Integrative Predictive Modeling of Alzheimer's Disease

项目摘要 阿尔茨海默病（AD）是一种医学紧急情况，迄今为止，已被证明是不可能战胜的。被 能够在早期症状阶段准确预测疾病进展将极大地推动我们的领域。 遵循显著疾病特征（例如淀粉样蛋白沉积）的预测模型，通过设计，将提供狭窄的- 范围的进步，将总是缺乏准确的疾病建模和结果预测。的 本申请提出了一种系统级多模式方法来识别有希望的成像遗传学 这些生物标志物将可靠地预测疾病早期阶段的认知能力下降。我们的长期研究目标是 开发一种具有成本效益的风险评估和预测建模方法，并将其应用于治疗 药物开发本申请的总体目标是开发一个综合预测框架， 基于生物标志物特征的轻度认知障碍（MCI）。我们的核心假设是我们的- 艺术统计和拓扑多模态数据分析将显着提高诊断和预测 MCI的准确性，并有助于缩小AD发病机制的知识差距。为此，我们建议实现 使用现有的临床、认知、成像、基因组和转录组数据实现以下具体目标：1） 使用持续性脑缺血模型表征MCI的基因表达模式和神经影像学内表型 同源性; 2）开发用于MCI诊断预测的贝叶斯多核学习框架及其 进展为AD痴呆;以及3）估计不同数据模式在其 早期预测和诊断的有效性。本申请中提出的方法提供了重要的 通过利用当代最先进的分析方法，如持续的 基于同源性的拓扑表面分析和贝叶斯多核学习框架。我们还将依靠 先验知识的整合，从而增强了数据驱动方法的强度， 专业领域。我们工作的积极影响是重大的，因为它将有助于促进我们的理解 AD中几种生物标志物模式之间的复杂相互作用，导致识别敏感的 用于AD风险评估的多模式生物标志物集，并可能发现新的关键疾病相关途径 这可能会导致新的治疗靶点的发现。

Project Abstract Alzheimer’s disease (AD) is a medical emergency that has, to date, proven impossible to defeat. Being able to accurately predict disease progression in the early symptomatic stages will critically advance our field. Predictive models abiding to a salient disease feature (e.g. amyloid deposition) would, by design, offer narrow- scope advances and will invariably come short of accurate disease modeling and outcome prediction. The present application proposes a systems-level, multimodal approach to identify promising imaging-genetics biomarkers that will reliably predict cognitive decline at early disease stages. Our long-term research goal is to develop a method for cost-efficient risk assessment and predictive modeling and to implement it in therapeutic drug development. The overall objective of this application is to develop an integrative predictive framework for mild cognitive impairment (MCI) based on biomarker signatures. Our central hypothesis is that our state-of-the- art statistical and topological multimodal data analysis will significantly improve the diagnostic and predictive accuracy in MCI and help close this knowledge gap in AD pathogenesis. To this end, we propose to accomplish the following specific aims using existing clinical, cognitive, imaging, genomic and transcriptomic data: 1) Characterize the gene expression patterns and neuroimaging endophenotypes in MCI using persistent homology; 2) Develop a Bayesian multi-kernel learning framework for diagnostic prediction of MCI and its progression to AD dementia; and 3) Estimate the relative contribution of different data modalities in terms of their effectiveness regarding early prediction and diagnosis. The methods proposed in this application offer significant advances over the status-quo by utilizing contemporary state-of -the-art analytic approaches such as persistent homology-based topological surface analysis and Bayesian multi-kernel learning framework. We will also rely on the integration of prior knowledge, whereby augmenting the strength of data-driven methods with interpretable domain expertise. The positive impact of our work is significant because it will help advance our understanding of the complex interactions between several biomarker modalities in AD, lead to the identification of sensitive multimodal biomarker set for AD risk assessment and potentially uncover novel critical disease-related pathways that might result in the discovery of new therapeutic targets.