I have a question regarding generalized PPI as has been advocated by
Dr. McClaren on this list. If I am understanding correctly,
generalized PPI simply involves estimating multiple PPIs in a single
model. In my case, I have four conditions: A B C D. Therefore, my gPPI
model should include 9 regressors (per session, and not including
additional covariates of no interest, such as motion regressors):
1 seed regressor
4 psych regressors (A, B, C, D)
4 PPI regressors (A, B, C, D)
I would like to estimate the differential connectivity with a seed
region in the contrast A > B. I have already done this in the
traditional way with a psychological regressor based on the comparison
of interest (using SPM5), and just recently tried it using the method
described above. To be clear, here are the steps I took for each
subject:
1. Built PPI regressors (in SPM5) for each condition (against implicit
baseline) separately
2. Estimate a some 1st-level model (for each subject) with the seed,
psychological regressors for each condition (4 total), and PPIs for
each condition (4 total)
3. Generated a contrast image among the PPI coefficients for my task
comparison of interest (A > B)
4. At the group-level, performed a one-sample t-test on participants'
A > B contrast images
Using this method, I am observing dramatically different results than
with the traditional method. Is it plausible that these two approaches
would yield very different results, or is this likely an error in my
procedure?
Please let me know if there is any other information I can provide,
and many thanks in advance for any advice.
Cheers,
Bob
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Bob Spunt
Doctoral Student
Department of Psychology
University of California, Los Angeles
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