Hi all,

I post this question for some statistical discussion on the nature of PPI (psychophysical interaction analysis).
During PPI analysis, the task regressor was also entered as a covariate to tease apart the connectivity originating co-activation during task.
The task regressor was modeled using hymodynamic functions such as GAMMA.
As the hymodynamic function can not fit our brain response very well, the task regressor can not explain all the co-activation variance.
Thus, the PPI we got may still be a by-product of task-coactivation.

So how about do the PPI in two steps?
First, we use a TENT function to best fit the activation in every TR during a trial to best explain the variance induced by task condition and all other covariates.
Second, we use the residual time-course in the first step to do PPI analysis.

Of course, statistically, the two step can be done within one GLM.
The main point is the necessity of using TENT to best explain the task-induced activation.

I would appreciate if you can shed some lights to me.

BEST,
lz