SI-HGN: Structure-Informed Hypergraph Networks for Virtual Perturbation-Based Effective Connectivity Inference

Effective connectivity (EC) characterizes directed interactions between brain regions, but inferring it from observational BOLD signals remains challenging: invasive stimulation cannot scale to the whole brain, classical methods impose restrictive stationarity or architectural assumptions, and existing data-driven surrogate models lack anatomical grounding and are limited to pairwise interactions.We present SI-HGN, a structure-informed hypergraph network that constructs individualized surrogate brains using structural connectivity (SC) as an anatomical scaffold while learning latent hyperedges to capture coordinated multi-region interactions. Hypergraph-derived edge attention modulates SC-supported anatomical pathways, allowing their contributions to vary across individuals and brain states while preserving anatomical support. Directed EC is then estimated by virtually perturbing individual regions of the frozen surrogate and aggregating the induced response differences across temporal segments of the BOLD time series. On generative benchmarks and macaque connectivity networks, SI-HGN recovers directed topology more accurately than neural perturbational inference and Granger causality (Pearson $r=0.94$ vs. $0.91$ on simulated recurrent systems; $p<0.001$), with consistent gains across complementary recovery metrics. Individualized EC further reveals disease-related network patterns in Alzheimer's disease and autism cohorts and characterizes directed-network disruption in patients with disorders of consciousness. These results suggest that anatomically grounded, higher-order surrogate brains offer a principled route to interventional-style EC inference, while highlighting the need for validation in larger multicenter and longitudinal cohorts.