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.

翻译：脑组织正日益通过多种成像模态进行表征，其中最为显著的是结构连接性（SC）与功能连接性（FC）。整合这些内在不同却又互补的数据源对于揭示驱动行为表型的跨模态模式至关重要。然而，连接组数据的高维性与非线性、复杂的非线性SC-FC耦合，以及从模态特异性变异中分离共享信息的挑战，都阻碍了有效的整合。为解决这些问题，我们提出了跨模态联合-独立变分网络（CM-JIVNet），这是一个统一的概率框架，旨在从配对的SC-FC数据集中学习因子化的潜在表示。我们的模型利用多头注意力融合模块来捕获非线性跨模态依赖关系，同时分离独立的、模态特定的信号。基于人类连接组计划青年成人（HCP-YA）数据的验证表明，CM-JIVNet在跨模态重建和行为特征预测方面均表现出优越性能。通过有效解耦联合与独立的特征空间，CM-JIVNet为大规模多模态脑分析提供了一个鲁棒、可解释且可扩展的解决方案。