuable input.

Laura

############## SCRIPT ###############################

# /bin/tcsh

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

# ---------------------------------------------------------------------------
# some experiment parameters (most can be inserted directly into the
# make_random_timing.py command)

set num_stim = 4
set num_runs = 4
set pre_rest = 0 # min rest before first stim (for magnet steady state)
set post_rest = 10 # min rest after last stim (for trailing BOLD response)
set min_rest = 0.5 # minimum rest after each stimulus
set max_rest = 4 # max rest after each stimulus
set tr = 2.0 # used in 3dDeconvolve, if not make_random_timing.py

# (options that take multiple values can also take just one if they are
# all the same, such as with this example)

set stim_durs = "12 12 12 12"
set stim_reps = "10 10 10 10" # = 40 stimuli x condition
set run_lengths = "690 690 690 690"
set labels = "NMA NMS HMA HMS"

# ---------------------------------------------------------------------------
# execution parameters
set iterations = 1000 # number of iterations to compare
set seed = 1234567 # initial random seed
set outdir = /home/lama/stim_results # directory that all results are under
set LCfile = NSD_sums # file to store norm. std. dev. sums in

# set pattern = LC # search pattern for LC[0], say
set pattern = 'norm. std.' # search pattern for normalized stdev vals


# ===========================================================================
# start the work
# ===========================================================================

# ------------------------------------------------------------
# recreate $outdir each time

if ( -d $outdir ) then
echo "** removing output directory, $outdir ..."
\rm -fr $outdir
endif

echo "++ creating output directory, $outdir ..."
mkdir $outdir
if ( $status ) then
echo "failure, cannot create output directory, $outdir"
exit
endif

# move into the output directory and begin work
cd $outdir

# create empty LC file
echo -n "" > $LCfile

echo -n "iteration (of $iterations): 0000"

# ------------------------------------------------------------
# run the test many times

foreach iter(`count -digits 4 1 $iterations`)

# make some other random seed

@ seed = $seed + 1

# create randomly ordered stimulus timing files
# (consider: -tr_locked -save_3dd_cmd tempfile)

make_random_timing.py -num_stim $num_stim -stim_dur $stim_durs \
-num_runs $num_runs -run_time $run_lengths \
-num_reps $stim_reps -prefix stimes.$iter \
-pre_stim_rest $pre_rest -post_stim_rest $post_rest \
-min_rest $min_rest \
-max_rest $max_rest \
-stim_labels $labels \
-seed $seed \
-tr $tr \
-show_timing_stats \
-save_3dd_cmd cmd.3dd.$iter \
>& out.mrt.$iter

# consider: sed 's/GAM/"TENT(0,15,7)"/' tempfile > cmd.3dd.$iter
# rm -f tempfile

# now evaluate the stimulus timings

tcsh cmd.3dd.$iter >& out.3dD.$iter

# save the sum of the 3 LC values
set nums = ( `awk -F= '/'"$pattern"'/ {print $2}' out.3dD.${iter}` )

# make a quick ccalc command
set sstr = $nums[1]
foreach num ( $nums[2-] )
set sstr = "$sstr + $num"
end
set num_sum = `ccalc -expr "$sstr"`

echo -n "$num_sum = $sstr : " >> $LCfile
echo "iteration $iter, seed $seed" >> $LCfile

echo -n "\b\b\b\b$iter"
end

echo ""
echo "done, results are in '$outdir', LC sums are in '$LCfile'"
echo consider the command: "sort -n $outdir/$LCfile | head -1"

echo check summary stats with timing_tool.py

# note that if iter 0594 seems to be the best, consider these commands:

timing_tool.py -multi_timing stimes.${iter}_0* \
-run_len $run_lengths -multi_stim_dur $stim_durs \
-multi_show_isi_stats

# run 3d deconvolve
tcsh cmd.3dd.$iter

# plot results
1dplot X.xmat.1D '[6..$]'
1dplot sum_ideal.1D



Edited 1 time(s). Last edit at 05/09/2018 07:50AM by Laura Verga.