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

Abstract

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&#x202f;&#xb1;&#x202f;1.04 vs. 1.78&#x202f;&#xb1;&#x202f;0.69 in controls, p&#x202f;<&#x202f;0.0001) and longer traversal times (18.04&#x202f;&#xb1;&#x202f;2.64&#x202f;s vs. 13.38&#x202f;&#xb1;&#x202f;1.95&#x202f;s, p&#x202f;=&#x202f;0.001), corroborated by open field and rotarod tests and a 68.7&#x202f;% 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&#x202f;% precision and 99.3&#x202f;% 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.