using 3dLSS after 3dDeconvolve

Hi,

I have being trying to make 3dLSS work for the past couple of days with no success. In Mumford (2012) paper, they suggested using a Least Square Separate approach, in which you estimate beta separately for each trial of interest with all the other trials as the other regressor.

I have an experiment in which 32 experimental conditions were presented twice in one run and 6 runs in total. Therefore, I have 32 timing flies with 6 rows and 2 time point in each row for each of the 32 conditions. I used afni_proc.py to perform the 3dDeconvolve by including 32 timing files (2 time points in each of the 6 runs). Because it is a fast event design, I want to further use 3dLSS to estimate a better beta for each of the 32 experimental conditions. I suppose I should run 32 times 3dDeconvolve followed by 3dLSS with one of the 32 conditions included as -stim_times_IM stimulus each time. OR should I just do 3dDeconvolve once with one big -stim_times_IM, in which the timing file is 6 rows with 64 time points in each row? How can I use LSS.1D to get the better beta values for each experimental condition?

The code I was using is as following.

3dDeconvolve -input pb04.\$subj.r*.scale+orig.HEAD \ # 6 runs of data
-censor censor_\$subj_combined_2.1D \
-polort A -float \
-local_times \
-num_stimts 2 \
-stim_times_IM 1 condition1.txt ‘BLOCK(1, 1)’ \ # condition1.txt contains 6 rows with two time points in each row
-stim_label 1 condition1 \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored X.nocensor.xmat.1D \
-fitts fitts.\$subj \
-errts errts.\$subj \
-cbucket all_betas.\$subj \
-bucket stats.\$subj

3dLSS -matrix X.xmat.1D -save1D X.LSS.1D

Thank you so much!

Best,

Zhengang

Hello

You need to run only one big 3dDeconvolve modelling all the trials individually, i.e. the 3226 in one single model. However, you could also run 3dDeconvolve and 3dLSS for each individual run since 3dDeconvolve is only used to create the design matrix for 3dLSS. Of course, 3dLSS will have to be run for each run separately too.

Hope this helps,
Cesar