With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.

中文翻译：

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多模态神经影像数据集成和通路分析

随着技术的进步，在一组共同的主题上收集多种类型的测量值正在成为科学中的常规。一些值得注意的例子包括用于同时研究大脑结构和功能的多模式神经影像学研究以及用于结合遗传和基因组信息的多组学研究。多模式数据的综合分析使科学家能够询问新的机械问题。然而，综合假设的数据收集和生成超过了多模态测量联合分析的可用方法。在本文中，我们研究了中介分析背景下的高维多模态数据集成。我们的目标是了解不同数据模式在暴露变量和结果之间的路径中作为可能的中介所扮演的角色。我们提出了一个中介模型框架，其中两种数据类型作为独立的中介集，并开发了一种用于参数估计的惩罚优化方法。我们通过渐近分析研究估计器的理论性质，并通过模拟研究其有限样本性能。我们用多模式脑通路分析来说明我们的方法，该分析具有结构和功能连接性作为性和语言处理之间关联的中介。