Hi,
I have been having a few issues in trying to do a PPI analysis, so I was just wondering if somebody might be able to help clarify things for me.

To briefly introduce my paradigm, we present some to-be-learned items that are high-value, and some that are low-value.  We're trying to do a brain-behavior correlation between PPI beta values representing connectivity at encoding, and the degree to which value influences memory at recall.  We're using the McLaren gPPI approach, where the high-value PPI regressor = 1 and the low-value PPI regressor = -1 in the model.  (Actually, there are 2 separable parts of each trial, so I have two pairs of PPI regressors in the full model, but hopefully that's not a problem.) We're using Matlab scripts developed at UCLA that allow us to compute PPI regressors in SPM while doing the rest of the analysis in FSL, to account for the issue raised in the Gitelman et al. (2003) paper, which, as I understand it, is particularly salient for an event-related design, as we have.

So here are the issues that I'm having with the analysis.  (These are independent issues, so if you can help with just 1 or 2 of them, that would still be very helpful.)

1. In trying to break down the PPI effects across the different conditions, I was given some advice (originating from Jeanette Mumford) that the physiological regressor should be included together with the relevant PPI regressor, instead of just looking at effects of the PPI regressor by itself.  I understand that this puts back effects of non-task-related connectivity that would otherwise be removed in the model, but I'm not sure under what circumstances it's necessary to do that.  If I'm just trying to see what's driving the brain-behavior correlation in the overall PPI effect (e.g., whether it's individual differences in connectivity during encoding of high-value items, or individual differences in connectivity during encoding of low-value items), my intuition is that it's better not to include the physiological regressor, but I'm not entirely sure.  Any advice on this?

2. It seems like most of our PPI effects only show up going in one direction, even though the PPI analysis should theoretically be nondirectional.  So for example, we see a brain-behavior correlation in hippocampus-VLPFC connectivity when using a hippocampal seed, but not when using a VLPFC seed, using the same ROIs.  Would this cause concern about whether the effect is real, or is it typical for PPI?

3. Although we're getting a correlation between the magnitude of the PPI effect and the magnitude of the behavioral effect between individuals, we're not getting a main effect for the PPI, and it's not clear why that is.  I did find some other papers in the PPI literature that reported a within-subjects correlation but no main effect (e.g., Ofen et al., 2012, in J. Neurosci., Cremers et al., 2010, in Neuroimage, and Passamonti et al., 2009, in J. Neurosci.), but it seems like this could still be a major issue with our data.  I'll attach a scatterplot from one of our analyses to help illustrate this. Is there anything non-intuitive about PPI that might lead to this pattern of results?

4. In setting up the PPI analysis, things seem to get more complicated because we're trying to mix FSL and SPM, but there's one particular piece that I'm uncertain about, which I tried implementing in two different ways.  The first approach was to do a nuisance analysis that incorporated all preprocessing steps, and then use the residuals of that analysis as the input for subsequent analyses, including to extract the seed and to run the first-level PPI analysis.  The second approach was to do everything on filtered_func_data from the univariate analysis, as Jill O'Reilly has suggested, while not running a separate nuisance analysis.  My understanding is that when using the gPPI approach, it's not necessary to do the nuisance analysis.  However, when I tried comparing the two methods, all of my correlations seemed a bit stronger when using the nuisance analysis.  I can't think of any way that this approach is "cheating" (we do also add in 6 empty EVs to the first-level PPI model to account for the extra degrees of freedom lost by putting the motion parameters in the nuisance analysis), so I think the nuisance model is just a bit better because it's getting rid of more noise...but again, I'm hoping that somebody who understands this stuff more deeply than I do can tell me if this is reasonable.

Anyway, any guidance that any of you might be able to give would be very helpful...

Many thanks,
Michael







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Michael S. Cohen, M.S., C. Phil
Ph.D. Candidate
Department of Psychology
University of California, Los Angeles
Los Angeles, CA 90095