Maximizing Power in AD Clinical Trials via Multimodal Machine Learning

描述（由申请人提供）：正在进行的阿尔茨海默病（AD）研究的重点是确定那些在疾病进展的各个阶段最能预测未来认知能力下降的生物标志物。然后，这些生物标志物可以作为诊断的早期标志物，并用于选择进入临床试验的受试者。最近的结果表明，通过调整机器学习方法来解决这个问题，可以识别这种区分性生物标志物：但到目前为止，研究主要是孤立地使用模式。这些方法提供的灵敏度/特异性对于更临床相关的问题是不令人满意的：哪些MCI患者会转化为AD？解决这些问题需要利用所有数据源的新方法（例如，成像方式、CSF测量）结合。该项目的重点是如何将来自多种生物标志物的数据最佳地聚合起来，以最好地预测未来的认知能力下降，以及这些模型如何改善AD的临床试验。假设：通过同时使用多种模式（连同纵向数据），可以在个体受试者水平上显著提高区分AD、MCI和健康对照的灵敏度和特异性。此外，这些方法将显着改善临床试验中的样本量估计，并帮助获得定制的结果，以评估新的治疗程序。具体目标：（1）开发新的基于图像的机器学习算法，可以在统一的框架内同时利用多种模态。(2)提供软件并在ADNI和BLSA数据集上广泛评估这些方法，以评估真正多模态分析方法可达到的灵敏度/特异性。(3)将多模式分类方法与AD临床试验相结合：（a）通过制定观察特定结局所需的综合样本量估计值，并使用这些方法为威斯康星州ADRC正在进行的R 01资助的观察性/前瞻性研究获得定制结局。方法：我们将开发新的多模态机器学习方法，以最佳方式同时利用所有数据源。我们的模型还将纳入纵向数据，并利用疾病不同阶段模式之间的相互作用。这将用于推导多模式疾病标志物（MMDM）（目的1）。这些算法将在大规模特征良好的数据集上进行评估，并作为软件工具提供（目标2）。我们将使用这些模型以两种方式改善AD临床试验：通过样本富集和定制结果，提供最大的统计功效来检测治疗效果（目标3）。重要性：该项目利用了威斯康星州ADRC在机器学习、统计临床试验设计、成像和AD和AD前疾病临床诊断方面的专业知识。该项目将是第一个 实现一种多模态机器学习指标，专门用于加速临床试验，以便评估潜在的治疗方法，并尽快获得有效的治疗方法。 公共卫生相关性：该项目将开发新的方法和软件系统，用于高度准确和自动识别与阿尔茨海默病相关的模式。所述技术将利用来自多个成像模态的个体的脑图像的聚合信息，所述多个成像模态诸如结构图像（例如，磁共振）和功能图像（例如，正电子发射断层扫描），以及认知和人口统计学信息。该软件，作为该项目成功完成的结果，将在个人水平上为AD诊断（和预测认知能力下降）提供宝贵的帮助。 这使得临床实验能够更有效地评估哪些药物和治疗对这些神经系统疾病有效。

DESCRIPTION (provided by applicant): An emphasis in ongoing Alzheimer's disease (AD) research is identifying those biomarkers which best predict future cognitive decline at the various stages of disease progression. These biomarkers can then serve as early markers for diagnosis, and for selection of subjects into clinical trials. Recent results suggest that the identification of such discriminative biomarkers is possible by adapting machine learning methods for this problem: but studies have primarily used modalities in isolation so far. The sensitivity/specificity offered by these methods is unsatisfactory for more clinically relevant questions: which MCI patients will convert to AD? Answering such questions requires new methods that leverage all data sources (e.g., imaging modalities, CSF measures) in conjunction. This project focuses on how data from multiple biomarkers should be optimally aggregated to best predict future cognitive decline, and how these models can improve clinical trials for AD. Hypothesis: Significant improvements in sensitivity and specificity for discriminating AD, MCI, and healthy controls at the level of individual subjects are possible by making use of multiple modalities (together with longitudinal data) simultaneously. Further, these methods will significantly improve sample size estimates in clinical trials, and help derive customized outcomes for evaluating new treatment procedures. Specific Aims: (1) To develop new image-based machine learning algorithms that can take advantage of multiple modalities simultaneously within a unified framework. (2) To provide a software and extensively evaluate these methods on the ADNI and BLSA datasets, to assess the sensitivity/specificity attainable by truly multi-modal analysis methods. (3) To interface multi-modal classification methods with AD clinical trials: (a) by developing comprehensive sample size estimates needed to observe specific outcomes, and using these methods to derive customized outcomes for an ongoing R01-funded observational/prospective study here at the Wisconsin ADRC. Methods: We will develop new multi-modal machine learning methods that will optimally exploit all data sources simultaneously. Our models will also incorporate longitudinal data, and exploit interaction between modalities at different stages of the disease. This will be used to derive a Multi-Modal Disease Marker (MMDM) (Aim 1). The algorithms will be evaluated on large-scale well-characterized datasets and provided as software tools (Aim 2). We will use these models to improve AD clinical trials in two ways: by sample enrichment and customized outcomes that provide maximum statistical power to detect treatment effects (Aim 3). Significance: This project capitalizes on the Wisconsin ADRC's expertise in machine learning, statistical clinical trial design, imaging, and clinical diagnosis of AD and pre-AD conditions. This project will be the first to implement a multi-modal machine learning metric specifically designed to speed up clinical trials so that potential therapies can be evaluated and an effective treatment arrived at as quickly as possible. PUBLIC HEALTH RELEVANCE: This project will develop novel methods and software systems for highly accurate and automated identification of patterns related to Alzheimer's disease. The techniques will take advantage of the aggregate information from an individual's brain images from multiple imaging modalities such as structural images (e.g., Magnetic Resonance) and functional images (e.g., Positron Emission Tomography), as well as cognitive and demographic information. The software, as a result of successful completion of this project, will provide invaluable assistance in AD diagnosis (and predicting cognitive decline) at the level of individual subjects, as well as allow clinical experiments to be more effective in evaluating which drugs and treatments are effective for such neurological disorders.