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

开发一种经济有效且神经生物学有效的 VR 评估工具，用于 AD 的早期检测

• 批准号: 10289512
• 负责人: Hadi Hosseini
• 金额: $ 23.61万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10289512
• 关键词: Activities of Daily Living; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Assessment tool; Atrophic; Back; Behavioral; Biological; Biological Testing; Brain; Brain Injuries; Brain Pathology; Cause of Death; Cessation of life; Clinical; Clinical assessments; Cognition; Cognitive; Computational Technique; Data; Dementia; Detection; Development; Diagnosis; Disease; Early Diagnosis; Environment; Episodic memory; Family; Fascicle; Goals; Grain; Health system; Heart Diseases; Hippocampus (Brain); Immersion; Individual; Lead; Literature; Machine Learning; Magnetic Resonance Imaging; Malignant neoplasm of prostate; Measurement; Measures; Medial; Memory; Modeling; Molecular; Monitor; Neurites; Neurobiology; Neuropsychology; Outcome; Parietal; Pathology; Patients; Performance; Process; Property; Psychometrics; Public Health; Reality Testing; Research; Research Personnel; Sampling; Source; Stroke; Symptoms; Task Performances; Testing; Time; United States; Validation; Visuospatial; amnestic mild cognitive impairment; base; clinical Diagnosis; clinically significant; cognitive function; cognitive testing; cost effective; gray matter; kinematics; malignant breast neoplasm; mortality; neuroimaging; novel; open source; prevent; prodromal Alzheimer' s disease; response; skills; standard of care; technology/technique; tool; virtual reality; virtual reality environment; virtual world; way finding; white matter

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.

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是最常见的一种痴呆症，对患者、家庭和精神健康都有重要影响 和公共卫生系统。在痴呆临床表现时，显著的不可逆脑损害 损害已经存在，使得开发具有成本效益的生物信息评估 早期发现疾病的工具是延迟或预防潜在治疗的迫切先决条件 症状虽然在表征AD早期阶段的研究方面取得了重大进展， 目前用于前驱AD临床诊断的标准护理措施缺乏在AD患者中识别AD的能力。 早期阶段这项研究的总体目标是将VR、先进的神经成像技术 技术和计算技术来完善，优化，测试和验证基于VR的评估工具 这对于AD的早期检测是成本有效的，并且在生态学和神经生物学上是有效的。特别是，我们将 基于VR多领域认知评估组合与实时 跨多个来源（例如运动学）的性能数据，以识别VR任务背后的微妙因素 这对前驱AD的灵敏检测贡献最大。最重要的是，使用先进的 定量MR测量大脑微观结构，我们将测试VR测量与早期 大脑网络中微观结构变化的标记，以确定最生物有效的VR措施， 前驱AD我们的中心假设是，VR测量情景记忆，空间导航， 视觉空间技能在检测前驱AD中最敏感，这些VR测量可预测AD- 内侧颞叶和后顶叶皮质区以及扣带和 海马白色物质束。我们的初步数据使用内部的，新颖的，多域VR 对遗忘型轻度认知障碍（aMCI）和老年人样本的成套评估 健康对照（HC）（N = 23，17例患有aMCI）支持我们的假设。我们建议改进、优化、测试 并在更大的样本中验证我们的VR测量套件（N = 50，30 aMCI），以实现以下目标 具体目的：优化和测试一套VR评估，用于敏感检测前驱AD（目的1） 并测试所提出的VR测量在检测前驱AD病理学中的生物有效性（目的2）。 成功验证拟议的VR电池可能会导致开发一种具有成本效益的，生态和 生物学有效的评估工具，用于早期检测AD。此外，这些措施可能被用作 用于监测对实验性AD治疗的反应和预测 认知和临床轨迹。