Population-Regularized Learning of Individualized Brain Effective Connectivity

Brain effective connectivity (EC) characterizes directed interactions among brain regions, but individualized EC estimation remains challenging with limited and noisy resting-state functional magnetic resonance imaging (rs-fMRI) data. Although population-level connectivity patterns shared among related subjects can provide stable guidance, existing population-informed methods rely largely on global priors that overlook pairwise inter-subject relevance. We propose PR-EC, a population-regularized framework for individualized EC estimation. PR-EC constructs a multimodal similarity graph that encodes pairwise inter-subject relevance, and aggregates the EC estimates of each target’s most relevant neighbors into a subject-specific population reference. This reference provides stable population guidance and is used to refine each target subject’s EC estimate. Because data-quality variations can affect EC edges differently, PR-EC further introduces a quality-control (QC)-aware refinement that adaptively modulates the contribution of the population reference to each edge. Theoretical analysis characterizes the trade-off between leveraging cross-subject information and preserving individual-specific connectivity. Extensive experiments on three datasets show that PR-EC improves EC-based disease classification. Cross-cohort analyses further reveal reproducible autism spectrum disorder (ASD)-related EC alterations as biomarkers, including bidirectionally enhanced thalamo-temporal connectivity.