Statistical Modeling of Alzheimer's Disease Progression Integrating Brain Imaging and -Omics Data

整合脑成像和组学数据的阿尔茨海默病进展统计模型

• 批准号: 10359718
• 负责人: Qi Long
• 金额: $ 65.33万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-27 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10359718
• 关键词: Accounting; Address; Algorithms; Alzheimer' s Disease; Alzheimer' s disease model; Anatomy; Atrophic; Automobile Driving; Bayesian Method; Biological; Biological Markers; Biomedical Research; Brain; Brain imaging; Clinical; Clinical Data; Cognition; Cognitive; Communities; Complex; DNA Sequence Alteration; Data; Data Set; Demographic Factors; Dependence; Development; Disease; Disease Progression; Disease model; Early Diagnosis; Etiology; Future; Genes; Goals; Guidelines; Heterogeneity; Image; Individual; Joints; Legal patent; Literature; Longitudinal Studies; MRI Scans; Methodology; Methods; Modality; Modeling; Molecular; Molecular Profiling; Outcome; Pathway interactions; Pattern; Phase; Phenotype; Population; Property; Research; Resolution; Severity of illness; Signal Transduction; Software Tools; Statistical Methods; Statistical Models; Study models; Surface; Testing; Time; Training; Translational Research; Validation; Visit; Work; base; brain shape; cerebral atrophy; computerized tools; data integration; design; disease prognosis; disorder risk; high dimensionality; high risk; innovation; insight; longitudinal analysis; longitudinal dataset; markov model; multidimensional data; multimodality; neuroimaging; neuroimaging marker; novel; open source; personalized predictions; precision medicine; predictive modeling; prognostic; risk prediction; simulation; therapeutic target; tool; user-friendly

Understanding of the etiology of Alzheimer's Disease (AD) is complicated due to the existence of dysregulations at different biological scales, ranging from genetic mutations to structural and functional brain alterations. Most models for studying AD are primarily focused on unimodal analysis, but there is a lack of systematic approaches that can integrate data across multiple scales to study the longitudinal disease progression. For example, the molecular mechanisms of brain atrophy related to progression to AD is not well understood. Although the promise of integrative analysis across multiple scales is increasingly recognized, there has been limited progress in developing interpretable and systematic approaches due the fact that the neuroimaging and -omics features have unique patterns of dependence and it is not immediately clear how to combine these two modalities for modeling progression to AD. Another limitation is that most of the existing methods have focused on delineating biological causes for differences between disease specific phenotypes that does not account for heterogeneity and does not treat the disorder as a continuum, which is recommended as per current NIA guidelines. To address these critical challenges, we develop a suite of statistical methods for modeling disease progression in AD involving longitudinal neuroimaging (MRI) scans and cognitive scores, combined with baseline -omics features and demographic and clinical data. Our integrative longitudinal analysis addresses critical gaps in literature and generates more robust results that are generalizable to more inclusive populations and yields more power in detecting true signals. We use spatially distributed voxel-wise brain surface features derived from MRI scans that provides high resolution interpretations about the changes in brain shape associated with disease progression. We develop predictive models which treats AD as a continuum while integrating data across disease stages and multiple visits in a systematic manner that is able to account for heterogeneity between and within disease stages and provides interpretable insights into longitudinal neuroimaging and baseline -omics features that drive cognition. Our methods can be used for developing individualized prediction trajectories for disease progression, identify latent states that are prognostic for specific disease stages, and predict cognition at future visits that can be directly used for early detection of high-risk individuals. We will develop and train our models using longitudinal ADNI data involving several thousand individuals and validate our findings on an independent longitudinal B-SHARP dataset. The statistical tools and algorithms developed will be made widely available to the broader research community. To our knowledge, our project is one of the first to develop an integrative and interpretable statistical framework for studying the trajectory of disease progression in AD using longitudinal and heterogeneous biomarker data from multiple scales, which provides valuable computational tools for early detection in AD that is of tremendous clinical importance in delivering patent centric outcomes in precision medicine.

阿尔茨海默病（AD）的病因学的理解是复杂的，由于存在 不同生物尺度的失调，从基因突变到大脑结构和功能 改变。大多数研究AD的模型主要集中在单峰分析上，但缺乏 系统的方法，可以整合多个尺度的数据，以研究纵向疾病 进展例如，与AD进展相关的脑萎缩的分子机制尚不清楚， 明白虽然跨多个尺度的综合分析的前景越来越被认可， 在制定可解释和系统的方法方面进展有限， 神经影像学和组学特征具有独特的依赖性模式，目前还不清楚如何 联合收割机将这两种模式结合起来，用于模拟向AD的进展。另一个限制是，大多数现有的 方法集中于描述疾病特异性表型之间差异的生物学原因 这并不能解释异质性，也不能将疾病视为一个连续体，这是推荐的 根据目前的NIA指南。为了应对这些关键挑战，我们开发了一套统计方法 为了对AD中的疾病进展进行建模，包括纵向神经成像（MRI）扫描和认知评分， 结合基线组学特征、人口统计学和临床数据。我们的综合纵向 分析解决了文献中的关键差距，并产生了更可靠的结果，可推广到更多的领域。 包容性群体，并在检测真实信号方面产生更大的力量。我们使用空间分布的体素方式 从MRI扫描中获得的大脑表面特征提供了对变化的高分辨率解释， 与疾病进展相关的大脑形状。我们开发的预测模型将AD视为 同时以系统的方式整合疾病阶段和多次访视的数据， 解释疾病阶段之间和疾病阶段内的异质性，并为以下方面提供可解释的见解： 纵向神经影像学和基线组学特征驱动认知。我们的方法可用于 开发疾病进展的个性化预测轨迹，识别潜在状态， 预测特定疾病阶段的预后，并预测未来访视时的认知， 检测高危人群。我们将使用纵向ADNI数据开发和训练模型， 数千人，并验证了我们的研究结果在一个独立的纵向B-SHARP数据集。的 将向更广泛的研究界广泛提供所开发的统计工具和算法。到 根据我们的知识，我们的项目是第一个开发综合和可解释的统计框架的项目之一 使用纵向和异质生物标志物数据研究AD疾病进展的轨迹 这为AD的早期检测提供了有价值的计算工具， 在精准医疗中提供以专利为中心的成果方面具有巨大的临床重要性。