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）影响超过500万美国人，给社区造成重大负担 和医疗保健系统。机器学习（ML）方法在检测疾病方面至关重要， 描述其进展。由于缺乏体内“地面实况”诊断，ML方法 通常依赖于临床衍生的标签和病例对照设计， 在病例对照设计中最佳区分两组的成像模式。 然而，临床标签内的异质性可能会降低性能和可解释性。的目标 本项目旨在解决这一局限性，并准确表征临床前和 症状性AD鉴于年龄是发展痴呆症的主要风险因素，我们将描述 使用多模态神经成像数据和目标1中的ML的健康老龄化。为此，我们建议制定 一种新型的无监督多视图机器学习工具，可以整合来自多个 成像模态（即，结构磁共振成像与淀粉样蛋白和tau蛋白敏感正电子 发射断层扫描）的原则。我们就能确定正常的年龄轨迹- 所有模式的相关变化，提供必要的背景，以了解AD病理学。我们 将使用目标2中的多模态神经成像数据和ML表征AD病理学。为此我们 建议开发一种新的半监督ML框架，该框架集成了多模态信息， 衍生出数据驱动的疾病维度。这是通过识别和量化个人 水平成像模式，捕捉神经解剖学和神经病理学的变化。我们的方法 建立在我们之前广泛的工作，使用先进的，无监督的多元模式分析 一种用于分析神经影像的技术，称为正交投影非负矩阵分解 数据重要的是，我们的项目利用了两个大型多模态数据集，Knight AD研究中心 （ADRC）队列和AD神经影像学倡议（ADNI），在整个连续体中对参与者进行抽样 这使得它们成为使用先进的ML技术研究AD病理学异质性的理想选择。 如果成功，我们的方法可以用于研究任何大脑疾病，并可以很容易地 未来，当丰富的多模态成像数据被整合到个性化医疗策略中时， 收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.