Using Deep Learning Approaches to Quantify Behavioral Changes in Normal Aging and Alzheimer's Disease Mouse Models
Document Type
Article
Publication Date
2025
Keywords
JMG
Sponsor
Surjeet Singh, Ph.D. and Kristen O'Connell, Ph.D.
Abstract
This study aims to characterize early changes in spontaneous behavior in normal aging mice and the Alzheimer’s Disease (AD) mouse model (App-SAA). By quantifying these changes, we will be able to identify early signs of spontaneous behavior that predict late-stage cognitive decline. Using the open field test (OFT), I quantified the amount of time spent and distance travelled by the mice in predefined regions (e.g., center vs periphery) in an open field arena. These measurements provided insight into the emergence of anxiety-related behavior during normal aging and in AD pathology.
Recommended Citation
Edwards, Namyanzi S.E., "Using Deep Learning Approaches to Quantify Behavioral Changes in Normal Aging and Alzheimer's Disease Mouse Models" (2025). Summer and Academic Year Student Reports. 2821.
https://mouseion.jax.org/strp/2821