Hi there,

I'd like to check with you my approach to using 3dGC. My data consists of people watching movies and we've got annotations for every word spoken (onset, duration and whether they are low or high context words). I'm interested in exploring whether there are any differences in connectivity for the TTG between the two types of words (both seed-to-target and target-to-seed). Already read [afni.nimh.nih.gov] and [afni.nimh.nih.gov], which inspired the following steps:

After 3dDeconvolve to obtain an estimated IRF for two types of words, used 3dmaskave to average the IRF inside the TTG ROI. Then, I used waver to convolve my estimated IRF back with the time onsets of the words:

waver -dt 1 -FILE 1 TTG_high_face.1D -tstim `cat movie_high_words_delay4_decon.1D` -numout 5470 > high_convolved_ts.1D
waver -dt 1 -FILE 1 TTG_low_face.1D -tstim `cat movie_low_words_delay4_decon.1D` -numout 5470 > low_convolved_ts.1D

TTG_high_face.1D - the average IRF inside the TTG for high context words
TTG_low_face.1D - the average IRF inside the TTG for low context words

Words are not separated equally in time, as these are normal movies. Our time onset resolution goes to 1 decimal place - e.g. 84.5

Am I correct in saying that the above output from waver should be my seed timeseries that goes into 3dGC? My approach seems to differ from what I've read about in the PPI analysis, since I haven't convolved a TS with any Gamma function and used the raw ts (after pre-processing) to do the first deconvolution. I wanted to check if I'm going down the right route.

Thanks for the help,
Florin