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

项目摘要/摘要 痴呆症是未来几十年最重要的公共卫生问题之一。其中最多的 重要的研究目标将是确定最早、最可靠和容易获得的生物标志物 退化，因为早期识别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氏症 (Ad)完全基于智能手机应用程序的筛选解决方案，该应用程序可以转换手机的面部 识别红外摄像机进入移动斜度计。我们的科学前提，由我们最近的临床试验证明 研究结果是，瞳孔反应提供了一个认知努力的生物标记物，这是在公开之前执行任务所需的 性能下降是显而易见的。在认知任务(如数字广度回忆)中，瞳孔大小增加 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 更大的放大/补偿努力)，当任务需要时，瞳孔尺寸减小，成绩下降 超越能力和补偿能力。需要付出更多努力才能达到相同分数的人 另一个人可能更接近最大补偿能力，因此，下降的风险更高。 我们发现，轻度认知障碍(MCI)的个体，他们患AD的风险更大，表现出更大的 数字广度任务的扩张性(努力)，而且扩张性越大，AD的多基因风险越大 蓝斑(LC)功能障碍的神经影像指标。这一点很重要，因为瞳孔反应 反映LC的功能，尸检研究表明LC是AD发病和死亡的早期部位 随着疾病进展而发生退行性改变。因此，瞳孔反应可作为一种特异的生物标志物。 受阿尔茨海默病最早表现影响的大脑系统的功能变化。目前，瞳孔 使用微创技术可以在短短5分钟内测量反应，但昂贵且复杂 基于办公室的设备。为了提高斜视筛查在AD中的可伸缩性，我们建议开发一种 智能手机评估，老年人可以在家中管理自己，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， 使用手机测量的瞳孔反应生物标记物执行严重程度的自动风险评估 在阿尔茨海默病的早期阶段有轻度认知障碍的风险。因为该项目将在 在加州大学圣地亚哥分校阿尔茨海默病研究中心(ADRC)下属的一个更大的NIA资助的RF1中，它 将有可能对照黄金标准的实验室内瞳孔测量在较老的患者中验证移动斜视测量评估 患有MCI的成年人、早期AD和健康对照组。我们的翻译目标是在家中提供低成本的 筛查可能处于痴呆症早期阶段的人。此外，还创造了一种低成本和 可以通过智能手机立即部署的准确验光仪将打开新的机会 适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.