发表机构
Monash University Malaysia; University of North Carolina at Chapel Hill; Nanyang Technological University（莫纳什大学马来西亚校区; 北卡罗来纳大学教堂山分校; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出多模态超图流匹配框架MHG-FM，联合生成结构-功能脑连接并跨模态翻译，通过超图编码和双交叉注意力保留高阶关系，在HCP-YA上优于基线且采样快8倍。
AI 中文摘要
结构连接（SC）和功能连接（FC）提供了脑区之间相互作用的互补信息，广泛用于神经精神疾病的神经影像学研究。生成建模可以缓解大规模配对SC-FC数据的稀缺性，但现有方法通常使用仅捕获二元交互的成对图，并且常常独立生成SC和FC，限制了高阶结构-功能关系的保留。我们提出了一种多模态超图流匹配（MHG-FM）框架，用于联合SC-FC连接生成和跨模态翻译。MHG-FM构建模态特定的超图，使用超图神经网络（HGNN）编码器学习高阶表示，并通过双交叉注意力（DCA）进行双向跨模态融合。变分自编码器将融合表示映射到紧凑的潜空间，其中条件流匹配通过潜传输实现连接合成和多模态翻译。在人类连接组计划青年成人（HCP-YA）数据集上的实验表明，MHG-FM在重建质量、拓扑保留、分布相似性和SC-FC耦合方面优于多个最先进的基线，同时实现比匹配的扩散骨干快约8倍的采样速度。
英文摘要
Structural connectivity (SC) and functional connectivity (FC) provide complementary information on interactions between brain regions and are widely used in neuroimaging studies of neuropsychiatric disorders. Generative modelling can alleviate the scarcity of large-scale paired SC-FC data, but existing approaches typically use pairwise graphs that capture only dyadic interactions and often generate SC and FC independently, limiting preservation of higher-order structure-function relationships. We propose a Multimodal Hypergraph Flow Matching (MHG-FM) framework for joint SC-FC connectivity generation and cross-modal translation. MHG-FM constructs modality-specific hypergraphs, learns higher-order representations with Hypergraph Neural Network (HGNN) encoders, and performs bidirectional cross-modal fusion using Dual Cross-Attention (DCA). A variational autoencoder maps the fused representations to a compact latent space, where conditional flow matching enables connectivity synthesis and multimodal translation via latent transport. Experiments on the Human Connectome Project Young Adult (HCP-YA) dataset show that MHG-FM outperforms several state-of-the-art baselines in reconstruction quality, topology preservation, distributional similarity, and SC-FC coupling, while achieving approximately 8x faster sampling than a matched diffusion backbone.
Comments10 pages, 3 figures, 5 tables
