  Brain functional connectome, the collection of interconnected neural circuits
along functional networks, is one of the most cutting edge neuroimaging traits,
and has a potential to play a mediating role within the effect pathway between
an exposure and an outcome. While existing mediation analytic approaches are
capable of providing insight into complex processes, they mainly focus on a
univariate mediator or mediator vector, without considering network-variate
mediators. To fill the methodological gap and accomplish this exciting and
urgent application, in the paper, we propose an integrative mediation analysis
under a Bayesian paradigm with networks entailing the mediation effect. To
parameterize the network measurements, we introduce individually specified
stochastic block models with unknown block allocation, and naturally bridge
effect elements through the latent network mediators induced by the
connectivity weights across network modules. To enable the identification of
truly active mediating components, we simultaneously impose a feature selection
across network mediators. We show the superiority of our model in estimating
different effect components and selecting active mediating network structures.
As a practical illustration of this approach's application to network
neuroscience, we characterize the relationship between a therapeutic
intervention and opioid abstinence as mediated by brain functional
sub-networks.
