MALM: Learning Latent Network Mediators from Paired Connectomes

Mediation analysis begins with specifying the mediator, which defines the mechanism being tested. In brain-network mediation, structural connectivity (SC) and functional connectivity (FC) are often treated as candidate network mediators, but they are coupled, noisy, and complementary measurements of brain connectivity, making parallel or serial multimodal formulations potentially restrictive. We propose MALM, an end-to-end Bayesian framework that learns an integrated network mediator from paired SC and FC and uses that learned network directly as the mediator. Its central contribution is to couple multimodal representation learning with exposure-to-mediator and mediator-to-outcome pathway estimation on the same region-pair coordinates. Dual cross-modal attention integrates the observed networks, while a structured mediation layer connects the mediator to closed-form direct, indirect, and total effects with an edge-level decomposition, without imposing an inter-modality ordering. Joint posterior inference quantifies uncertainty over model unknowns and induced mediation quantities, including learned network mediators, pathway maps, global effects, and active mediating brain edges. On synthetic data, MALM improves effect estimation and pathway recovery when connectome views are coupled, interact non-additively, or violate a fixed serial ordering. In HCP and ABCD cohorts, MALM estimates significant connectivity-mediated effects in both cohorts and recovers a substantially more consistent mediating network than existing methods, concentrated in default-mode, visual, and somatomotor systems and supporting a mediation pathway that remains stable across developmental stages.