Topic: behavioral segmentation

A popular approach to quantifying animal behavior from video data is through behavioral segmentation, wherein video frames are labeled as containing one or more discrete behavior classes, such as grooming or rearing. These behaviors are often manually labeled, which is time consuming and error prone. An alternative approach is to train a sequence model which learns to map behavioral features extracted from video frames to discrete behaviors, although supervised models still require manually labeled examples to learn from. In order to reduce the need for expensive manual labels in this supervis

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