Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.14.295808v1?rss=1 Authors: Huang, K., Han, Y., Chen, K., Pan, H., Yi, W., Li, X., Liu, S., Wei, P., Wang, L. Abstract: Objective quantification of animal behavior is crucial to understanding the relationship between brain activity and behavior. For rodents, this has remained a challenge due to the high-dimensionality and large temporal variability of their behavioral features. Inspired by the natural structure of animal behavior, the present study uses a parallel, multi-stage approach to decompose motion features and generate an objective metric for mapping rodent behavior into the animal's feature space. Incorporating a three-dimensional (3D) motion-capture system and unsupervised clustering into this approach, we developed a framework that can automatically identify animal behavioral phenotypes from experimental monitoring. We demonstrate the efficacy of our framework by generating an "autistic-like behavior space" that can robustly characterize a transgenic mouse disease model based on motor activity without human supervision. Our results suggest that our framework features a broad range of applications, including animal disease model phenotyping and the modeling of relationships between neural circuits and behavior. Copy rights belong to original authors. Visit the link for more info