用注意力机制分离脑网络共性与个性特征，提升多模态分析精度
Attention-Based Variational Framework for Joint and Individual Components Learning with Applications in Brain Network Analysis
- 基于多头注意力的变分框架，分离结构与功能脑网络的共享和独立特征
- 在HCP-YA数据上实现更优的跨模态重建与行为特质预测性能
- 适合从事脑科学多模态分析、神经影像建模的研究者使用
脑组织的表征正越来越多地依赖于多种成像模态，尤其是结构连接（SC）与功能连接（FC）。整合这些本质不同但互补的数据源对揭示驱动行为表型的跨模态模式至关重要。然而，有效融合受到连接组数据高维度、非线性以及复杂非线性SC-FC耦合的限制，且难以分离共享信息与模态特异性差异。为此，我们提出跨模态联合-个体变分网络（CM-JIVNet），一种统一的概率框架，用于从配对的SC-FC数据集中学习解耦的潜在表示。模型采用多头注意力融合模块捕捉非线性跨模态依赖关系，同时分离出独立的模态特异性信号。在人类连接组计划青年成人数据集（HCP-YA）上的验证表明，CM-JIVNet在跨模态重建和行为特质预测方面表现更优。通过有效分离联合与个体特征空间，该模型为大规模多模态脑分析提供了稳健、可解释且可扩展的解决方案。
原文摘要 · Abstract (English)
Brain organization is increasingly characterized through multiple imaging modalities, most notably structural connectivity (SC) and functional connectivity (FC). Integrating these inherently distinct yet complementary data sources is essential for uncovering the cross-modal patterns that drive behavioral phenotypes. However, effective integration is hindered by the high dimensionality and non-linearity of connectome data, complex non-linear SC-FC coupling, and the challenge of disentangling shared information from modality-specific variations. To address these issues, we propose the Cross-Modal Joint-Individual Variational Network (CM-JIVNet), a unified probabilistic framework designed to learn factorized latent representations from paired SC-FC datasets. Our model utilizes a multi-head attention fusion module to capture non-linear cross-modal dependencies while isolating independent, modality-specific signals. Validated on Human Connectome Project Young Adult (HCP-YA) data, CM-JIVNet demonstrates superior performance in cross-modal reconstruction and behavioral trait prediction. By effectively disentangling joint and individual feature spaces, CM-JIVNet provides a robust, interpretable, and scalable solution for large-scale multimodal brain analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。