RT Journal Article SR Electronic T1 Mapping Mouse Behavior with an Unsupervised Spatio-temporal Sequence Decomposition Framework JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.09.14.295808 DO 10.1101/2020.09.14.295808 A1 Kang Huang A1 Yaning Han A1 Ke Chen A1 Hongli Pan A1 Wenling Yi A1 Xiaoxi Li A1 Siyuan Liu A1 Pengfei Wei A1 Liping Wang YR 2020 UL http://biorxiv.org/content/early/2020/09/14/2020.09.14.295808.abstract AB 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.Competing Interest StatementThe authors have declared no competing interest.