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

基本信息

批准号：
10474552
负责人：
Hadi Hosseini
金额：
$ 19.68万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10474552
关键词：
Activities of Daily Living Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease pathology Assessment tool Atrophic Back Behavioral 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 Magnetic Resonance Imaging Malignant neoplasm of prostate Measurement Measures Medial Memory 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 machine learning prediction malignant breast neoplasm mortality neuroimaging novel open source prevent prodromal Alzheimer&apos s disease response skills standard of care technology/technique tool virtual reality virtual reality environment virtual world way finding white matter

项目摘要

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.

项目概要阿尔茨海默病 (AD) 是最常见的痴呆症，对患者及其家庭产生重大影响和公共卫生系统在痴呆症临床表现时，大脑出现显着的不可逆性。损害已经存在，需要开发具有成本效益的生物学信息评估早期检测疾病的工具是延迟或预防潜在疗法的迫切先决条件虽然在研究 AD 早期特征方面已经取得了重大进展，目前用于 AD 前驱期临床诊断的护理标准措施缺乏识别 AD 的能力该研究的早期阶段的总体目标是整合 VR、先进的神经成像。用于完善、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 的早期检测来说具有成本效益且在生态和神经生物学上有效。将基于 VR 的多域认知评估电池的性能与实时相结合跨多个来源（例如运动学）的性能数据，用于识别 VR 任务背后的微妙因素对前驱 AD 的灵敏检测贡献最大的性能最重要的是，使用先进的技术。大脑微观结构的定量 MR 测量，我们将测试 VR 测量与早期测量之间的关联大脑网络微观结构变化的标记，以确定最具生物学意义的 VR 测量方法我们的中心假设是 VR 测量情景记忆、空间导航和视觉空间技能对于检测前驱 AD 最为敏感，并且这些 VR 测量可以预测 AD- 内侧颞叶和后顶叶皮质区域以及扣带回和扣带回的相关脑病理学我们使用内部新颖的多域 VR 获得的初步数据。对遗忘性轻度认知障碍 (aMCI) 及老年人样本进行的评估组健康对照 (HC)（N = 23，aMCI 为 17）支持我们的假设，我们建议改进、优化和测试。并在更大的样本（总共 N = 50，30 aMCI）中验证我们的 VR 测量套件，以完成以下任务具体目标：优化和测试一套 VR 评估，以敏感检测前驱 AD（目标 1）并测试所提出的 VR 措施在检测前驱 AD 病理学方面的生物学有效性（目标 2）。所提出的 VR 电池的成功验证可能会导致开发出一种具有成本效益、生态环保的电池。此外，这些措施还可用作 AD 早期检测的生物学有效评估工具。用于监测实验性 AD 治疗反应并预测的敏感行为标记认知和临床轨迹。