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 在纵向神经影像学研究中，跨时间的受试者内部变化提供了有关时间的见解 与神经发育，神经变性或 疾病进展。发现和量化跨模式的多路关系，包括环境 - 心理， *OMICS，成像和神经认知数据将有助于更好地了解复杂背后的机制 疾病，例如滥用药物对神经发育和阿尔茨海默氏病的影响。考虑 遗传，人口统计学和表型性状，表征疾病异质性（例如性别）至关重要 相关差异，用于精确医学。尽管进行纵向和路径分析的方法 ATE数据可以应用于单个数据元素，有限的方法可直接用于具有结构化的数据 大型数据集的约束和集成分析。该提议的长期目标是开发小说 统计方法分析具有数学约束和新颖的纵向高维数据 广义路径分析方法是将从多个来源收集的复杂数据集成到应用 与神经发育/神经变性和相关精神障碍的研究有关。总体目标 是为了阐明对大脑结构和功能的纵向影响，以表征种群异质性， 了解不同的方式和机制的作用，并提早提供个性化的指导 预防/干预策略。多文章纵向综合机理建模的挑战 数据包括（i）具有复杂结构（例如正定矩阵）的变量的纵向建模，（ii） 高维和异质性，（iii）多个途径的描述，以及（iv）大规模发展 和计算有效算法。为了解决这些问题，提出了三个特定目标：（1）发展小说 带有预测因子的多个纵向高维协方差矩阵结果的回归框架 跨模态； （2）通过纵向，高维，复杂变量进行大数据路径分析； （3）开发统计方法来表征复杂变量的个体生长轨迹。目标1 引入具有协方差矩阵的纵向模型，以研究数据结构的变化 - 网络级别的特性和/或特征。 AIM 2无效路径调节和集成优化 高维结构化数据的标准，以识别标记并在纵向下寻找因果途径 NAL设置。 AIM 3开发方法指导个性化的预防/干预策略。养育 科学发现的传播，可重复性，可重复性和可复制性，开源软件将是 发达。拟议的研究具有创新性，因为它提出了执行纵向和 高维数据的路径分析，该数据具有从多个域收集的复杂和特定结构。 拟议的研究非常重要，因为它将丰富对人脑的理解和指导 从业者促进青少年和老年人口的福祉。