Behavioural Characterization of Healthy and Alzheimer's Disease Mice Using EthoVision Video Tracking System and Machine Learning
Year: 2024
Type: dissertation
Abstract: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, associated with the presence of amyloid-beta plaques and neurofibrillary tangles. Transgenic mouse models, which replicate AD pathologies, are essential for understanding disease mechanisms and testing potential therapies. However, differences in reported behaviours across various studies highlight the need for more advanced methods of analysis. This study aims to develop a machine learning algorithm to accurately classify mice as healthy or transgenic AD mice based on behavioural data from EthoVision video tracking system. Features from EthoVision were statistically analysed to identify those that significantly distinguish between AD and healthy mice. These identified features were subsequently used to train various machine learning models, including Logistic regression, Random forest, Support vector machine and an ensemble method. An analysis of feature importance revealed that elongation, distance travelled, and velocity were among the most critical indicators of AD-related behavioural changes. The algorithm demonstrated optimal performance using Random forest for females with an accuracy of 77% and using an ensemble method for males with an accuracy of 74%. These results highlight the potential of machine learning to improve the accuracy of behavioural analyses in AD research, providing valuable insights for future diagnostics and treatment strategies.
Author: Olivero, Corinne
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7036475595