Computational gait analysis in the 3xTg-AD mouse model: evidence of progressive alterations in locomotor coordination.

INTRODUCTION

Gait alterations have been proposed as functional indicators of neural dysfunction in Alzheimer's disease; however, their ability to capture disease-associated locomotor changes in preclinical models remains insufficiently characterized.

METHODS

In this study, we applied a neuroinformatics framework combining markerless pose estimation, kinematic feature extraction, conventional statistical analysis, supervised machine learning, and model interpretability to characterize gait alterations in the 3xTg-AD mouse model. Two complementary analyses were performed: cross-sectional genotype classification at 12 months of age in 46 mice (3xTg-AD, n = 22; 3xTg-WT, n = 24) and longitudinal assessment of 31 paired surviving animals re-evaluated at 16 months (3xTg-AD, n = 15; 3xTg-WT, n = 16).

RESULTS

At 12 months, gait features provided moderate-to-good genotype discrimination, with Logistic Regression achieving the highest balanced performance (AUC = 0.82) and showing relevant contributions from Duty Factor, Allometric Stride Length, Allometric Step Width, and Speed. In the longitudinal cohort, classification performance was more limited (Random Forest, AUC = 0.61), likely reflecting the combined influence of physiological aging, genotype-associated change, selective survival, and task re-exposure. Nevertheless, conventional statistical analysis and SHAP-based model interpretation consistently identified longitudinal changes in Allometric Stride Length and Normalized Coordination Instability as relevant contributors to genotype-associated locomotor divergence.

DISCUSSION: These findings suggest that computational gait analysis can capture relevant alterations in locomotor organization in the 3xTg-AD model, with cross-sectional differences associated with distributed spatiotemporal features and longitudinal changes showing a stronger contribution of stride- and coordination-related variables.