arXiv：cs.LG· Chyong Yi Poh, Hwa Hui Tew, Junn Yong Loo, Rapha\"{e}l C. -W. Phan, Fuad Noman, Pew-Thian Yap, Chee-Ming Ting·· 3 小时前AI 评分31
MHG-FM：基于多模态超图流匹配的脑结构-功能连接生成
Structural-Functional Brain Connectivity Generation via Multimodal Hypergraph-based Flow Matching
AI 导读
研究者提出多模态超图流匹配框架 MHG-FM，用于脑结构连接（SC）与功能连接（FC）的联合生成及跨模态翻译。该框架用超图神经网络编码模态专属超图，经双重交叉注意力融合后由变分自编码器映射到紧凑隐空间，再以条件流匹配实现生成与翻译。在 HCP-YA 数据集上，MHG-FM 在重建质量、拓扑保持、分布相似性和 SC-FC 耦合上优于多个 SOTA 基线，采样速度约为匹配扩散骨干的 8 倍。
正文
Abstract: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.
| Comments: | 10 pages, 3 figures, 5 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02722 [cs.LG] |
| (or arXiv:2610.02722v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02722 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chyong Yi Poh [view email]
[v1]
Fri, 2 Oct 2026 02:58:09 UTC (1,256 KB)
来源：arXiv：cs.LG · arxiv.org