Smartphone Pupillometer for At-Home Screening for Risk of Alzheimer’s Disease

项目总结/摘要 痴呆症是未来几十年最重要的公共卫生问题之一。的最 重要的研究目标将是确定最早，最可靠和最容易获得的生物标志物， 退化，因为早期识别出最有可能衰退的个体现在代表着最有希望的 治疗干预的窗口。这个项目的主要目标是开发一种阿尔茨海默病 (AD)完全基于智能手机应用程序的筛选解决方案， 将红外识别摄像机转换成移动的瞳孔计。我们的科学前提，通过我们最近的临床试验证明， 瞳孔反应提供了一个生物标志物，表明在明显的视觉刺激之前， 业绩下滑是显而易见的。认知任务期间的瞳孔大小（例如，数字广度回忆）增加， 对增加的需求的反应，与认知能力呈负相关（能力较低的人表现出 更大的扩张/补偿努力），瞳孔大小减小，当任务要求时，表现下降 超越能力和补偿能力。有人需要更多的努力，以达到同样的分数， 另一个人可能更接近最大代偿能力，因此衰退的风险更高。 我们发现，患有轻度认知障碍（MCI）的人，患AD的风险更大， 扩张（努力）的数字跨度任务，更大的扩张与更大的多基因风险的AD 和蓝斑（LC）功能障碍的神经影像学指标。这很重要，因为瞳孔反应 反映了LC的功能，尸检研究表明LC是AD发病的早期部位， 退行性改变伴随疾病进展。因此，瞳孔反应可以作为一种特定的生物标志物， 大脑系统的功能改变受到AD的早期表现的影响。目前，pupil 反应可以在短短5分钟内测量使用微创，但昂贵和复杂的 办公设备。为了提高瞳孔测量筛查AD的可扩展性，我们建议开发一种 智能手机评估，老年人可以在家里管理自己，跟踪瞳孔的微小变化 在认知任务中的扩张我们将进一步开发和评估不同的机器学习算法， 使用电话测量的瞳孔反应生物标志物进行严重程度的自动风险评估 在AD早期出现轻度认知障碍。因为这个项目是在 一个更大的NIA资助的RF 1隶属于加州大学圣地亚哥分校阿尔茨海默病研究中心（ADRC），它 将有可能验证移动的瞳孔测量评估对黄金标准的实验室瞳孔测量在老年人 MCI、早期AD和健康对照的成年人。我们的转化目标是提供低成本的家庭 筛查可能处于痴呆症早期阶段的人。此外，建立一个低成本和 可以通过智能手机立即部署的精确瞳孔计将开辟新的机会 用于需要在日常生活环境中频繁测量瞳孔反应的大规模认知研究。

Project Summary/Abstract Dementia represents one of the most important public health concerns in the coming decades. Among the most important research goals will be to identify the earliest, most reliable and easily obtainable biomarkers of degeneration, because early identification of individuals most likely to decline now represents the most promising window for therapeutic interventions. The main objective of this project is to develop an Alzheimer’s disease (AD) screening solution based completely on a smartphone application that converts the phone’s facial recognition IR camera into a mobile pupillometer. Our scientific premise, demonstrated by our recent clinical findings, is that pupillary responses provide a biomarker of cognitive effort required to perform tasks before overt performance declines are manifest. Pupil size during cognitive tasks (e.g., digit span recall) increases in response to increased demands, is inversely related to cognitive ability (individuals with lower ability show greater dilation/compensatory effort), and pupil size decreases and performance declines when task demands exceed abilities and compensatory capacity. Someone requiring more effort to achieve the same score as another person is likely to be closer to maximum compensatory capacity and, therefore, at higher risk for decline. We have found that individuals with mild cognitive impairment (MCI), who are at greater risk for AD, show greater dilation (effort) on the digit span task, and that greater dilation is associated with greater polygenic risk for AD and neuroimaging indicators of locus coeruleus (LC) dysfunction. This is important because pupillary responses reflect LC functioning, and postmortem studies implicate the LC as an early site of AD pathogenesis and degenerative changes with disease progression. Thus, pupillary responses may serve as a specific biomarker of functional alterations in a brain system affected by the earliest manifestations of AD. Currently, pupillary responses can be measured in as little as 5 minutes using minimally invasive, but expensive and complicated office-based devices. To increase the scalability of pupillometry screening in AD, we propose to develop a smartphone assessment that older adults can administer themselves at home that tracks small changes in pupil dilation during cognitive tasks. We will further develop and evaluate different machine learning algorithms that use the pupillary response biomarker measured by the phone to perform automated risk assessment of severity of mild cognitive impairment at the early stages of AD. Because the project would be carried out in the context of a larger NIA-funded RF1 affiliated with the UC San Diego Alzheimer’s Disease Research Center (ADRC), it will be possible to validate mobile pupillometry assessments against gold-standard in-lab pupillometry in older adults with MCI, early AD, and healthy controls. Our translational goal is to provide access to low-cost at-home screening for people who are potentially at early stages of dementia. In addition, the creation of a low-cost and accurate pupillometer that can be immediately deployed through smartphones will open-up new opportunities for large scale cognition studies that require measurement of pupillary response frequently in daily life settings.