Hi Gang - quick (hah!) question:

I have a complex task that I am calculating gPPI on 2 conditions, with the following event of interest structure:

ConditionA

initiation

outcome1

outcome2

ConditionB

initiation

outcome1

outcome2

At the individual level, I am calculating seed to whole brain gPPI and I have 8 a priori regions of interest that I’d like to use your new Bayesian ROI group method. My question is: Which stats should I enter into the group analysis?

Should I use the PPIinteraction>baseline effects, or should I do post hoc t-tests (say, ‘SYM: +initiationA -initiationB’)? These are the tests I’m interested in running for each ROI:

+initiationA -initiationB

+outcome1A +outcome2A -outcome1B -outcome2B (main effect of condition)

+outcome1A -outcome2A +outcome1B -outcome2B (main effect of reply type)

+outcome1A -outcome2A -outcome1B +outcome2B (interaction)

+outcome1A -outcome2A

+outcome1A -outcome1B

+outcome2A -outcome2B

So, for each participant, I would like to have each of those contrasts within each ROI. For each event, I could extract the mean Z value for eventPPI>baseline or I can extract the mean Z from the post hoc t-tests. Do you know which would be better?

Also, just a heads up, I have two groups. So I’m also interested in the effect of group on the above tests as well and, to make it even more complicated, I’d like to add a continuous (e.g., age) or interval (e.g., likert-type scale) variable as well.

This seems like a good fit for your bayes multilevel model, but in that paper it only looks like you use one group and one effect. I always seem to be making your new analyses much more complicated

Also, related question: for a voxelwise group PPI analysis would you recommend I take the PPI>baseline or individual level contrasts to the group level. With the PPI>baseline then I’d have a lot of repeated measures and I should probably use 3dLME, whereas with the contrasts I could probably get away with 3dMEMA.

Thanks!!

Dustin