Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia

Brandon Oubre,Christopher D Stephen,Kallie Whritenour,Anoopum S Gupta,Jeremy D Schmahmann,Jean-Francois Daneault,Nergis C Khan,Sunghoon Ivan Lee

doi:10.1007/s12311-021-01247-6

Abstract

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists. Data features designed to capture signs of ataxia were extracted from participants’ decomposed wrist velocity time-series. A machine learning regression model was trained to estimate overall ataxia severity, as measured by the Brief Ataxia Rating Scale (BARS). Classification models were trained to distinguish between ataxia participants and controls and between ataxia and parkinsonism phenotypes. Movement decomposition revealed expected and novel characteristics of the ataxia phenotype. The distance, speed, duration, morphology, and temporal relationships of decomposed movements exhibited strong relationships with disease severity. The regression model estimated BARS with a root mean square error of 3.6 points, r2 = 0.69, and moderate-to-excellent reliability. Classification models distinguished between ataxia participants and controls and ataxia and parkinsonism phenotypes with areas under the receiver-operating curve of 0.96 and 0.89, respectively. Movement decomposition captures core features of ataxia and may be useful for objective, precise, and frequent assessment of ataxia in home and clinic environments.

Highlights

Cerebellar ataxia is a neurologic phenotype caused by a heterogeneous set of underlying diseases that affect the function of the cerebellum [1]
Significant differences were identified between all groups for mean speed and distance, and between the high severity group and other groups for the duration
The performance of the models for distinguishing a diverse set of ataxia diagnoses from controls and parkinsonism indicate that movement elements capture characteristics relevant and specific to the ataxia phenotype

Summary

Introduction

Cerebellar ataxia is a neurologic phenotype caused by a heterogeneous set of underlying diseases that affect the function of the cerebellum [1]. Cerebellum ataxias are generally progressive conditions, resulting in functional impairments, limited autonomy, and reduced quality of life over time [3, 4]. Technologies that enable frequent, remote, and objective assessment of severity would, benefit clinical trials by lowering participation barriers and improving the ability to more sensitively and objectively measure disease state and progression. Towards this end, prior studies estimated ataxia severity using data collected from various sensing modalities including cameras [10], wearable sensors [11], instrumented spoons [12], and computer mice [13]

Methods

Results

Conclusion