Identification of cognitive decline and dementia: Prediction by everyday driving behaviors and physiological responses

随着人口的继续老龄化和晚年认知障碍的发生率上升，早期发现认知障碍 对于及时实施干预措施和安全倡议，损害日益重要。这 可能对大脑淀粉样蛋白负荷较高的人特别重要，使他们 罹患阿尔茨海默病及相关脑部疾病的风险。在具有挑战性的、 复杂的、高风险的日常活动，如驾驶，以及伴随而来的生理反应可能一起进行 为早期发现提供一个廉价的途径。这可能起到提醒个人或 卫生保健提供者的早期认知障碍以及潜在的安全问题。尖端的车内 日益成为新车标准的技术可能会提供一种手段，以不引人注目的方式 捕捉有关自然驾驶行为的敏感信息，并可能有助于早期发现 认知障碍。这项拟议的研究将采用一种新的方法，以不引人注目的方式监控年长的司机 (A)自然主义，(B)固定路线，和(C)模拟器驾驶情况。将使用机器学习方法 在所有驾驶场景中选择驾驶行为的关键特征和唤醒的生理措施 来自固定和模拟驾驶的眼球跟踪测量预测司机的临床诊断：年轻成年司机， 患有和不患有高淀粉样蛋白负荷的健康老年司机，以及患有轻度认知障碍的司机 明显的淀粉样蛋白负担。参与者将在密歇根州阿尔茨海默病进行纵向跟踪 研究中心(MADRC)，每年进行认知和神经评估，以及重复驾驶和 从基线开始进行两年的生理测试。理解和识别驾驶行为的变化 以及这些预测将如何预测谁将发展为临床可识别的认知障碍将导致这种发展 一种早期发现认知能力下降和ADRD的模型。

As the population continues to age and rates of late-life cognitive impairment rise, early detection of cognitive impairment is increasingly important for the timely implementation of interventions and safety initiatives. This may be particularly important in individuals found to have high brain amyloid burden, putting them at particular risk for Alzheimer’s disease and related disorders (ADRD) of the brain. Performance changes in challenging, complex, high-stakes daily activities, such as driving, and accompanying physiological responses may together provide an inexpensive avenue for early detection. This may serve the dual purpose of alerting individuals or health care providers to early cognitive impairment, as well as to potential safety issues. Sophisticated in-car technology that is increasingly becoming standard in new vehicles may provide the means to unobtrusively capture sensitive information about naturalistic driving behaviors and potentially assist with early detection of cognitive impairment. The proposed study will apply a novel approach to unobtrusively monitor older drivers in (a) naturalistic, (b) fixed course, and (c) simulator driving situations. Machine learning approaches will be used to select key features of driving behaviors and physiological measures of arousal in all driving scenarios and eye tracking measures from fixed and simulator drives to predict drivers’ clinical diagnosis: young adult drivers, healthy older drivers with and without high amyloid burden, and drivers with mild cognitive impairment with evident amyloid burden. The participants will be followed longitudinally in the Michigan Alzheimer’s Disease Research Center (MADRC) with annual cognitive and neurological evaluations, as well as repeat driving and physiological testing at two years from baseline. Understanding and identifying changes in driving behaviors and how these predict who will develop clinically identifiable cognitive impairment will lead to the development of a model for early detection of cognitive decline and ADRD.