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

Pre-clinical Models for Geriatric Pharmacotherapy

With ageing of the population worldwide and discovery of new medications for prevention and management of age-related conditions, there is increasing use of medications by older adults. There are international efforts to increase the representativeness of participants in clinical trials to match the intended real-world users of the medications across a range of characteristics including age, multimorbidity, polypharmacy and frailty. Currently, much of the data on medication-related harm in older adults are from pharmacovigilance studies. New methods in pre-clinical models have allowed for measurement of exposures (such as chronic exposure, polypharmacy and deprescribing) and outcomes (such as health span functional measures and frailty) that are highly relevant to geriatric pharmacotherapy. Here we describe opportunities for design and implementation of pre-clinical models that can better predict drug effects in geriatric patients. This could improve the translation of new drugs from bench to bedside and improve outcomes of pharmacotherapy in older adults.

Visual detection of seizures in mice using supervised machine learning

Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Highly Accurate and Precise Determination of Mouse Mass Using Computer Vision

Changes in body mass are a key indicator of health and disease in humans and model organisms. Animal body mass is routinely monitored in husbandry and preclinical studies. In rodent studies, the current best method requires manually weighing the animal on a balance which has at least two consequences. First, direct handling of the animal induces stress and can have confounding effects on studies. Second, the acquired mass is static and not amenable to continuous assessment, and rapid mass changes can be missed. A noninvasive and continuous method of monitoring animal mass would have utility in multiple areas of biomedical research. Here, we test the feasibility of determining mouse body mass using video data. We combine computer vision methods with statistical modeling to demonstrate the feasibility of our approach. Our methods determine mouse mass with 4.8% error across highly genetically diverse mouse strains, with varied coat colors and mass. This error is low enough to replace manual weighing with image-based assessment in most mouse studies. We conclude that visual determination of rodent mass using video enables noninvasive and continuous monitoring and can improve animal welfare and preclinical studies.

NAPS: Integrating pose estimation and tag-based tracking

1. Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. 2. To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). 3. We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group. 4. Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.

Effects of heterozygous deletion of autism-related gene Cullin-3 in mice

Autism Spectrum Disorder (ASD) is a developmental disorder in which children display repetitive behavior, restricted range of interests, and atypical social interaction and communication. CUL3, coding for a Cullin family scaffold protein mediating assembly of ubiquitin ligase complexes through BTB domain substrate-recruiting adaptors, has been identified as a high-risk gene for autism. Although complete knockout of Cul3 results in embryonic lethality, Cul3 heterozygous mice have reduced CUL3 protein, demonstrate comparable body weight, and display minimal behavioral differences including decreased spatial object recognition memory. In measures of reciprocal social interaction, Cul3 heterozygous mice behaved similarly to their wild-type littermates. In area CA1 of hippocampus, reduction of Cul3 significantly increased mEPSC frequency but not amplitude nor baseline evoked synaptic transmission or paired-pulse ratio. Sholl and spine analysis data suggest there is a small yet significant difference in CA1 pyramidal neuron dendritic branching and stubby spine density. Unbiased proteomic analysis of Cul3 heterozygous brain tissue revealed dysregulation of various cytoskeletal organization proteins, among others. Overall, our results suggest that Cul3 heterozygous deletion impairs spatial object recognition memory, alters cytoskeletal organization proteins, but does not cause major hippocampal neuronal morphology, functional, or behavioral abnormalities in adult global Cul3 heterozygous mice.

A groom with a view

Mapping mouse grooming episodes to neural activity shows that striatal cells deep in the brain collectively represent key aspects of self-grooming.

Disruption of Astrocyte-Dependent Dopamine Control in the Developing Medial Prefrontal Cortex Leads to Excessive Grooming in Mice

BACKGROUND

Astrocytes control synaptic activity by modulating perisynaptic concentrations of ions and neurotransmitters including dopamine (DA) and, as such, could be involved in the modulating aspects of mammalian behavior.

METHODS

We produced a conditional deletion of the vesicular monoamine transporter 2 (VMAT2) specifically in astrocytes (aVMTA2cKO mice) and studied the effects of the lack of VMAT2 in prefrontal cortex (PFC) astrocytes on the regulation of DA levels, PFC circuit functions, and behavioral processes.

RESULTS

We found a significant reduction of medial PFC (mPFC) DA levels and excessive grooming and compulsive repetitive behaviors in aVMAT2cKO mice. The mice also developed a synaptic pathology, expressed through increased relative AMPA versus NMDA receptor currents in synapses of the dorsal striatum receiving inputs from the mPFC. Importantly, behavioral and synaptic phenotypes were rescued by re-expression of mPFC VMAT2 and L-DOPA treatment, showing that the deficits were driven by mPFC astrocytes that are critically involved in developmental DA homeostasis. By analyzing human tissue samples, we found that VMAT2 is expressed in human PFC astrocytes, corroborating the potential translational relevance of our observations in mice.

CONCLUSIONS

Our study shows that impairment of the astrocytic control of DA in the mPFC leads to symptoms resembling obsessive-compulsive spectrum disorders such as trichotillomania and has a profound impact on circuit function and behaviors.

A virtual library for behavioral performance in standard conditions-rodent spontaneous activity in an open field during repeated testing and after treatment with drugs or brain lesions

Background

Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose.

Findings

Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset.

Conclusions

Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions.

Whole-genome sequencing reveals the genetic mechanisms of domestication in classical inbred mice

Background

The laboratory mouse was domesticated from the wild house mouse. Understanding the genetics underlying domestication in laboratory mice, especially in the widely used classical inbred mice, is vital for studies using mouse models. However, the genetic mechanism of laboratory mouse domestication remains unknown due to lack of adequate genomic sequences of wild mice.

Results

We analyze the genetic relationships by whole-genome resequencing of 36 wild mice and 36 inbred strains. All classical inbred mice cluster together distinctly from wild and wild-derived inbred mice. Using nucleotide diversity analysis, Fst, and XP-CLR, we identify 339 positively selected genes that are closely associated with nervous system function. Approximately one third of these positively selected genes are highly expressed in brain tissues, and genetic mouse models of 125 genes in the positively selected genes exhibit abnormal behavioral or nervous system phenotypes. These positively selected genes show a higher ratio of differential expression between wild and classical inbred mice compared with all genes, especially in the hippocampus and frontal lobe. Using a mutant mouse model, we find that the SNP rs27900929 (T>C) in gene Astn2 significantly reduces the tameness of mice and modifies the ratio of the two Astn2 (a/b) isoforms.

Conclusion

Our study indicates that classical inbred mice experienced high selection pressure during domestication under laboratory conditions. The analysis shows the positively selected genes are closely associated with behavior and the nervous system in mice. Tameness may be related to the Astn2 mutation and regulated by the ratio of the two Astn2 (a/b) isoforms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02772-1.

A machine-vision-based frailty index for mice

Heterogeneity in biological aging manifests itself in health status and mortality. Frailty indices (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. Here we introduce a machine-learning-based visual FI for mice that operates on video data from an open-field assay. We use machine vision to extract morphometric, gait and other behavioral features that correlate with FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 ± 0.002 (mean absolute error). Unnormalized, this error is 1.08 ± 0.05, which is comparable to one FI item being mis-scored by 1 point or two FI items mis-scored by 0.5 points. This visual FI provides increased reproducibility and scalability that will enable large-scale mechanistic and interventional studies of aging in mice.

Historical and Modern Evidence for the Role of Reward Circuitry in Emergence

Increasing evidence supports a role for brain reward circuitry in modulating arousal along with emergence from anesthesia. Emergence remains an important frontier for investigation, since no drug exists in clinical practice to initiate rapid and smooth emergence. This review discusses clinical and preclinical evidence indicating a role for two brain regions classically considered integral components of the mesolimbic brain reward circuitry, the ventral tegmental area and the nucleus accumbens, in emergence from propofol and volatile anesthesia. Then there is a description of modern systems neuroscience approaches to neural circuit investigations that will help span the large gap between preclinical and clinical investigation with the shared aim of developing therapies to promote rapid emergence without agitation or delirium. This article proposes that neuroscientists include models of whole-brain network activity in future studies to inform the translational value of preclinical investigations and foster productive dialogues with clinician anesthesiologists.

Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience

The use of rigorous ethological observation via machine learning techniques to understand brain function (computational neuroethology) is a rapidly growing approach that is poised to significantly change how behavioral neuroscience is commonly performed. With the development of open-source platforms for automated tracking and behavioral recognition, these approaches are now accessible to a wide array of neuroscientists despite variations in budget and computational experience. Importantly, this adoption has moved the field toward a common understanding of behavior and brain function through the removal of manual bias and the identification of previously unknown behavioral repertoires. Although less apparent, another consequence of this movement is the introduction of analytical tools that increase the explainabilty, transparency, and universality of the machine-based behavioral classifications both within and between research groups. Here, we focus on three main applications of such machine model explainabilty tools and metrics in the drive toward behavioral (i) standardization, (ii) specialization, and (iii) explainability. We provide a perspective on the use of explainability tools in computational neuroethology, and detail why this is a necessary next step in the expansion of the field. Specifically, as a possible solution in behavioral neuroscience, we propose the use of Shapley values via Shapley Additive Explanations (SHAP) as a diagnostic resource toward explainability of human annotation, as well as supervised and unsupervised behavioral machine learning analysis.

A Large-Scale Mouse Pose Dataset for Mouse Pose Estimation

Mouse pose estimations have important applications in the fields of animal behavior research, biomedicine, and animal conservation studies. Accurate and efficient mouse pose estimations using computer vision are necessary. Although methods for mouse pose estimations have developed, bottlenecks still exist. One of the most prominent problems is the lack of uniform and standardized training datasets. Here, we resolve this difficulty by introducing the mouse pose dataset. Our mouse pose dataset contains 40,000 frames of RGB images and large-scale 2D ground-truth motion images. All the images were captured from interacting lab mice through a stable single viewpoint, including 5 distinct species and 20 mice in total. Moreover, to improve the annotation efficiency, five keypoints of mice are creatively proposed, in which one keypoint is at the center and the other two pairs of keypoints are symmetric. Then, we created simple, yet effective software that works for annotating images. It is another important link to establish a benchmark model for 2D mouse pose estimations. We employed modified object detections and pose estimation algorithms to achieve precise, effective, and robust performances. As the first large and standardized mouse pose dataset, our proposed mouse pose dataset will help advance research on animal pose estimations and assist in application areas related to animal experiments.

Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models

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 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. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD.

METHODS

We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales 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 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts 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. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy.

LIMITATIONS

Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under 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 quantitative diagnosis criteria.

Indirect Genetic Effects: A Cross-disciplinary Perspective on Empirical Studies

Indirect genetic effects (IGE) occur when an individual's phenotype is influenced by genetic variation in conspecifics. Opportunities for IGE are ubiquitous, and, when present, IGE have profound implications for behavioral, evolutionary, agricultural, and biomedical genetics. Despite their importance, the empirical study of IGE lags behind the development of theory. In large part, this lag can be attributed to the fact that measuring IGE, and deconvoluting them from the direct genetic effects of an individual's own genotype, is subject to many potential pitfalls. In this Perspective, we describe current challenges that empiricists across all disciplines will encounter in measuring and understanding IGE. Using ideas and examples spanning evolutionary, agricultural, and biomedical genetics, we also describe potential solutions to these challenges, focusing on opportunities provided by recent advances in genomic, monitoring, and phenotyping technologies. We hope that this cross-disciplinary assessment will advance the goal of understanding the pervasive effects of conspecific interactions in biology.

Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network

Grooming is a common behavior for animals to care for their fur, maintain hygiene, and regulate body temperature. Since various factors, including stressors and genetic mutations, affect grooming quantitatively and qualitatively, the assessment of grooming is important to understand the status of experimental animals. However, current grooming detection methods are time-consuming, laborious, and require specialized equipment. In addition, they generally cannot discriminate grooming microstructures such as face washing and body licking. In this study, we aimed to develop an automated grooming detection method that can distinguish facial grooming from body grooming by image analysis using artificial intelligence. Mouse behavior was recorded using a standard hand camera. We carefully observed videos and labeled each time point as facial grooming, body grooming, and not grooming. We constructed a three-dimensional convolutional neural network (3D-CNN) and trained it using the labeled images. Since the output of the trained 3D-CNN included unlikely short grooming bouts and interruptions, we set posterior filters to remove them. The performance of the trained 3D-CNN and filters was evaluated using a first-look dataset that was not used for training. The sensitivity of facial and body grooming detection reached 81.3% and 91.9%, respectively. The positive predictive rates of facial and body grooming detection were 83.5% and 88.5%, respectively. The number of grooming bouts predicted by our method was highly correlated with human observations (face: r = 0.93, body: r = 0.98). These results highlight that our method has sufficient ability to distinguish facial grooming and body grooming in mice.

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

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.

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.

Variation and Variability in Drosophila Grooming Behavior

Behavioral differences can be observed between species or populations (variation) or between individuals in a genetically similar population (variability). Here, we investigate genetic differences as a possible source of variation and variability in Drosophila grooming. Grooming confers survival and social benefits. Grooming features of five Drosophila species exposed to a dust irritant were analyzed. Aspects of grooming behavior, such as anterior to posterior progression, were conserved between and within species. However, significant differences in activity levels, proportion of time spent in different cleaning movements, and grooming syntax were identified between species. All species tested showed individual variability in the order and duration of action sequences. Genetic diversity was not found to correlate with grooming variability within a species: melanogaster flies bred to increase or decrease genetic heterogeneity exhibited similar variability in grooming syntax. Individual flies observed on consecutive days also showed grooming sequence variability. Standardization of sensory input using optogenetics reduced but did not eliminate this variability. In aggregate, these data suggest that sequence variability may be a conserved feature of grooming behavior itself. These results also demonstrate that large genetic differences result in distinguishable grooming phenotypes (variation), but that genetic heterogeneity within a population does not necessarily correspond to an increase in the range of grooming behavior (variability).

BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking

Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software's graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.

Comparison of SHANK3 deficiency in animal models: phenotypes, treatment strategies, and translational implications

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition, which is characterized by clinical heterogeneity and high heritability. Core symptoms of ASD include deficits in social communication and interaction, as well as restricted, repetitive patterns of behavior, interests, or activities. Many genes have been identified that are associated with an increased risk for ASD. Proteins encoded by these ASD risk genes are often involved in processes related to fetal brain development, chromatin modification and regulation of gene expression in general, as well as the structural and functional integrity of synapses. Genes of the SH3 and multiple ankyrin repeat domains (SHANK) family encode crucial scaffolding proteins (SHANK1-3) of excitatory synapses and other macromolecular complexes. SHANK gene mutations are highly associated with ASD and more specifically the Phelan-McDermid syndrome (PMDS), which is caused by heterozygous 22q13.3-deletion resulting in SHANK3-haploinsufficiency, or by SHANK3 missense variants. SHANK3 deficiency and potential treatment options have been extensively studied in animal models, especially in mice, but also in rats and non-human primates. However, few of the proposed therapeutic strategies have translated into clinical practice yet.

MAIN TEXT

This review summarizes the literature concerning SHANK3-deficient animal models. In particular, the structural, behavioral, and neurological abnormalities are described and compared, providing a broad and comprehensive overview. Additionally, the underlying pathophysiologies and possible treatments that have been investigated in these models are discussed and evaluated with respect to their effect on ASD- or PMDS-associated phenotypes.

CONCLUSIONS

Animal models of SHANK3 deficiency generated by various genetic strategies, which determine the composition of the residual SHANK3-isoforms and affected cell types, show phenotypes resembling ASD and PMDS. The phenotypic heterogeneity across multiple models and studies resembles the variation of clinical severity in human ASD and PMDS patients. Multiple therapeutic strategies have been proposed and tested in animal models, which might lead to translational implications for human patients with ASD and/or PMDS. Future studies should explore the effects of new therapeutic approaches that target genetic haploinsufficiency, like CRISPR-mediated activation of promotors.