Hello,

I'm a student analyzing my first fMRI data set, and I'm running into some problems that I'm hoping someone here can lend some insight into.

The problem seems to be with the way that I am modeling the data for the 3dDeconvolve call. My experiment consists of 3 block types: Study, Test, and Baseline. Within the test, I have 5 different conditions, and have modeled each of them. I have 5 runs, each consisting of study block, a test block, a baseline block, a study block, and a test block, always in that order and each having a total of 199 dynamics. I have concatenated these runs. My TR for the runs is 2 seconds. My study and test blocks consist of trials that are each 3 seconds with a .5 second ITI. There is also a 2 second interval between each block.

In order to model the study and test, I'm using stim times files and tent functions. I have also modeled the 2 second Inter-Block interval. I have not modeled the baseline block, which to my understanding, should make it an implicit baseline. The problem that I am having is that my beta co-efficients all seem to be hovering around 0. This is concerning because previous work in our lab suggests that, at least in a few of our ROI's, there should be an increase relative to baseline in the study condition. I'm hoping that I'm just modeling the deconvolve call incorrectly. I've included an abridged version of the call below, as well as an example of my stim time files. I have also checked my motion sensor vector, and it mostly consists of 1's, so I don't think that I'm just censoring out most of my time-points.

3dDeconvolve call:

3dDeconvolve -input run_all_blur1+tlrc -polort 6 -concat '1D: 0 199 398 597 796' -local_times \
-censor motion_censor_vector.txt \
-num_stimts 13 -GOFORIT 20 \
-stim_times 13 stimtimes.13.1D 'TENT(0,18,10)' -stim_label 13 "Study" \
-num_glt 11 \
-gltsym 'SYM: +Study[2..6 ]' -glt_label 11 "Study" \
-jobs 12 \
-nocout -nobout -tout -fout -bucket DeconvTPS_4 \
-xjpeg model.jpg

Stim Times file:

128.00 149.00 366.50 *
377.00 *
117.50 142.00 *
124.50 152.50 *
128.00 135.00 366.50 380.50 *

Thanks for your time!