| First Author | Abdus-Saboor I | Year | 2022 |
| Journal | Neuromethods | Volume | 178 |
| Pages | 441-456 | PubMed ID | 35783537 |
| Mgi Jnum | J:365829 | Mgi Id | MGI:8188294 |
| Doi | 10.1007/978-1-0716-2039-7_21 | Citation | Abdus-Saboor I, et al. (2022) Measuring Mouse Somatosensory Reflexive Behaviors with High-speed Videography, Statistical Modeling, and Machine Learning. Neuromethods 178:441-456 |
| abstractText | Objectively measuring and interpreting an animal's sensory experience remains a challenging task. This is particularly true when using preclinical rodent models to study pain mechanisms and screen for potential new pain treatment reagents. How to determine their pain states in a precise and unbiased manner is a hurdle that the field will need to overcome. Here, we describe our efforts to measure mouse somatosensory reflexive behaviors with greatly improved precision by high-speed video imaging. We describe how coupling sub-second ethograms of reflexive behaviors with a statistical reduction method and supervised machine learning can be used to create a more objective quantitative mouse "pain scale." Our goal is to provide the readers with a protocol of how to integrate some of the new tools described here with currently used mechanical somatosensory assays, while discussing the advantages and limitations of this new approach. |
