Multimodal, multiclass prediction of disease status in Alzheimer’s

项目摘要 预计到2050年，患有阿尔茨海默病(AD)的美国人将达到1300万，我们 必须优先考虑及早发现疾病的工作。AD早期检测的瓶颈已经大大阻碍了 临床治疗和成功疗法的发展。多模式广告的可用性更高 生物标记物在临床实践中，我们有一个独特的机会来利用统计机器学习更早 AD的检测。过去的工作表明，使用该方法对AD的临床诊断具有良好的分类准确性 二分类(即，AD-痴呆症与健康认知，HC；轻度认知障碍，MCI与HC；AD与 MCI)。然而，这些分类器通常依赖于单峰生物标记物输入，并且没有正式的比较 多模式生物标志物集成(“融合”)方法用于预测AD的临床诊断或 A/T(N)框架定义的生物标志物状态。缺乏最优的多模式融合策略和整体 超出二进制分类的诊断预测降低了统计机器学习的翻译价值 临床实践中的分类器。该提案通过评估几种相互竞争的策略来填补这些空白 多模式融合和多类分类(例如，AD、MCI和HC)使用来自National的数据 阿尔茨海默病协调中心和阿尔茨海默病神经成像倡议。使用大型、 疾病预测的多模式数据集伴随着处理丢失数据的挑战，这是一个障碍 用于构建可靠的分类器。该提案将以两个具体目标应对这些挑战：(1)比较 最优数据补偿和多模式融合技术，以及(2)开发多类模型以准确地 使用多模式输入预测AD状态(AD/MCI/HC和A+T+/A+T-/A-T-)。初步分析了我国目前存在的 基于随机森林和稀疏组套索分类器的二值分类多模式数据融合 目的1.初步分析了多类分类的两种策略，论证了开发多类分类算法的可行性 目标2的多模式、多类别分类器。拟议的工作将通过出色的培训和 加州大学欧文分校(UCI)的研究环境，包括直接访问NIA资助的33个项目中的1个 阿尔茨海默病研究中心(ADRC)。ADRC提供了第三个独立的数据集作为 验证集，以提高拟议实验结果的严谨性。申请者将由以下人员支持 ADRC Biomarker核心负责人Craig Stark博士和主任Babak ShahBaba博士的共同指导 ，并将接受老龄化和AD研究方面的高级培训和 统计学和机器学习技术。研究金培训将通过额外的 机器学习的Peter Chang博士、多模式数据融合的Michele Guindani博士和Dr. S.Ahmad Sajadi在AD方面的临床专业知识。拟议的培训和研究计划将导致 开发用于AD状态预测的可靠、多模式和多类分类器，以便更早地实现 疾病检测和患者分层，以进行更有效的临床试验。

Project Summary As the number of Americans living with Alzheimer’s disease (AD) is projected to reach 13 million by 2050, we must prioritize efforts for early disease detection. The bottleneck of early AD detection has greatly hindered clinical treatment and development of successful therapeutics. With greater availability of multimodal AD biomarkers in clinical practice, we have a unique opportunity to leverage statistical machine learning for earlier detection of AD. Past work has demonstrated good classification accuracy of clinical diagnosis of AD using binary classifications (i.e., AD-dementia vs healthy cognition, HC; mild cognitive impairment, MCI vs HC; AD vs MCI). These classifiers, however, are often reliant on unimodal biomarker inputs, and no formal comparison of multimodal biomarker integration (“fusion”) methods exist for predicting either AD clinical diagnosis or biomarker status as defined by the A/T(N) framework. Lack of optimal multimodal fusion strategies and holistic diagnosis prediction beyond binary classification reduce the translational value of statistical machine learning classifiers in clinical practice. This proposal fills these gaps by evaluating several competing strategies for multimodal fusion and multiclass classification (e.g., AD vs MCI vs HC) using data from the National Alzheimer’s Coordinating Center and Alzheimer’s Disease Neuroimaging Initiative. The strength of using large, multimodal datasets for disease prediction is accompanied by the challenge of handling missing data, a barrier for building a reliable classifier. This proposal will address these challenges with two specific aims: (1) compare techniques for optimal data imputation and multimodal fusion, and (2) develop a multiclass model to accurately predict AD status (AD/MCI/HC and A+T+/A+T-/A-T-) using multimodal inputs. Preliminary analyses of multimodal data fusion in binary classification using random forest and sparse group lasso classifiers motivate Aim 1. Preliminary analysis of two strategies of multiclass classification demonstrates feasibility of developing a multimodal, multiclass classifier for Aim 2. The proposed work will be enhanced by the excellent training and research environment at the University of California, Irvine (UCI), including direct access to 1 of 33 NIA-funded Alzheimer’s Disease Research Centers (ADRCs). The ADRC offers a third, independent dataset to serve as a validation set to improve the rigor of results from the proposed experiments. The applicant will be supported by the joint mentorship of Dr. Craig Stark, the ADRC Biomarker Core Leader, and Dr. Babak Shahbaba, Director of the UCI Data Science Initiative, and will receive advanced training in both aging and AD research and statistics and machine learning techniques. Fellowship training will be further strengthened by the additional mentorship of Dr. Peter Chang for machine learning, Dr. Michele Guindani for multimodal data fusion, and Dr. S. Ahmad Sajjadi for clinical expertise in AD. The proposed training and research plans will result in the development of a reliable, multimodal, and multiclass classifier for AD status prediction to enable earlier disease detection and stratification of patients for more effective clinical trials.