JAX Animal Behavior System (JABS): A video-based phenotyping platform for the laboratory mouse

Beane, Glen L.; Geuther, Brian Q.; Sproule, Thomas J.; Trapszo, Jarek; Hession, Leinani; Kohar, Vivek; Kumar, Vivek

doi:10.1101/2022.01.13.476229

Cited by 4 publications

(6 citation statements)

References 38 publications

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“…Ease of use of the trained model by non-computational labs is an important challenge. Therefore, in addition to implementation details in the Methods section, we have detailed our integrated mouse phenotyping platform– a hardware and software solution that provides tracking, pose estimation, feature generation, and automated behavior analysis in [57]. This platform requires a specific open field apparatus, however, researchers would be able to use the trained model if they generate the same features as our model using their own open field data-collection apparatus.…”

Section: Discussionmentioning

confidence: 99%

“…The open field behavioral assays were conducted as described in [16, 23, 57]. Mice were shipped from Nathan Shock Center aging colony which resides in different room in the same animal facility at JAX.…”

Section: Methodsmentioning

confidence: 99%

“…Our open field arena, video apparatus, and tracking and segmentation networks are as detailed previously [23, 57]. Briefly, the open field arena measures 20.5” by 20.5” with Sentech camera mounted 40 inches above.…”

Section: Methodsmentioning

confidence: 99%

“…Open field measures were derived from ellipse tracking of mice as described before [23, 24, 57]. The tracking was used to produce locomotor activity and anxiety features.…”

Section: Methodsmentioning

confidence: 99%

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A machine vision based frailty index for mice

et al. 2021

Preprint

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Chronological aging is uniform, but biological aging is heterogeneous. Clinically, this heterogeneity manifests itself in health status and mortality, and it distinguishes healthy from unhealthy aging. Clinical frailty indexes (FIs) serve as an important tool in gerontology to capture health status. FIs have been adapted for use in mice and are an effective predictor of mortality risk. To accelerate our understanding of biological aging, high-throughput approaches to pre-clinical studies are necessary. Currently, however, mouse frailty indexing is manual and relies on trained scorers, which imposes limits on scalability and reliability. Here, we introduce a machine learning based visual frailty index (vFI) for mice that operates on video data from an open field assay. We generate a large mouse FI dataset of both male and female mice. From video data on these same mice, we use neural networks to extract morphometric, gait, and other behavioral features that correlate with manual FI score and age. We use these features to train a regression model that accurately predicts frailty within 0.04 ± 0.002 (3.7% ± 0.01%) of the pre-normalized FI score in terms of mean absolute error. We show that features of biological aging are encoded in open-field video data and can be used to construct a vFI that can complement or replace current manual FI methods. We use the vFI data to examine sex-specific aspects of aging in mice. This vFI provides increased accuracy, reproducibility, and scalability, that will enable large scale mechanistic and interventional studies of aging in mice.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Open field measures were derived from ellipse tracking of mice as described before [23, 24, 57]. The tracking was used to produce locomotor activity and anxiety features.…”

Section: Methodsmentioning

confidence: 99%

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A machine vision based frailty index for mice

et al. 2021

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“…We first aimed to automate the scoring of behaviors in the open field formalin assay using JABS constructed behavioral classifiers, heuristic measures, and gait.JABS is an active learning system that uses 12 point pose coordinates and calculates distances and angles between points at each frame across a specified window to train a behavior classifier [40]. One behavior we classified is licking/biting.…”

Section: Features Correlated With Nociceptionmentioning

confidence: 99%

A high-throughput machine vision-based univariate scale for pain and analgesia in mice

Sabnis

Hession

Kim

et al. 2022

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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.

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