Dear Dr. McLaren (and anyone else experienced with gPPI),
I apologize in advance for the long, and hopefully not too confusing email, but any advice or feedback would be appreciated.
We have been conducting analyses using your gPPI toolbox and have found some interesting results. Specifically we conducted 3-way psycho-physio-physiological interaction, testing if the time series from the seed region is differentially correlated with the rest of the brain as a function of our experimental task (condition A vs. condition B) and a time series from another ROI. After specifying this model on an individual level, we enter the beta images from the 3-way interaction term into a one sample t test and see significant results at the group level.
We were interested in probing the interaction to further understand the results. Although we are not aware of a standard method for probing any type of PPI results, we have ideas for how to do so but had a couple concerns we wanted to get your thoughts on. Basically, we know from the GLM that when you have a significant interaction, the beta coefficient for a continuous variable in the model describes the slope for that independent variable when the other variables in the equation are zero. (If stress interacts with gender to predict depression, and gender is coded (male = 0, and female = 1) the beta for stress in the model containing stress, gender, and stress X gender would represent the relationship between stress and depression in males). Further, you can deduce the slope for females by adding the beta for the interaction term to the slope for males. We were interested applying this principle to data generated with your gPPI toolbox.
Along with beta images for the 3-way interaction term, your toolbox generates beta images for all repressors in the model. We thought that it may be possible to extract parameters for the cluster identified with our one sample t test from the beta images for the time series in our seed region, which would give us the slope for the relationship between our seed and our identified cluster when the experimental task and time series from the second ROI are zero. Then we would extract parameters from the same cluster for the beta images associated with the interaction terms to determine how this slope changes as a function of experimental condition and BOLD signal in the second ROI.
Our concerns are threefold. First, would this method be feasible, and would extracting parameters from the beta images associated with the other regressors in the model be giving us values that would be representative of the betas derived from a GLM with a significant interaction produced by SPSS or other statistical software. Second, with regard to the experimental task (condition A vs. condition B) although at the single subject level one condition is modeled as more positive and the other is modeled as less positive, the task is modeled continuously from (-0.14426) to (1.44326) (which are generated by the toolbox). Do you think it would be meaningful to probe the effects at experimental condition = 1 and experimental condition = 0? Finally, we were interested to know if there may be some sort of between subjects vs within subjects effects going on in SPM during the group level models that we are not aware of that may complicate interpretations of this type.
Any advice or feedback would be appreciated. Thanks for your time.
Adam Gorka & Annchen Knodt
Laboratory of NeuroGenetics
Duke University
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