Gait-level analysis of mouse open field behavior using deep learning-based pose estimation

Abstract

1.AbstractGait and whole body posture are sensitive measures of the proper functioning of numerous neural circuits, and are often perturbed in many neurological, neuromuscular, and neuropsychiatric illnesses. Rodents provide a tractable model for elucidating disease mechanisms and interventions, however, studying gait and whole body posture in rodent models requires specialized methods and remains challenging. Here, we develop a simple assay that allows adoption of the commonly used open field apparatus for gait and whole body posture analysis. We leverage modern neural networks to abstract a mouse into keypoints and extract gait and whole body coordination metrics of the animal. Gait-level analysis allows us to detect every step of the animal’s movement and provides high resolution information about the animal’s behavior. We quantitate gait and whole body posture with high precision and accuracy across 62 highly visually diverse strains of mice. We validate our approach using four genetic mutants with known gait deficits. In extended analysis, we demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. We conduct a large strain survey of over 1898 mice, and find that gait and whole body posture measures are highly heritable in the laboratory mouse, and fall into three classes. Furthermore, the reference mouse strain, C57BL/6J, has a distinctly different gait and posture compared to other standard laboratory and wild-derived strains. We conduct a genome wide association study (GWAS) to define the genetic architecture of mouse movement in the open field. Combined, we describe a simple, sensitive, accurate, scalable, and ethologically relevant method of mouse gait and whole body posture analysis for behavioral neurogenetics. These results provide one of the largest laboratory mouse gait-level data resources for the research community and show the utility of automated machine learning approaches for biological insights.

A machine vision based frailty index for mice

“…Over the course of the data collection, 4 different scorers conducted the manual FI. The open field video was processed by a tracking network and a pose estimation network, to produce a track, an ellipse-fit, and a 12-point pose of the mouse for each frame [23,25]. These frame-by-frame measurements were used to calculate a variety of pervideo features.…”

“…These frame-by-frame measurements were used to calculate a variety of pervideo features. These features included traditional open field measures such as anxiety, hyperactivity [23], neural network-based grooming [24], and novel gait measures [25]. All features are defined in Section 3.2 and Supplementary Table S1.…”

“…We along with a number of other groups have applied these new methods to animal behavior analysis. Our group has developed methods for image segmentation and tracking in complex environments [23], action detection [24] and pose-based gait and whole body coordination measurements in the open field [25]. These and other highly sensitive methods have advanced animal behavior extraction [15,26,27].…”

Deep Phenotyping Reveals Movement Phenotypes in Mouse Neurodevelopmental Models

“…Sexdependent differences in gene regulation have been suggested to underlie the higher incidence of autism in males [23]. We then identified features of movement that were correlated with genetic background and specific genetic manipulations [10,24,25]. As a test of the method's discriminatory power, we explored gene-knockout (KO) models of neurodevelopmental disorders that perturb either the whole brain (Cntnap2 KO) or are cerebellum-specific (L7-Tsc1 mutant).…”

“…Mice with Purkinje cell specific (L7) null mutation of tuberous sclerosis 1 (Tsc1) reportedly exhibit a variety of social deficits compared to wild-type littermates, along with changes in gait, increased time spent grooming, and decreased behavioral flexibility [29][30][31]. Postural defects have been recently observed in many mouse models of autism [25], revealing an opportunity for deep phenotyping using machine-vision methods. Both of these strains exhibit similar altered spontaneous and task behavior compared to wild-type when using coarsegrained metrics; we applied our deep behavioral phenotyping to test for distinct signatures of behavior under spontaneous non-task conditions.…”