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Gait Analysis of Age-dependent Motor Impairments in Mice with Neurodegeneration
Published on: June 18, 2018
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Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration
Ruyi Huang1,2,3, Ali A Nikooyan1,4, Bo Xu1,2
1Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, 300 Stein Plaza, Ste. 536, Los Angeles, CA, 90095-6901, USA.
Scientific Reports
|February 18, 2021
Summary
Alzheimer's disease (AD) causes motor deficits before cognitive decline. This study used motion capture in J20 mice to identify specific gait changes linked to amyloid precursor protein (APP) overexpression, aiding early AD diagnosis.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Gerontology
Background:
- Motor deficits manifest early in Alzheimer's disease (AD), preceding cognitive impairment.
- Amyloid precursor protein (APP) accumulation is a key factor in AD pathogenesis.
- Understanding the link between APP and motor dysfunction is crucial for early AD detection.
Purpose of the Study:
- To investigate the role of amyloid proteins in gait disturbances associated with Alzheimer's disease.
- To characterize locomotion in APP-overexpressing J20 transgenic mice as a model for AD.
- To identify specific kinematic features indicative of AD-related motor deficits.
Main Methods:
- Utilized three-dimensional motion capture to analyze quadrupedal locomotion on a treadmill.
- Studied J20 transgenic mice and wild-type littermates at 4 and 13 months of age.
- Employed a random forest classification algorithm with leave-one-out cross-validation to differentiate genotypes.
Main Results:
- Achieved high classification accuracy (92.3-93.3%) in distinguishing J20 mice from wild-type littermates.
- Identified age-specific kinematic features associated with APP overexpression.
- Found trunk tilt and hip instability critical in younger mice, while shoulder and iliac crest movements were key in older mice.
Conclusions:
- Simultaneous analysis of multiple gait kinematic features can accurately classify genotypes in a mouse model of AD.
- These findings highlight age-specific motor alterations in AD pathogenesis.
- The approach holds potential for developing methods to classify human motor disorders.
