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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Predicting executive functioning from walking features in Parkinson's disease using machine learning.

Artur Piet1, Johanna Geritz2, Pascal Garcia3

  • 1Institute of Medical Informatics, University of Luebeck, Ratzeburger Allee 160, 23562, Luebeck, Germany. ar.piet@uni-luebeck.de.

Scientific Reports
|November 28, 2024
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Summary
This summary is machine-generated.

This study reveals a link between Parkinson's disease patients' walking patterns and their executive functions. Specific gait features, like step time variability, significantly correlate with cognitive performance.

Keywords:
Executive functioningMachine learningParkinson’s diseaseWalking features

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Geriatrics

Background:

  • Parkinson's disease (PD) presents with both motor and cognitive impairments.
  • Existing research suggests a connection between motor symptoms (walking) and cognitive deficits in PD, but empirical evidence is limited.
  • Understanding this relationship is crucial for comprehensive PD management.

Purpose of the Study:

  • To investigate the association between specific walking features and executive functioning in Parkinson's disease patients.
  • To apply advanced machine learning techniques to analyze gait data and cognitive test results.
  • To identify gait parameters that may serve as biomarkers for cognitive status in PD.

Main Methods:

  • Analysis of gait data from 103 geriatric Parkinson's inpatients across four walking conditions using an inertial measurement unit (IMU).
  • Utilized five imputation methods and four regression approaches, including Multiple Imputation by Chained Equations (MICE) and Support Vector Regression (SVR).
  • Executive functioning was assessed using the Trail-Making Test (TMT).

Main Results:

  • Support Vector Regression (SVR) combined with Multiple Imputation by Chained Equations (MICE) improved prediction accuracy by 4.95%.
  • Gait features, particularly step time variability, double limb support time variability, and gait speed during dual-task walking with cognitive demands, significantly correlated with changes in executive functioning (Δ-TMT).
  • Machine learning models using only walking features showed a significant, albeit mild, correlation with executive functioning outcomes.

Conclusions:

  • This study provides direct empirical evidence linking specific gait characteristics to executive functioning in Parkinson's disease.
  • Walking features, especially under cognitively demanding conditions, may offer insights into cognitive status in PD patients.
  • These findings highlight the potential of using gait analysis as a non-invasive tool for assessing cognitive function in Parkinson's disease.