Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

Abstract

Background: Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, PD includes motor abnormalities beyond the symptoms and signs as measured by UPDRS, such as the lack of anticipatory adjustments or compromised movement smoothness, which are difficult to assess clinically. Moreover, PD may entail motor abnormalities not yet known. All these abnormalities are quantifiable via motion capture and may serve as biomarkers to diagnose and monitor PD.Objective: In this pilot study, we attempted to identify motion features revealing maximum contrast between healthy subjects and PD patients with deep brain stimulation (DBS) of the nucleus subthalamicus (STN) switched off and on as the first step to develop biomarkers for detecting and monitoring PD patients' motor symptoms. Methods:We performed 3D gait analysis in 7 out of 26 PD patients with DBS switched off and on, and in 25 healthy control subjects. We computed feature values for each stride, related to 22 body segments, four time derivatives, left-right mean vs. difference, and mean vs. variance across stride time. We then ranked the feature values according to their distinguishing power between PD patients and healthy subjects.Results: The foot and lower leg segments proved better in classifying motor anomalies than any other segment. Higher degrees of time derivatives were superior to lower degrees (jerk > acceleration > velocity > displacement). The averaged movements across left and right demonstrated greater distinguishing power than left-right asymmetries. The variability of motion was superior to motion's absolute values.Kuhner et al. Motion Capture in Parkinson's DiseaseConclusions: This small pilot study identified the variability of a smoothness measure, i.e., jerk of the foot, as the optimal signal to separate healthy subjects' from PD patients' gait. This biomarker is invisible to clinicians' naked eye and is therefore not included in current motor assessments such as the UPDRS. We therefore recommend that more extensive investigations be conducted to identify the most powerful biomarkers to characterize motor abnormalities in PD. Future studies may challenge the composition of traditional assessments such as the UPDRS.

Machine learning for adaptive deep brain stimulation in Parkinson’s disease: closing the loop

“…Discrimination between PD and healthy subjects is also possible using information from wearables placed on a single arm with the implementation of the K-nearest neighbors (KNN) algorithm, providing a quantitative assessment of bradykinesia, rigidity and tremor [ 43 ]. Moreover RF algorithms using 3D gait data and motor readout signals have been used to show that a standing up test can be used to distinguish PD OFF DBS patients from healthy subjects; also, foot and lower leg kinematics are better in classifying motor anomalies than other gait analysis segments [ 44 , 45 ]. This contributes not only to the diagnosis of PD, but also to the monitoring of the symptom progression and response to treatment.…”

Parkinson’s disease therapy: what lies ahead?

“…Device-assisted digital assessments during inpatients visits can help objectify clinical evaluation, measure treatment response, and help overcome interrater variations. For example, digital motion biomarkers enable measurement of discreet movement disturbances not visible during routine examination (e.g., smoothness of gait and jerk of foot, Kuhner et al 2019 ). In parallel to cardinal motor features, machine learning-based speech analyses were able to identify early and mid-stage PD patients with high accuracy (Suppa et al 2022 ).…”

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

“…Several studies have deciphered motor classifiers by comparing certain motor response differences between healthy and Parkinson's subjects [37][38][39][40]. Kuhner et al [37,38] used RF algorithms to stratify 14 PD patients with (DBS-ON) or without (DBS-OFF) stimulation from 26 healthy subjects using a variety of motor control tasks. They found a 94.6% accuracy in distinguishing DBS-OFF patients from healthy subjects when using a standing up test.…”

“…They found a 94.6% accuracy in distinguishing DBS-OFF patients from healthy subjects when using a standing up test. Another study [38] performed 3D motion capture and gait analysis on 26 PD patients (seven with STN-DBS) and 25 healthy subjects. They identified classifiers (a combination of low-order time derivatives) that performed best at distinguishing DBS-OFF patients from healthy subjects.…”