Statistical methods for longitudinal integrated mechanistic modeling of multiview data

摘要 在纵向神经成像研究中，对受试者内随时间变化的建模提供了对时间的洞察- 与神经发育、神经退行性变有关的大脑变化的依赖效应和因果关系 疾病的发展。揭示和量化跨医疗模式的多路关系，包括环境- 精神、组学、成像和神经认知数据，将有助于更好地理解复杂的背后的机制 疾病，如药物滥用对神经发育和阿尔茨海默病的影响。考虑 遗传、人口学和表型特征，表征疾病的异质性是至关重要的，例如性别- 相关差异，用于精准医学。通过对单变量进行纵向和通径分析的方法。 ATE数据可以应用于单个数据元素，有限的方法直接适用于具有结构化的数据 大数据集的约束和集成分析。这项提议的长期目标是开发新的 具有数学约束和新颖的纵向高维数据分析的统计方法 通用通径分析方法，集成从多个来源收集的复杂数据，并应用于 研究神经发育/神经变性和相关精神障碍的阳离子。总体目标 是阐明对大脑结构和功能的纵向影响，表征种群的异质性， 了解不同模式和机制的作用，并为个性化早期提供指导 预防/干预策略。多视点纵向综合机械建模面临的挑战 数据包括(一)具有复杂结构的变量的纵向建模(例如，正的definite矩阵)，(二) 高维度和异质性，(3)多路径的划分，(4)大范围的发展 并且在计算上使用fi有效的算法。为了解决这些问题，提出了三个具体的fic目标：(1)开发新的 具有预测因子的多个纵向高维协方差矩阵结果的回归框架 跨模式；(2)开发纵向、高维、复杂变量的大数据路径分析； (3)发展统计方法，以刻画复杂变量的个体成长轨迹。目标1 引入以协方差矩阵为结果的纵向模型，以调查数据结构的变化 真实和/或网络级别的特征。目标2创新路径正则化和集成优化 高维结构化数据识别标记和在纵向下搜索因果路径的标准- NAL设置。目标3制定指导个性化预防/干预战略的方法。扶植 科学ficfi编码的传播性、可重复性、再现性和可复制性，开源软件将 发展起来的。拟议的研究具有创新性，因为它提出了进行纵向和 从多个领域收集的具有复杂和特殊fic结构的高维数据的路径分析。 这项拟议的研究具有重要的意义，因为它将丰富对人脑的理解和指导 促进青少年和老年人口福祉的从业者。

Abstract In longitudinal neuroimaging studies, modeling within-subject variation across time offers insights about time- dependent effects and causal relationships in brain changes related to neurodevelopment, neurodegeneration, or disease progression. Uncovering and quantifying the multi-way relationship across modalities, including environ- mental, *omics, imaging, and neurocognitive data, will help better understand the mechanisms behind complex diseases, such as the impact of substance abuse on neurodevelopment and Alzheimer's Disease. Considering genetic, demographic, and phenotypic traits, it is crucial to characterize disease heterogeneity, such as sex- related differences, for precision medicine. Though methods to perform longitudinal and path analysis of univari- ate data can be applied to individual data elements, limited methods are available directly for data with structured constraints and integrated analysis of large datasets. The long-term goal of this proposal is to develop novel statistical methodologies to analyze longitudinal high-dimensional data with mathematical constraints and novel generalized path analysis methodologies to integrate complex data collected from multiple sources, with appli- cation to the study of neurodevelopment/neurodegeneration and related mental disorders. The overall objective is to elucidate longitudinal effects on brain structure and function, to characterize population heterogeneity, to understand the role of different modalities and mechanisms, and to provide guidance on personalized early prevention/intervention strategies. The challenges of longitudinal integrated mechanistic modeling of multiview data include (i) longitudinal modeling of variables with complex structure (e.g. positive definite matrices), (ii) high dimensionality and heterogeneity, (iii) delineation of multiple pathways, and (iv) development of large-scale and computationally efficient algorithms. To address these, three specific aims are proposed: (1) develop novel regression frameworks for multiple longitudinal, high-dimensional covariance matrix outcomes with predictors across modalities; (2) develop big-data path analysis with longitudinal, high-dimensional, complex variables; (3) develop statistical methodologies to characterize individual growth trajectories of complex variables. Aim 1 introduces longitudinal models with covariance matrices as the outcome to investigate changes in data struc- ture and/or characteristics at a network level. Aim 2 innovates path regularization and integrated optimization criteria for high-dimensional structured data to identify markers and search for causal pathways under longitudi- nal settings. Aim 3 develops methodologies to guide personalized prevention/intervention strategies. To foster dissemination, repeatability, reproducibility, and replicability of scientific findings, open-source software will be developed. The proposed research is innovative because it proposes methodologies to perform longitudinal and path analysis for high-dimensional data with complex and specific structures collected from multiple domains. The proposed research is significant because it will enrich the understanding of the human brain and guide practitioners to promote well-being in adolescent and elderly populations.