Effective identification of Alzheimer's disease in mouse models via deep learning and motion analysis

Abstract

Spatial disorientation is an early symptom of Alzheimer's disease (AD). Detecting this impairment effectively in animal models can provide valuable insights into the disease and reduce experimental burdens. We have developed a markerless motion analysis system (MMAS) using deep learning techniques for the Morris water maze test. This system allows for precise analysis of behaviors and body movements from video recordings. Using the MMAS, we identified unilateral head-turning and tail-wagging preferences in AD mice, which distinguished them from wild-type mice with greater accuracy than traditional behavioral parameters. Furthermore, the cumulative turning and wagging angles were linearly correlated with escape latency and cognitive scores, demonstrating comparable effectiveness in differentiating AD mice. These findings underscore the potential of motion analysis as an advanced method for improving the effectiveness, sensitivity, and interpretability of AD mouse identification, ultimately aiding in disease diagnosis and drug development.