Automated ladder rung test for evaluating motor coordination in Parkinson's disease mouse models.

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.