No abstract

Automated detection of complex animal behavior remains a challenge in behavioral neurogenetics. Developments in computer-vision have greatly advanced automated behavior detection and allow high-throughput pre-clinical studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational labs. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher level constructs such as frailty. Here, we describe in detail our integrated hardware and software solution for robust, high quality video monitoring of mice. We provide safety data for mice in long-term monitoring by recording environmental conditions in the arenas as well as health markers. The details provided here offer one potential solution for standardized high quality video data for computer vision tasks, and enables replication of our work.

Treatment of acute and chronic pain represent a widespread clinical challenge with poor therapeutic options. While rodents are an invaluable model to study pain, scoring nociceptive responses in clinically relevant paradigms and at high-throughput remains an unmet challenge. Therefore, there is a need for automated, high-throughput methods that sensitively and accurately assess pain and analgesia. Such objective and scalable technologies will enable the discovery of novel analgesics and yield mechanistic insights into the neural and genetic mechanisms of pain. Here, we adopt the open field arena to build a univariate scale for the formalin injection model of inflammatory pain by using a machine learning approach that incorporates 82 behavioral features. This tool outperforms traditional measures of licking and shaking in detection of formalin dose, and was validated using 4 diverse mouse strains. We also detected previously unreported differences in formalin induced nocifensive behaviors that were strain and sex specific. This model also reliably identifies morphine induced antinociception. This novel, sensitive, and inexpensive tool provides a method for quantifying voluntary nociceptive responses to facilitate genetic mapping and analgesic compound screening in a high throughput manner.