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Bob:

The Bayesian deconvolution in the PPI script has generally performed quite well, although nothing is infallible. I am not that surprised that modeling trials as events vs. boxcars would make this difference since you are in a sense telling the deconvolution that the underlying neuronal activity is different.

One way to decide if the deconvolution is inaccurate would be to take PPI.xn (the deconvolved "neural" activity), convolve it with an hrf and interpolate it from microtime to TR time. Then you can compare the resulting "reconstituted" BOLD data with your original eigenvariate. I'd be interested to hear what you find.

Darren



On Thu, Sep 15, 2011 at 2:32 AM, Bob Spunt <[log in to unmask]> wrote:
Dear SPM experts,

When conducting PPI with event-related designs, I've found that the
PPI regressors are dramatically affected by how trials are modeled,
i.e. either with a delta (duration = 0) or boxcar (duration = stimulus
offset minus onset) function. For example, the attached plots show PPI
regressors from identical seeds in identical designs, where the only
parameter varying is the use of a delta vs. boxcar function to model
the stimulation (trials varied in length from about 3-5 seconds). The
correlation between these regressors is only about .50. This seems
surprisingly low.

The reason I bring this up because, when one is merely concerned with
modeling the psychological response, the difference between modeling
trials as delta vs. boxcar functions is often small (although
certainly not insignificant; Grinband et al., 2008, NeuroImage).
However, this difference seems to be amplified when doing PPI with
event-related designs. Is this a reasonable conclusion?

Thanks in advance for any comments,
Bob

-------------------------------------------------------------------------------
Bob Spunt
Postdoctoral Fellow
Social Cognitive Neuroscience Lab - www.scn.ucla.edu
Department of Psychology
University of California, Los Angeles



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Darren Gitelman, MD
Northwestern University
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