Multimodal, multiclass prediction of disease status in Alzheimer’s

项目摘要 随着美国阿尔茨海默病（AD）患者人数预计到2050年将达到1300万，我们 必须优先努力进行早期疾病检测。早期AD检测的瓶颈大大阻碍了 临床治疗和开发成功的治疗方法。随着多模式AD的更大可用性 生物标志物在临床实践中，我们有一个独特的机会，利用统计机器学习， 检测AD。过去的工作已经证明了使用AD的临床诊断的良好分类准确性。 二元分类（即，AD-痴呆vs健康认知，HC;轻度认知损害，MCI vs HC; AD vs MCI）。然而，这些分类器通常依赖于单峰生物标志物输入，并且没有正式的比较。 存在多模式生物标志物整合（“融合”）方法用于预测AD临床诊断或 生物标志物状态由A/T（N）框架定义。缺乏最佳的多模态融合策略和整体 二进制分类之外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竞争战略来填补这些空白， 多模式融合和多类分类（例如，AD vs MCI vs HC），使用国家 阿尔茨海默氏症协调中心和阿尔茨海默氏症神经成像倡议。使用大型， 用于疾病预测的多模态数据集伴随着处理缺失数据的挑战，这是一个障碍， 建立一个可靠的分类器。本提案将通过两个具体目标应对这些挑战：（1）比较 技术的最佳数据填补和多模态融合，以及（2）开发一个多类模型，以准确 使用多模态输入预测AD状态（AD/MCI/HC和A+T+/A+T-/A-T-）。的初步分析 使用随机森林和稀疏组Lasso分类器的二进制分类中的多模态数据融合 目标1.通过对两种多类分类策略的初步分析，论证了开发一种 多模态，多类分类器的目标2。拟议的工作将通过出色的培训和 在加州大学欧文分校（UCI）的研究环境，包括直接访问33个国家情报局资助的1 阿尔茨海默病研究中心（ADRC）。ADRC提供了第三个独立的数据集， 验证集，以提高所提出的实验结果的严谨性。申请人将获得以下支持： ADRC生物标志物核心负责人克雷格斯塔克博士和主任Babak Shahbaba博士的联合指导 UCI数据科学计划，并将接受老龄化和AD研究方面的高级培训， 统计和机器学习技术。研究金培训将进一步加强， Peter Chang博士的机器学习指导，Michele Guindani博士的多模态数据融合指导，以及Dr. S. Ahmad Sajjadi在AD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、多模态和多类分类器， 疾病检测和患者分层，以便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.