Structural-Functional Brain Connectivity Generation via Multimodal Hypergraph-based Flow Matching

The paper introduces Multimodal Hypergraph Flow Matching (MHG‑FM), a framework that jointly generates structural connectivity (SC) and functional connectivity (FC) by constructing modality‑specific hypergraphs and learning higher‑order representations with Hypergraph Neural Network encoders. It employs Dual Cross‑Attention for bidirectional cross‑modal fusion, a variational autoencoder to map fused representations into a latent space, and conditional flow matching to synthesize connectivity and perform multimodal translation. Experiments on the Human Connectome Project Young Adult dataset demonstrate that MHG‑FM outperforms state‑of‑the‑art baselines in reconstruction quality, topology preservation, distributional similarity, and SC‑FC coupling, while achieving roughly eight times faster sampling than a comparable diffusion backbone.

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• diffusion
• multimodal
• benchmarks