从不配对的脑影像与基因数据中，发现可解释的脑区-通路关联
CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data
- 通过线性投影对齐跨模态潜在空间，实现不配对数据的可解释关联学习
- 在自闭症数据上发现免疫与代谢通路与特定皮层区域相关，效果优于主流方法
- 适合研究脑疾病中多模态交互的科研人员，尤其关注可解释性分析
大脑结构与遗传因素的相互作用是理解神经精神疾病的关键。然而，大多数大规模数据集为单模态，仅提供神经影像或基因数据。我们提出CALM框架，从完全分离的人群中学习脑区（ROI）与基因通路之间的可解释关联。该方法通过线性投影将两个模态映射到共享潜在空间，同时匹配类别条件下的潜变量分布并保证组间可分性。这些投影生成了可解释的通路-脑区对应关系。在单模态影像与基因数据上训练后，CALM在未见的配对数据集上表现优于多个先进方法和消融基线，并展现出对配对基线的关联稳定性。自闭症谱系障碍实验揭示免疫与代谢通路与特定皮层区域相关，结果与已有文献一致。因此，CALM为利用大规模单模态数据资源研究不同数据集中脑疾病的跨模态交互提供了新路径。
原文摘要 · Abstract (English)
The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders. However, most large-scale datasets are unimodal, providing either neuroimaging or genetics data. We propose CALM, a framework that learns interpretable associations between brain ROIs and genetic pathways from completely disjoint populations. CALM aligns the two modalities in a shared latent space via linear projections that simultaneously match the class-conditional latent distributions and ensure group separability. These projections provide interpretable pathway--ROI associations. When trained on unimodal imaging and genetics datasets, CALM generalizes to an unseen paired dataset, outperforming several state-of-the-art methods and ablation baselines. We also demonstrate stability of the learned associations against a paired baseline. Our experiments on autism spectrum disorder reveal immune and metabolic pathways linked to specific cortical regions and are consistent with established literature. Thus, CALM opens the door to leveraging large unimodal repositories for studying cross-modal interactions in brain disorders across disparate datasets.
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。