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

由于阿尔茨海默病(AD)的存在，对其病因的理解是复杂的 不同生物尺度的失调，从基因突变到结构和功能大脑 改装。大多数研究AD的模型主要集中在单峰分析上，但缺乏 可以整合多个尺度的数据以研究纵向疾病的系统方法 进步。例如，与进展为阿尔茨海默病相关的脑萎缩的分子机制并不清楚 明白了。尽管跨多个尺度的综合分析的前景越来越被认识到， 在制定可解释的和系统的方法方面取得的进展有限，因为 神经成像和组学特征具有独特的依赖模式，目前尚不清楚如何 将这两种模式结合起来，以模拟进展到AD。另一个限制是，现有的大多数 方法侧重于描述疾病特定表型之间差异的生物学原因 这不能解释异质性，也不能把疾病作为一个连续体来对待，这是推荐的。 根据目前NIA的指导方针。为了应对这些关键挑战，我们开发了一套统计方法 为了对涉及纵向神经成像(MRI)扫描和认知评分的AD的疾病进展进行建模， 结合基线组学特征以及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，并生成了更可靠的结果，这些结果可推广到更多 并在检测真实信号方面产生更大的能力。我们使用空间分布的体素 从MRI扫描获得的大脑表面特征提供了关于这些变化的高分辨率解释 大脑的形状与疾病的进展有关。我们开发了预测模型，将AD视为 连续，同时以系统的方式集成跨疾病阶段和多次访问的数据，从而能够 解释疾病阶段之间和疾病阶段内的异质性，并提供可解释的见解 纵向神经成像和基线组学特征推动认知。我们的方法可以用于 开发疾病进展的个性化预测轨迹，识别符合以下条件的潜在状态 预测特定疾病阶段的预后，并预测未来就诊时的认知能力，可直接用于早期 检测高危个体。我们将使用纵向ADNI数据开发和训练我们的模型，包括 并在一个独立的纵向B-Sharp数据集上验证了我们的发现。这个 开发的统计工具和算法将广泛提供给更广泛的研究界。至 我们的知识，我们的项目是第一批开发一个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记物数据研究AD的疾病进展轨迹 这为AD的早期检测提供了有价值的计算工具 在提供以专利为中心的精密医学成果方面具有巨大的临床重要性。

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.