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: 10685565
• 负责人: Yi Zhao
• 金额: $ 51.52万
• 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/10685565
• 关键词: Address; Adolescent; Aging; Alcohols; Algorithms; Alzheimer' s Disease; Alzheimer' s disease pathology; Area; Articulation; Behavior; Behavioral; Big Data; Brain; Characteristics; Clinical Investigator; Clinical Sciences; Collaborations; Complex; Complex Variables; Computational algorithm; Computer software; Data; Data Analyses; Data Element; Development; Disease; Disease Progression; Elderly; Equation; Fostering; 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; Prevention; Reproducibility; Research; Risk Factors; Role; Series; Sex Differences; Site; Source; Statistical Methods; Statistical Models; Structure; Substance abuse problem; Techniques; Time; Time Factors; Variant; behavior measurement; biomarker identification; 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; sex; statistical learning; 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.

抽象的 在纵向神经影像研究中，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、神经变性或相关的大脑变化的依赖性影响和因果关系 揭示和量化跨模式（包括环境）的多向关系。 心理、*组学、成像和神经认知数据，将有助于更好地理解复杂背后的机制 疾病，例如药物滥用对神经发育和阿尔茨海默病的影响。 遗传、人口统计和表型特征，表征疾病异质性至关重要，例如性别 通过对单变量进行纵向和路径分析的方法来实现相关差异。 数据可以应用于单个数据元素，直接用于结构化数据的方法有限。 该提案的长期目标是开发新颖的大型数据集的约束和综合分析。 具有数学约束和新颖性的统计方法来分析纵向高维数据 广义路径分析方法可集成从多个来源收集的复杂数据，并应用 致力于神经发育/神经变性和相关精神障碍的研究。 是为了阐明对大脑结构和功能的纵向影响，表征群体异质性， 了解不同模式和机制的作用，并为个性化早期提供指导 多视图纵向集成机械建模的挑战。 数据包括（i）具有复杂结构的变量的纵向建模（例如正定义矩阵），（ii） 高维性和异质性，（iii）多种途径的描绘，以及（iv）大规模的开发 为了解决这些问题，提出了三个具体目标：（1）开发新颖的算法。 具有预测变量的多个纵向、高维协方差矩阵结果的回归框架 跨模态；（2）开发纵向、高维、复杂变量的大数据路径分析； (3) 开发统计方法来表征复杂变量的个体增长轨迹。 引入带有协方差矩阵的纵向模型作为研究数据结构变化的结果 目标 2 创新路径正则化和集成优化。 高维结构化数据的标准，用于识别标记并搜索纵向下的因果路径 目标 3 制定指导个性化预防/干预策略的方法。 科学发现的传播性、可重复性、再现性和可复制性，开源软件将 所提出的研究具有创新性，因为它提出了纵向和横向执行的方法。 对从多个领域收集的具有复杂且特定结构的高维数据进行路径分析。 这项研究意义重大，因为它将丰富对人脑的理解并指导 照顾者促进青少年和老年人的福祉。