Action detection using a neural network elucidates the genetics of mouse grooming behavior

Geuther, Brian Q.; Peer, Asaf; He, Hao; Sabnis, Gautam; Philip, Vivek; Kumar, Vivek

doi:10.7554/elife.63207

Cited by 46 publications

(46 citation statements)

References 71 publications

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“…We have previously used versions of the JABS data acquisition module to collect short (1-hr) and long (4 -30 days) of continuous video data [8][9][10][11]. The modifiable front panel allows customization, such as use of single or multiple water bottles to conduct 2-bottle choice experiments with ethanol or methamphetamine.…”

Section: Resultsmentioning

confidence: 99%

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

Beane

Geuther

Sproule

et al. 2022

Preprint

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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.

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Section: Resultsmentioning

confidence: 99%

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

Beane

Geuther

Sproule

et al. 2022

Preprint

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“…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.…”

Section: Data Collectionmentioning

confidence: 99%

“…The frame-by-frame segmentation, ellipse fit, and 12-point pose coordinates were used to extract pervideo features [23][24][25]. All extracted features with explanation and source of the measurements can be found in Supplementary Table S1.…”

Section: Feature Extractionmentioning

confidence: 99%

A machine vision based frailty index for mice

et al. 2021

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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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“…Grooming behaviors vary in a complex manner in neurodevelopmental mouse models Grooming refers to a variety of repetitive self-touching behaviors, and mouse selfgrooming has been used as an animal model for the self-stimulating behaviors observed in autism [39]. Previous reports have limited quantification to 10 minutes of observation and have not distinguished different types of self-grooming or reported sex differences [29,40,41]. In our four-day recording period, we observed that all mouse groups groomed more frequently as the days passed (Fig.…”

Section: Spatial Habituation Is Reduced In L7-tsc1 Mutant Micementioning

confidence: 99%

Deep Phenotyping Reveals Movement Phenotypes in Mouse Neurodevelopmental Models

Klibaite

Kislin

Verpeut

et al. 2021

Preprint

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Background: Repetitive action, resistance to environmental change, and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here, we aimed at investigating behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice, both mutations are found in the clinical conditions and have been associated with ASD. We observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. Methods: Here, we use advances in computer vision and deep learning, a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple time scales using a single popular behavioral assay, the open field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results: Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations: Genetic risk factors for autism are numerous, and here we tested only two. Our pipeline was only applied to conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as a quantitative diagnosis criteria.

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Action detection using a neural network elucidates the genetics of mouse grooming behavior

Cited by 46 publications

References 71 publications

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

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

A machine vision based frailty index for mice

Deep Phenotyping Reveals Movement Phenotypes in Mouse Neurodevelopmental Models

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