The Morris Water Maze (MWM) is a widely used behavioral test to assess the spatial
learning and memory of animals, particularly valuable in studying neurodegenerative
disorders such as Alzheimer’s disease. Traditional methods for analyzing MWM experiments
often face limitations in capturing the complexity of animal behaviors. In this study,
we present a novel AI-based automated framework to process and evaluate MWM test videos,
aiming to enhance behavioral analysis through machine learning. Our pipeline involves
video preprocessing, animal detection using convolutional neural networks (CNNs),
trajectory tracking, and postprocessing to derive detailed behavioral features. We
propose concentric circle segmentation of the pool next to the quadrant-based division,
and we extract 32 behavioral metrics for each zone, which are employed in classification
tasks to differentiate between younger and older animals. Several machine learning
classifiers, including random forest and neural networks, are evaluated, with feature
selection techniques applied to improve the classification accuracy. Our results demonstrate
a significant improvement in classification performance, particularly through the
integration of feature sets derived from concentric zone analyses. This automated
approach offers a robust solution for MWM data processing, providing enhanced precision
and reliability, which is critical for the study of neurodegenerative disorders.
