Advanced machine learning algorithms that integrate genomewide, longitudinal MRI and demographic data to predict future cognitive decline toward dementia

摘要 阿尔茨海默病（AD）的“临床前”阶段的特征在于脑内异常水平的 在没有主要症状的情况下，淀粉样蛋白的积累可以持续几十年，并可能保持 成功治疗策略的关键。如今，迫切需要定量生物标志物 和基因测试，可以预测在个人水平的临床进展。该项目将开发 尖端的机器学习算法，将挖掘高维，多模态， 纵向数据，以推导出在以下背景下产生个体水平临床预测的模型： 痴呆开发的预测模型将专门利用无处不在和负担得起的数据 类型：结构脑MRI扫描，唾液或血液来源的全基因组序列数据，以及 人口统计学变量（年龄、教育和性别）。先前的研究表明，所有这些 变量与痴呆症的临床下降密切相关，但迄今为止，我们还没有模型 它可以收集这些高维数据中嵌入的所有预测信息。 机器学习（ML）算法越来越多地用于从高水平数据计算临床预测。 三维生物医学数据，如临床扫描。然而，大多数先前的ML方法都是针对 “预测”任务是关于并发条件的应用（例如，歧视案件， 控制）;和确定的风险因素（例如，年龄），多种形式（例如，基因型和图像）， 纵向数据没有得到充分利用。该应用程序的核心创新将是开发 严格，灵活，实用的ML方法，可以充分利用多模态，纵向和高， 三维生物医学数据来计算预后临床预测。 拟议的项目将建立在PI在计算建模和分析方面的强大背景之上 大规模的生物医学数据。我们将采用创新的Bayesian ML框架，提供 灵活地处理和利用现实生活中的纵向和多模态数据。我们假设 开发的模型将比用于识别临床前的替代基准更有用 这些人面临着即将发生临床衰退的高风险。我们将使用一个严格的统计 发现、交叉验证和基准测试开发工具的方法。该项目将 产生免费分发、记录和验证的软件和模型，用于预测未来的临床 基于全基因组、纵向结构MRI和人口统计学数据的进展。我们认为 我们开发的算法和软件将为临床前AD的分层提供宝贵的工具 药物试验中的受试者，优化未来的治疗，并最大限度地减少不良反应的风险。

ABSTRACT The “preclinical” phase of Alzheimer’s disease (AD) is characterized by abnormal levels of brain amyloid accumulation in the absence of major symptoms, can last decades, and potentially holds the key to successful therapeutic strategies. Today there is an urgent need for quantitative biomarkers and genetic tests that can predict clinical progression at the individual level. This project will develop cutting edge machine learning algorithms that will mine high dimensional, multi-modal, and longitudinal data to derive models that yield individual-level clinical predictions in the context of dementia. The developed prognostic models will specifically utilize ubiquitous and affordable data types: structural brain MRI scans, saliva or blood-derived genome-wide sequence data, and demographic variables (age, education, and sex). Prior research has demonstrated that all these variables are strongly associated with clinical decline to dementia, however to date we have no model that can harvest all the predictive information embedded in these high dimensional data. Machine learning (ML) algorithms are increasingly used to compute clinical predictions from high- dimensional biomedical data such as clinical scans. Yet, most prior ML methods were developed for applications where the ``prediction’’ task was about concurrent condition (e.g., discriminate cases and controls); and established risk factors (e.g., age), multiple modalities (e.g., genotype and images) and longitudinal data were not fully exploited. This application’s core innovation will be to develop rigorous, flexible, and practical ML methods that can fully exploit multi-modal, longitudinal, and high- dimensional biomedical data to compute prognostic clinical predictions. The proposed project will build on the PI’s strong background in computational modeling and analysis of large-scale biomedical data. We will employ an innovative Bayesian ML framework that offers the flexibility to handle and exploit real-life longitudinal and multi-modal data. We hypothesize that the developed models will be more useful than alternative benchmarks for identifying preclinical individuals who are at heightened risk of imminent clinical decline. We will use a statistically rigorous approach for discovery, cross-validation, and benchmarking the developed tools. This project will yield freely distributed, documented, and validated software and models for predicting future clinical progression based on whole-genome, longitudinal structural MRI and demographic data. We believe the algorithms and software we develop will yield invaluable tools for stratifying preclinical AD subjects in drug trials, optimizing future therapies, and minimizing the risk of adverse effects.