# Applying MBA approach to Connectivity-behavior analysis

Greetings,

I'm new to BRMS but interested in tweaking the code underlying MBA to run some type of Connectivity-behavior analysis. The idea would be to look at how for example changes in connectivity between two conditions relates to changes in some behavioral measure.

Assuming the following default code:

``````brm(deltaY ~ 1 + (1|Subj) + (1|ROI1:ROI2) + (1|mm(ROI1, ROI2, weights = cbind(w, w), scale=FALSE)) + (1|mm(ROI1:Subj, ROI2:Subj, weights = cbind(w, w), scale=FALSE)) ...
``````

one can control for covariates by running I believe:

``````brm(deltaY ~ 1 + Covar + (1|Subj) + (1|ROI1:ROI2) + (1|mm(ROI1, ROI2, weights = cbind(w, w), scale=FALSE)) + (1|mm(ROI1:Subj, ROI2:Subj, weights = cbind(w, w), scale=FALSE)) ...
``````

however if purposefully want to investigate behavioral correlates should I run:

``````brm(deltaY ~ 1 + (1|Subj) + (1|ROI1:ROI2:**deltaBehavior**) + (1|mm(ROI1, ROI2, weights = cbind(w, w), scale=FALSE)) + (1|mm(ROI1:Subj, ROI2:Subj, weights = cbind(w, w), scale=FALSE)) ...
``````

if so, would I then just calculate the Average Marginal Effect of deltaBehavior predicting deltaY at each ROI1:RO2 pairing?

@sinandrei Maybe something like the following?

``````brm(deltaY ~ Covar + (1|Subj) + (Covar|ROI1:ROI2) + (Covar|mm(ROI1, ROI2, weights = cbind(w, w), scale=FALSE)) + (Covar|mm(ROI1:Subj, ROI2:Subj, weights = cbind(w, w), scale=FALSE)) ...
``````

Upon further contemplation, I find myself increasingly cautious about relying solely on correlation-based analysis. Consequently, the potential productivity of this approach remains uncertain to me.

Gang Chen

Thank you! My primary interest at the moment just lies in understanding how some of the traditional analyses look like in the rba-mba-brm world.