CALM: 可解释的跨模态对齐用于从非配对数据中发现生物标志物
CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data
AI总结：
提出CALM框架，通过线性投影对齐脑区和遗传通路的潜在分布，从完全不相交的人群中学习可解释的跨模态关联，在自闭症谱系障碍数据上优于现有方法。
摘要：
大脑结构与遗传影响之间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是单模态的，仅提供神经影像或遗传学数据。我们提出了CALM，一个从完全不相交的人群中学习脑区与遗传通路之间可解释关联的框架。CALM通过线性投影将两种模态对齐到共享的潜在空间，同时匹配类别条件潜在分布并确保组间可分离性。这些投影提供了可解释的通路-脑区关联。当在单模态影像和遗传学数据集上训练时，CALM能够泛化到未见过的配对数据集，优于多种最先进方法和消融基线。我们还展示了所学关联相对于配对基线的稳定性。我们在自闭症谱系障碍上的实验揭示了与特定皮层区域相关的免疫和代谢通路，这与已有文献一致。因此，CALM为利用大型单模态库研究跨不同数据集的脑疾病跨模态相互作用打开了大门。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
