Statistical methods for longitudinal integrated mechanistic modeling of multiview data

多视图数据纵向综合机制建模的统计方法

• 批准号: 10445698
• 负责人: Yi Zhao
• 金额: $ 54.45万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445698
• 关键词: Address; Adolescent; Aging; Alcohols; Algorithms; Alzheimer' s Disease; Alzheimer' s disease pathology; Area; Behavior; Behavioral; Big Data; Biological Markers; Brain; Characteristics; Clinical Investigator; Clinical Sciences; Complex; Complex Variables; Computational algorithm; Computer software; Data; Data Analyses; Data Element; Development; Disease; Disease Progression; Elderly; Equation; Fostering; Foundations; Functional Imaging; Genetic; Goals; Growth; Heterogeneity; Human; Image; Individual; Intermediate Variables; Intervention; Investigation; Joints; Knowledge; Literature; Mathematics; Mental disorders; Methodology; Methods; Modality; Modeling; Nerve Degeneration; Neurocognitive; Neurodegenerative Disorders; Neurodevelopmental Disorder; Neuropsychology; Neurosciences; Outcome; Pathology; Pathway interactions; Pattern; Personal Satisfaction; Phenotype; Population; Population Heterogeneity; Reproducibility; Research; Risk Factors; Role; Series; Sex Differences; Site; Source; Statistical Methods; Statistical Models; Structure; Study models; Substance abuse problem; Techniques; Time; Time Factors; Variant; base; behavior measurement; complex data; connectome; data integration; data modeling; data structure; disease heterogeneity; heterogenous data; high dimensionality; improved; individualized prevention; innovation; insight; large datasets; longitudinal analysis; multidimensional data; network models; neurodevelopment; neuroimaging; novel; novel marker; open source; personalized intervention; precision medicine; preventive intervention; sex; structured data; trait; vector

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.

摘要 在纵向神经影像学研究中，对受试者内随时间变化的建模提供了关于时间的见解， 与神经发育、神经变性或 疾病进展。揭示和量化包括环境在内的各种模式之间的多方面关系， 心理学、组学、成像和神经认知数据，将有助于更好地理解复杂的 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、人口统计学和表型特征，表征疾病异质性（例如性别）至关重要， 相关的差异，用于精准医疗。通过纵向和路径分析的方法， 数据可以应用于单个数据元素，有限的方法可直接用于结构化数据 约束和大型数据集的综合分析。这项提案的长期目标是开发新的 统计方法来分析纵向高维数据的数学约束和新的 广义路径分析方法，以整合从多个来源收集的复杂数据，与appli， 神经发育/神经变性和相关精神障碍的研究。总体目标 是阐明对大脑结构和功能的纵向影响，描述群体异质性， 了解不同模式和机制的作用，并提供个性化的早期指导 预防/干预战略。纵向一体化多视图机构建模面临的挑战 数据包括（i）具有复杂结构的变量的纵向建模（例如，正定矩阵），（ii） 高维性和异质性，（iii）多个途径的描绘，以及（iv）大规模 和计算效率高的算法。为了解决这些问题，提出了三个具体目标：（1）开发新的 多个纵向、高维协方差矩阵结果与预测因子的回归框架 （2）开发具有纵向、高维、复杂变量的大数据路径分析; (3)开发统计方法，以描述复杂变量的个人增长轨迹。要求1 介绍了纵向模型与协方差矩阵的结果，调查数据结构的变化， 网络级的真实性和/或特性。目标2创新路径正则化和集成优化 标准的高维结构化数据，以确定标记和搜索因果关系的途径下， 最终设置。目标3制定指导个性化预防/干预战略的方法。促进 科学发现的传播、可重复性、可再现性和可复制性，开源软件将 开发拟议的研究是创新的，因为它提出了方法来执行纵向和 从多个领域收集的具有复杂和特定结构的高维数据的路径分析。 这项拟议中的研究意义重大，因为它将丰富对人类大脑的理解，并指导 从业者促进青少年和老年人的福祉。