Combined multi-group and multi-level SEM

I'd like to test whether the variation in the average responses given by participants(the so-called "random intercept" in multilevel model terminology) is different across levels of a specific grouping variable. This seems like something I should be able to do in OpenMx, but I can't figure out how. Specifically, I'm stumped on how to combine multi-group SEM with multi-level SEM, even though I know OpenMx can do each of these separately.

Here's some code that creates a dataset for a reproducible example of the problem I'm facing. The code creates a dataset with 40 participants who each give responses across 20 trials, 10 each in two separate conditions. I want to compare the variance in the participants' average responses across the two conditions.

I've figured out how to fit a multi-level model in OpenMx that estimates the variation in the average participant responses, collapsed across conditions -- I'm just adrift on how to fit multiple multilevel models in the same mxModel, then test whether the variance in the random intercepts ("by_participant.intercept" in the example below) is different across levels of the grouping variable ("condition" in the example below).

---

library(lme4)
library(OpenMx)
fml <- "~ condition + (condition | participant_id)"
d <- expand.grid(participant_id=1:40, trial_num=1:10)
d <- rbind(cbind(d, condition="control"), cbind(d, condition="experimental"))

set.seed(23432)
d <- cbind(d, simulate(formula(fml),
newparams=list(beta=c(0, .5),
theta=c(.5, 0, 0),
sigma=1),
family=gaussian,
newdata=d))

by_participant <- mxModel(
model="by_participant", type="RAM",
latentVars="intercept",
mxData(data.frame(participant_id=unique(d$participant_id)), type="raw", primaryKey="participant_id"),
mxPath(from="intercept", arrows=2, values=1)
)

overall_model <- mxModel(
model="overall_model", type="RAM", by_participant,
manifestVars="sim_1",
mxData(d, type="raw"),
mxPath(from="one", to="sim_1", arrows=1, free=TRUE),
mxPath(from="sim_1", arrows=2, values=1),
mxPath(from="by_participant.intercept", to="sim_1", arrows=1, free=FALSE, values=1, joinKey="participant_id")
)

fit <- mxRun(overall_model)
summary(fit)