Automated ladder rung test for evaluating motor coordination in Parkinson's disease mouse models
Peng Zhang1, Wei Xu1, Wei Jiang1
1Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Background:
The ladder rung walking test assesses fine motor coordination in Parkinson's disease (PD) mouse models but relies on labor-intensive, subjective manual scoring, necessitating an automated, objective system.
New Method:
We developed a cost-effective automated ladder rung test system with a ladder featuring regular and irregular rung patterns, array through-beam optical sensors for foot-error detection, and an Arduino microcontroller. Custom Python software enables intuitive control, real-time visualization, dynamic sensor mapping, adjustable debounce, and CSV data export.
Results:
In an MPTP-induced PD mouse model, the system detected increased foot errors on irregular rungs (5.13 ± 1.04 vs. 1.78 ± 0.69 in controls, p < 0.0001) and longer traversal times (18.04 ± 2.64 s vs. 13.38 ± 1.95 s, p = 0.001), corroborated by open field and rotarod tests and a 68.7 % reduction in substantia nigra neurons.
Comparison With Existing Methods:
Unlike costly camera-based systems requiring complex algorithms, our system uses simple photoelectric sensors and costs approximately 127 USD for all components, achieving 96.4 % precision and 99.3 % recall, making it accessible and user-friendly.
Conclusions:
This automated system offers a reproducible, high-throughput tool for objective motor assessment in PD and neurological models, enhancing preclinical research.
