Development of a cost-effective and neurobiologically valid VR assessment tool for early detection of AD

项目总结 阿尔茨海默病(AD)是最常见的痴呆症，对患者、家庭有重大影响 和公共卫生系统。在痴呆的临床表现时，显著的不可逆脑 损害已经存在，使得开发具有成本效益的、基于生物信息的评估 早期发现疾病的工具是延迟或预防潜在治疗的紧迫先决条件 症状。虽然在研究AD的早期阶段的特征方面已经取得了重大进展， 目前用于临床诊断前驱AD的标准护理措施缺乏识别AD的能力 早期阶段。这项拟议研究的总体目标是将VR、先进的神经成像 用于提炼、优化、测试和验证基于VR的评估工具的技术和计算技术 这对于AD的早期检测具有成本效益以及生态和神经生物学上的有效性。特别是，我们将 基于VR的多领域认知评估组件与实时相结合的综合性能 跨多个来源(例如运动学)的性能数据，以确定VR任务背后的微妙因素 对前驱AD的敏感检测贡献最大的表现。最重要的是，使用高级 脑微结构的定量MR测量，我们将测试VR测量与早期 脑网络微结构变化的标记物，以确定最生物有效的VR测量 前驱AD。我们的中心假设是VR测量情节记忆、空间导航和 视觉空间技能在检测前驱AD方面最敏感，这些VR测量可以预测AD- 内侧颞叶和顶叶后皮质区以及扣带和丘脑的相关脑病理 海马区白质束。我们使用内部的、新颖的、多领域虚拟现实的初步数据 对遗忘性轻度认知障碍(AMCI)及以上人群样本的评估 健康对照组(HC)(N=23，17例急性心肌梗死患者)支持我们的假设。我们建议改进、优化、测试 并在更大的样本(N=50，30个aMCI)中验证我们的VR测量套件，以实现以下目标 具体目标：优化和测试一套敏感检测前驱AD的VR评估(目标1) 并测试所提出的VR措施在检测前驱AD病理方面的生物学有效性(目标2)。 对所提议的VR电池的成功验证可能会导致开发一种经济、生态和 用于AD早期检测的生物学有效评估工具。此外，这些措施可能被用作 敏感的行为标记物用于监测实验性AD治疗的反应和预测 认知和临床轨迹。

PROJECT SUMMARY Alzheimer’s disease (AD) is the most common form of dementia with significant impact on patients, families and the public health system. At the time of clinical manifestation of dementia, significant irreversible brain damage is already present, rendering the development of cost-effective, biologically informed assessment tools for early detection of the disease an urgent prerequisite for potential therapies to delay or prevent symptoms. While significant advances have been made in characterizing early stages of AD for research, current standard-of-care measures used for clinical diagnosis of prodromal AD lack the ability to identify AD in early stages. The overarching goals of the proposed study are to integrate VR, advanced neuroimaging technologies, and computational techniques to refine, optimize, test and validate a VR-based assessment tool that is cost-effective and ecologically and neurobiologically valid for early detection of AD. Particularly, we will integrate performance on a VR-based multidomain cognitive assessment battery combined with real-time performance data across multiple sources (e.g. kinematic) to identify subtle factors underlying VR task performance that contribute the most to sensitive detection of prodromal AD. Most importantly, using advanced quantitative MR measures of brain microstructure, we will test the association between VR measures and early markers of microstructural changes in brain networks to identify the most biologically valid VR measures of prodromal AD. Our central hypothesis is that VR measures of episodic memory, spatial navigation, and visuospatial skills are most sensitive in detecting prodromal AD, and that these VR measures predict AD- related brain pathology in medial temporal and posterior parietal cortical regions as well as in cingulum and hippocampal white matter fascicles. Our preliminary data using an in-house, novel, multidomain VR assessment battery on a sample of individuals with amnestic mild cognitive impairment (aMCI) and older healthy controls (HC) (N = 23, 17 with aMCI) supported our hypothesis. We propose to refine, optimize, test and validate our suite of VR measures in a larger sample (N = 50 total, 30 aMCI) to accomplish the following Specific Aims: To optimize and test a suite of VR assessments for sensitive detection of prodromal AD (Aim 1) and to test the biological validity of the proposed VR measures in detecting prodromal AD pathology (Aim 2). Successful validation of the proposed VR battery may lead to development of a cost-effective, ecologically and biologically valid assessment tool for early detection of AD. Further, these measures can potentially be used as sensitive behavioral markers for monitoring the response to experimental AD treatments and predicting cognitive and clinical trajectories.