Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation
| Autor: | Keith Sheppard, Justin Gardin, Gautam S. Sabnis, Asaf Peer, Megan Darrell, Sean Deats, Brian Geuther, Cathleen M. Lutz, Vivek Kumar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Cell reports |
| ISSN: | 2211-1247 |
| Popis: | SUMMARY Gait and posture are often perturbed in many neurological, neuromuscular, and neuropsychiatric conditions. Rodents provide a tractable model for elucidating disease mechanisms and interventions. Here, we develop a neural-network-based assay that adopts the commonly used open field apparatus for mouse gait and posture analysis. We quantitate both with high precision across 62 strains of mice. We characterize four mutants with known gait deficits and demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. Mouse gait and posture measures are highly heritable and fall into three distinct classes. We conduct a genome-wide association study to define the genetic architecture of stride-level mouse movement in the open field. We provide a method for gait and posture extraction from the open field and one of the largest laboratory mouse gait and posture data resources for the research community. Graphical abstract In brief Sheppard et al. present a method for gait and posture analysis in the common open field apparatus using neural-network-based pose estimation. They apply this high-throughput method to dissect the genetic architecture of mouse movement. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|