Abstract The genetic tractability, well-mapped circuitry, and diverse behavioral repertoire of the nematode C. elegans make it an ideal model for physiological and behavioral studies. A wide range of methods has been developed for analyzing C. elegans behaviors, evolving with advances in technology such as videography and computer-assisted analysis. Here, we introduce LabGym—an open-source, artificial intelligence (AI)-based platform we recently developed—to the C. elegans research community. We trained deep learning models in LabGym capable of automatically categorizing and quantifying multiple user-defined parameters of worm locomotion behavior in multi-worm videos with high accuracy. Furthermore, we demonstrated their efficacy in quantifying locomotion changes in aging worms. Our work offers a cost-effective, user-accessible approach to behavioral analysis in C. elegans.
Xu et al. (Wed,) studied this question.