Advanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's Disease

摘要 阿尔茨海默病(AD)影响着500多万美国人，给社区带来了沉重的负担 和医疗保健系统。机器学习(ML)方法在检测疾病和 描述了它的进程。由于缺乏活体“地面真相”的诊断，ML接近 通常依赖于临床衍生的标签和病例对照设计来寻找单一的 病例对照设计中最佳区分两组的成像模式。 然而，临床标签中的异质性可能会降低性能和可解释性。的目标是 该项目旨在解决这一限制，并准确地描述临床前和临床前和 症状性AD。鉴于年龄是罹患痴呆症的主要风险因素，我们将对 使用多模式神经成像数据和目标1中的ML进行健康老龄化。为此，我们建议开发 一种新型的可集成多视图信息的无监督多视图机器学习工具 成像方式(即结构磁共振成像、淀粉样蛋白和tau敏感正电子 发射断层扫描)以原则性的方式。这将使我们能够定义正常的年龄轨迹- 所有模式的相关变化，为理解AD病理提供了必要的背景。我们 将在Aim 2中使用多模式神经成像数据和ML来表征AD的病理。为此，我们 提出了一种新的半监督ML框架，该框架集成了多通道信息和 派生数据驱动的疾病维度。这是通过在个体上进行识别和量化来实现的 捕捉神经解剖和神经病理改变的水平成像模式。我们的方法 基于我们之前在使用高级、无监督的多变量模式分析方面所做的大量工作 神经影像分析的一种技术，称为正交化投影非负矩阵分解 数据。重要的是，我们的项目利用了两个大型多模式数据集，Knight AD研究中心 (ADRC)队列和AD神经成像倡议(ADNI)，在整个过程中对参与者进行抽样 这使它们成为使用先进的ML技术研究AD病理异质性的理想选择。 如果成功，我们的方法可以用来研究任何大脑疾病，而且可以很容易地 当丰富的多模式成像数据在未来整合到个性化医疗策略中 采集将成为医院的常规诊断程序。

Abstract Alzheimer's Disease (AD) affects over 5 million Americans posing a significant burden to the community and health care system. Machine learning (ML) methods have been crucial in detecting the disease and characterizing its progression. Due to the lack of an in vivo “ground truth” diagnosis, ML approaches have typically relied on clinically derived labels and a case-control design in their search for a single imaging pattern that optimally distinguishes between the two groups in the case-control design. However, heterogeneity within clinical labels may degrade performance and interpretability. The goal of this project is to address this limitation and accurately characterize heterogeneity in preclinical and symptomatic AD. Given that age is a major risk factor for developing dementia, we will characterize healthy aging using multimodal neuroimaging data and ML in Aim 1. To this end, we propose to develop a novel unsupervised multi-view machine learning tool that can integrate information from multiple imaging modalities (i.e., structural Magnetic Resonance Imaging, and amyloid and tau sensitive Positron Emission Tomography) in a principled way. This will enable us to define the normal trajectory of age- related changes across all modalities, providing the necessary context to understand AD pathology. We will characterize AD pathology using multimodal neuroimaging data and ML in Aim 2. To this end, we propose to develop a novel semi-supervised ML framework that integrates multimodal information and derives data-driven disease dimensions. This is achieved by identifying and quantifying at the individual level imaging patterns that capture neuroanatomical and neuropathological alterations. Our approach builds on our extensive prior work on using an advanced, unsupervised multivariate pattern analysis technique, termed orthonormal projective non-negative matrix factorization, for analyzing neuroimaging data. Importantly, our project leverages two large multimodal datasets, the Knight AD Research Center (ADRC) cohort and AD Neuroimaging Initiative (ADNI), which sample participants across the continuum of AD making them ideal for investigating heterogeneity of AD pathology using advanced ML techniques. If successful, our approaches could be used for studying any brain disorder and could be readily integrated into personalized medicine strategies in the future when rich, multimodal imaging data collection will become a routine diagnostic procedure in hospitals.