Galit,

> Originally, I was thinking of adding RTs as a predictor to the analysis, but then
> I reckoned it was a bad idea as it correlates with the Condition predictor. I see
> that in Example2 in the documentation, RT is added as a random effect. What
> is the meaning of RT being a random effect?

This is a very good question! When the RT is correlated with a within-subject factor (Condition in your case), I recommend that you center the RT value within each Condition level before you feed the RT values into 3dLME. Even though the following page only covers the situation with as between-subjects factor (multiple groups), it might be helpful to understand the concept and mechanism:

[afni.nimh.nih.gov]

The point of incorporating RT as an explanatory variable is to account for RT variability across subjects *within* each level (condition in your case).

> In analogy, an R formula would look something like this:
> lmer(BOLD ~ Cond*RT + age + (Cond|RT))

The model for Exam 2 is actually this:

lmer(BOLD ~ Cond*RT + age + (RT|subjects)

Other models are possible, but currently not strictly implemented:

mer(BOLD ~ Cond*RT + age + (RT|subjects) + (Cond|subjects)

Gang