Ryu Jihye, Vero Joe, Dobkin Roseanne D, Torres Elizabeth B
Department of Psychology, Rutgers University; Rutgers University Center for Cognitive Science, Rutgers University.
Department of Psychology, Rutgers University.
J Vis Exp. 2019 Jul 24(149). doi: 10.3791/59827.
As Parkinson's disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field's most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person's stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.
由于帕金森病（PD）是一种异质性疾病，真正需要个性化医疗来优化护理。以目前的形式来看，传统上用于追踪疾病进展的纸笔症状测量的标准分数过于粗略（离散），无法在面对巨大的症状多样性时捕捉所考虑临床现象的细微差别。因此，传感器、可穿戴设备和移动设备越来越多地被纳入PD研究和日常护理中。这些数字测量虽然更精确，但产生的数据比传统测量数据的标准化程度和可解释性更低，因此，这两种类型的数据在很大程度上仍然是孤立的。这两个问题都为该领域最精确评估工具的广泛临床应用带来了障碍。本方案解决了这两个问题。我们使用传统任务来测量认知和运动控制，对参与者进行测试，同时使用可穿戴设备不显眼地共同记录生物物理信号。然后，我们将传统纸笔方法的分数与我们持续记录的数字数据整合起来。我们提供了一种新的标准化数据类型和统一的统计平台，能够在不同条件下动态追踪个体随机特征的变化，这些条件可以探测神经运动控制从自主到非自主的不同功能水平。该方案和标准化统计框架提供了与经过验证的临床量表相对应的PD身体和认知功能的动态数字生物标志物，同时显著提高了它们的精确性。
