Development and validation of efficient cognitive composite scores of digital tools for the detection of early pathophysiological changes in Alzheimers disease

项目总结 临床前阿尔茨海默病(AD)的认知能力下降与肌萎缩侧索硬化密切相关 用正电子发射断层扫描(PET)测量。其他病理生理因素(例如，淀粉样β蛋白和 神经血管功能障碍)与认知功能减退有关的生物学和临床因素 临床前AD的异质性。识别这种异质性对于提高临床前疾病的检测至关重要。 认知正常(CN)个体的AD。捕捉临床前AD的病理生理事件 挑战传统的纸笔认知评估。一种认知数字钟的特点 绘图测试(DCDT)，捕获绘图结果和过程的时间戳坐标 在老年CN受试者中，Tau PET与Tau PET显示出更高的相关性。但是，中使用的dCDT功能 目前的研究是基于AD和CN患者之间的临床诊断而选择的。 该项目的总体目标是1)确定与潜在AD最相关的新dCDT特征 使用最先进的多模式神经成像技术测量的相关病理生理学，占 临床前AD的生物学和临床异质性，以及2)建立dCDT在临床前AD的纵向表现 捕捉临床前阿尔茨海默病患者的认知衰退。新dCDT特征的鉴定将提供一种非 临床前进行性病理生理事件背景下认知状态的侵入性评估 广告。这将使AD治疗试验的参与者筛选变得高效和经济，促进 将数字测试转化为AD初级保健设置，并通过这样做，改善其获得服务不足的机会 人口。在K99阶段，我们将确定与tau PET最相关的新dCDT特征 使用机器学习和深度学习的措施。为了实现这些目标，申请者将利用 在PET成像和机器学习方面的现有技能，并获得1)多模式神经成像的额外培训， 2)深度学习；3)病理生理；4)与正常衰老和AD有关的神经心理改变。 通过这些培训活动，申请者将做好准备，进行R00阶段的dCDT评估 在临床前AD中使用多模式的临床和生物异质性的表现 神经成像技术。这一奖励将促进申请人向独立研究人员的过渡 将利用多模式神经成像来更好地了解大脑疾病的生物学机制。

PROJECT SUMMARY Cognitive decline in preclinical Alzheimer’s disease (AD) has been closely correlated with tauopathy as measured by Positron Emission Tomography (PET). Other pathophysiological factors (e.g., amyloid-beta and neurovascular dysfunction) associated with cognitive decline contribute to the biological and clinical heterogeneity in preclinical AD. Identifying this heterogeneity is crucial for improving the detection of preclinical AD in cognitively normal (CN) individuals. Capturing pathophysiological events in preclinical AD has been challenging with traditional pencil-and-paper cognitive assessments. Features of a cognitive digital clock drawing test (dCDT) that capture time-stamped coordinates of drawing outcomes and process have demonstrated superior correlations with tau PET in elderly CN subjects. However, the dCDT features used in current studies were selected based on the clinical diagnosis between AD and CN subjects. The overall goal of this project is to 1) identify novel dCDT features most correlated with underlying AD- related pathophysiology measured using state-of-the-art multimodal neuroimaging techniques, accounting for biological and clinical heterogeneity in preclinical AD, and 2) establish the longitudinal performance of dCDT in capturing cognitive decline in preclinical AD. The identification of the novel dCDT features will provide a non- invasive assessment of cognitive status in the context of progressive pathophysiological events in preclinical AD. This will enable efficient and cost-effective participant screening for AD therapeutic trials, facilitate the translation of digital testing into AD primary care settings, and in doing so, improve its access for underserved populations. During the K99 phase, we will identify novel dCDT features most correlated with tau PET measures using machine learning and deep learning. To accomplish these goals, the applicant will leverage existing skills in PET imaging and machine learning and gain additional training in 1) multimodal neuroimaging, 2) deep learning, 3) pathophysiological, and 4) neuropsychological changes related to normal aging and AD. With these training activities, the applicant will be well poised to conduct the R00 phase to evaluate the dCDT performance in the context of clinical and biological heterogeneity in preclinical AD using multimodal neuroimaging techniques. This award will facilitate the applicant’s transition to an independent researcher who will utilize multimodal neuroimaging to better understand the biological mechanisms underlying brain disorders.