Learning beyond Site Bias for OOD Generalization in Brain Networks

Abstract

Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-site out-of-distribution (OOD) settings, as site-dependent confounder effects can obscure disease-related connectivity patterns and static functional connectivity (FC) does not explicitly capture informative within-scan variations. In this paper, we propose Cross-site OOD Robust brain nEtwork (CORE), a unified framework for brain network learning across unseen sites. First, CORE estimates site-specific confounder effects and aggregates the resulting deconfounders by cross-source reliability for unseen-site correction, while extracting a population scaffold of reproducible label-associated connections from site-wise residualized FC. It then summarizes temporal variations on scaffold edges into compact descriptors for line-graph modeling. Finally, prior-guided subject-adaptive gating modulates message passing, balancing population priors with individual variability. Extensive leave-one-site-out experiments on ABIDE, REST-meta-MDD, SRPBS, and ABCD demonstrate that CORE consistently outperforms competitive baselines, with up to a 10.3% relative improvement in accuracy. These gains also persist across different brain parcellation schemes on ABIDE.