VAME outperforms conventional assessment of behavioral changes and treatment efficacy in Alzheimer’s mouse models
Stephanie Miller, Kevin Luxem, Kelli Lauderdale, Pranav Nambiar, Patrick Honma, Katie Ly, Shreya Bangera, Nick Kaliss, Mary Bullock, Jia Shin, Yuechen Qiu, K Dakota Mallen, Zhaoqi Yan, Andrew Mendiola, Takashi Saito, Takaomi Saido, Alex Pico, Reuben Thomas, Erik Roberson, Katerina Akassoglou, Pavol Bauer, Stefan Remy, Jorge Palop · COSYNE 2025
Alzheimer's disease (AD) is a progressive neurological disorder characterized by amyloid accumulation and behavioral changes across preclinical and clinical stages. Although AD is currently incurable, emerging machine-learning (ML) approaches are facilitating novel insights into disease etiology and treatment mechanisms. Our study utilized and built upon two open-source ML platforms, DeepLabCut (Mathis et al. 2018) and Variational Animal Motion Encoding (VAME) (Luxem et al. 2022), to track and analyze naturalistic behavior in App knock-in (KI) and 5xFAD transgenic mouse models of AD. New human