As a follow-up question, I am running a Mixed Effects model for testing moderating effect of a risk factor X on differences in activations across three conditions of increasing difficulty, that is,

> Y(i,t) ~ t + X + t:X + (1 + t | subject)

where Y(i,t) is estimated beta at a given voxel from subject-level processing for subject i in condition at levels t=0,1,2 of difficulty. The random intercept and slope for t allow for within subject variability as a random effect. There is still variability associated with the estimation of Y(i, t). Is it appropriate to use the inverse of (se^2) [where se is standard error of Y(i, t)] as prior weights at each voxel? Would the between-subjects variance be taken care of by the linear mixed effects modeling here? The 3dMEMA.R file contains the 3dMEMA function, which allows OLS designs that do not incorporate the random intercept/slope.

My plan is to adapt these scripts to use "lmer" instead. At this point, does it make sense I use the 3dMEMA function to estimate between-subjects variance and assign weights as 1/(v + se^2), where v is estimate of variance from 3dMEMA and se is as above.

Thank you.