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Abstract

Behavior

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published: July 24th, 2019

DOI:

10.3791/59827

1Department of Psychology, Rutgers University, 2Rutgers University Center for Cognitive Science, Rutgers University, 3Rutgers-Robert Wood Johnson Medical School, Department of Psychiatry, Rutgers University, 4Center for Biomedicine, Imaging and Modelling, Department of Computer Science, Rutgers University

Abstract

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. 

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Keywords Parkinson s Disease

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