Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

A line attractor encoding a persistent internal state requires neuropeptide signaling

Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant. Here, we demonstrate that neuropeptidergic signaling is necessary for line attractor dynamics in this system by using cell-type-specific CRISPR-Cas9-based gene editing combined with single-cell calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1+ neurons that control aggression diminished attack, reduced persistent neural activity, and eliminated line attractor dynamics while only slightly reducing overall neural activity and sex- or behavior-specific tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor in mammals. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Encoding of female mating dynamics by a hypothalamic line attractor

Females exhibit complex, dynamic behaviours during mating with variable sexual receptivity depending on hormonal status1-4. However, how their brains encode the dynamics of mating and receptivity remains largely unknown. The ventromedial hypothalamus, ventrolateral subdivision contains oestrogen receptor type 1-positive neurons that control mating receptivity in female mice5,6. Here, unsupervised dynamical system analysis of calcium imaging data from these neurons during mating uncovered a dimension with slow ramping activity, generating a line attractor in neural state space. Neural perturbations in behaving females demonstrated relaxation of population activity back into the attractor. During mating, population activity integrated male cues to ramp up along this attractor, peaking just before ejaculation. Activity in the attractor dimension was positively correlated with the degree of receptivity. Longitudinal imaging revealed that attractor dynamics appear and disappear across the oestrus cycle and are hormone dependent. These observations suggest that a hypothalamic line attractor encodes a persistent, escalating state of female sexual arousal or drive during mating. They also demonstrate that attractors can be reversibly modulated by hormonal status, on a timescale of days.

Long-term tracking of social structure in groups of rats

Rodents serve as an important model for examining both individual and collective behavior. Dominance within rodent social structures can determine access to critical resources, such as food and mating opportunities. Yet, many aspects of the intricate interplay between individual behaviors and the resulting group social hierarchy, especially its evolution over time, remain unexplored. In this study, we utilized an automated tracking system that continuously monitored groups of male rats for over 250 days to enable an in-depth analysis of individual behavior and the overarching group dynamic. We describe the evolution of social structures within a group and additionally investigate how past behaviors influence the emergence of new social hierarchies when group composition and experimental area changes. Notably, we find that conventional individual and pairwise tests exhibit a weak correlation with group behavior, highlighting their limited accuracy in predicting behavioral outcomes in a collective context. These results emphasize the context-dependence of social behavior as an emergent property of interactions within a group and highlight the need to measure and quantify social behavior in more naturalistic environments.

Mapping the landscape of social behavior

Social interaction is integral to animal behavior. However, we lack tools to describe it with quantitative rigor, limiting our understanding of its principles and neuropsychiatric disorders, like autism, that perturb it. Here, we present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals, solving the challenging subject occlusion and part assignment problems using 3D geometric reasoning, graph neural networks, and semi-supervised learning. We collected over 140 million 3D postures in interacting rodents, featuring new monogenic autism rat lines lacking reports of social behavioral phenotypes. Using a novel multi-scale embedding approach, we identified a rich landscape of stereotyped actions, interactions, synchrony, and body contact. This enhanced phenotyping revealed a spectrum of changes in autism models and in response to amphetamine that were inaccessible to conventional measurements. Our framework and large library of interactions will greatly facilitate studies of social behaviors and their neurobiological underpinnings.

Mapping the developmental structure of stereotyped and individual-unique behavioral spaces in C. elegans

Developmental patterns of behavior are variably organized in time and among different individuals. However, long-term behavioral diversity was previously studied using pre-defined behavioral parameters, representing a limited fraction of the full individuality structure. Here, we continuously extract ∼1.2 billion body postures of ∼2,200 single C. elegans individuals throughout their full development time to create a complete developmental atlas of stereotyped and individual-unique behavioral spaces. Unsupervised inference of low-dimensional movement modes of each single individual identifies a dynamic developmental trajectory of stereotyped behavioral spaces and exposes unique behavioral trajectories of individuals that deviate from the stereotyped patterns. Moreover, classification of behavioral spaces within tens of neuromodulatory and environmentally perturbed populations shows plasticity in the temporal structures of stereotyped behavior and individuality. These results present a comprehensive atlas of continuous behavioral dynamics across development time and a general framework for unsupervised dissection of shared and unique developmental signatures of behavior.

Microglial type I interferon signaling mediates chronic stress-induced synapse loss and social behavior deficits

Inflammation and synapse loss have been associated with deficits in social behavior and are involved in pathophysiology of many neuropsychiatric disorders. Synapse loss, characterized by reduction in dendritic spines can significantly disrupt synaptic connectivity and neural circuitry underlying social behavior. Chronic stress is known to induce loss of spines and dendrites in the prefrontal cortex (PFC), a brain region implicated in social behavior. However, the underlying mechanisms are not well understood. In the present study, we investigated the role of type I Interferon (IFN-I) signaling in chronic unpredictable stress (CUS)-induced synapse loss and behavior deficits in mice. We found increased expression of type I IFN receptor (IFNAR) in microglia following CUS. Conditional knockout of microglial IFNAR in adult mice rescued CUS-induced social behavior deficits and synapse loss. Bulk RNA sequencing data show that microglial IFNAR deletion attenuated CUS-mediated changes in the expression of genes such as Keratin 20 (Krt20), Claudin-5 (Cldn5) and Nuclear Receptor Subfamily 4 Group A Member 1 (Nr4a1) in the PFC. Cldn5 and Nr4a1 are known for their roles in synaptic plasticity. Krt20 is an intermediate filament protein responsible for the structural integrity of epithelial cells. The reduction in Krt20 following CUS presents a novel insight into the potential contribution of cytokeratin in stress-induced alterations in neuroplasticity. Overall, these results suggest that microglial IFNAR plays a critical role in regulating synaptic plasticity and social behavior deficits associated with chronic stress conditions.

Why do patients with cancer die?

Cancer is a major cause of global mortality, both in affluent countries and increasingly in developing nations. Many patients with cancer experience reduced life expectancy and have metastatic disease at the time of death. However, the more precise causes of mortality and patient deterioration before death remain poorly understood. This scarcity of information, particularly the lack of mechanistic insights, presents a challenge for the development of novel treatment strategies to improve the quality of, and potentially extend, life for patients with late-stage cancer. In addition, earlier deployment of existing strategies to prolong quality of life is highly desirable. In this Roadmap, we review the proximal causes of mortality in patients with cancer and discuss current knowledge about the interconnections between mechanisms that contribute to mortality, before finally proposing new and improved avenues for data collection, research and the development of treatment strategies that may improve quality of life for patients.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

Characterizing the structure of mouse behavior using Motion Sequencing

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

Measuring the replicability of our own research

In the study of transgenic mouse models of neurodevelopmental and neurodegenerative disorders, we use batteries of tests to measure deficits in behaviour and from the results of these tests, we make inferences about the mental states of the mice that we interpret as deficits in "learning", "memory", "anxiety", "depression", etc. This paper discusses the problems of determining whether a particular transgenic mouse is a valid mouse model of disease X, the problem of background strains, and the question of whether our behavioural tests are measuring what we say they are. The problem of the reliability of results is then discussed: are they replicable between labs and can we replicate our results in our own lab? This involves the study of intra- and inter- experimenter reliability. The variables that influence replicability and the importance of conducting a complete behavioural phenotype: sensory, motor, cognitive and social emotional behaviour are discussed. Then the thorny question of failure to replicate is examined: Is it a curse or a blessing? Finally, the role of failure in research and what it tells us about our research paradigms is examined.

Studying dominance and aggression requires ethologically relevant paradigms

Although aggression is associated with several psychiatric disorders, there is no effective treatment nor a rigorous definition for "pathological aggression". Mice make a valuable model for studying aggression. They have a dynamic social structure that depends on the habitat and includes reciprocal interactions between the mice's aggression levels, social dominance hierarchy (SDH), and resource allocation. Nevertheless, the classical behavioral tests for territorial aggression and SDH in mice are reductive and have limited ethological and translational relevance. Recent work has explored the use of semi-natural environments to simultaneously study dominance-related behaviors, resource allocation, and aggressive behavior. Semi-natural setups allow experimental control of the environment combined with manipulations of neural activity. We argue that these setups can help bridge the translational gap in aggression research toward discovering neuronal mechanisms underlying maladaptive aggression.

A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior

To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

Development of a 3D tracking system for multiple marmosets under free-moving conditions

Assessment of social interactions and behavioral changes in nonhuman primates is useful for understanding brain function changes during life events and pathogenesis of neurological diseases. The common marmoset (Callithrix jacchus), which lives in a nuclear family like humans, is a useful model, but longitudinal automated behavioral observation of multiple animals has not been achieved. Here, we developed a Full Monitoring and Animal Identification (FulMAI) system for longitudinal detection of three-dimensional (3D) trajectories of each individual in multiple marmosets under free-moving conditions by combining video tracking, Light Detection and Ranging, and deep learning. Using this system, identification of each animal was more than 97% accurate. Location preferences and inter-individual distance could be calculated, and deep learning could detect grooming behavior. The FulMAI system allows us to analyze the natural behavior of individuals in a family over their lifetime and understand how behavior changes due to life events together with other data.

Markerless Mouse Tracking for Social Experiments

Automated behavior quantification in socially interacting animals requires accurate tracking. While many methods have been very successful and highly generalizable to different settings, issues of mistaken identities and lost information on key anatomical features are common, although they can be alleviated by increased human effort in training or post-processing. We propose a markerless video-based tool to simultaneously track two interacting mice of the same appearance in controlled settings for quantifying behaviors such as different types of sniffing, touching, and locomotion to improve tracking accuracy under these settings without increased human effort. It incorporates conventional handcrafted tracking and deep-learning-based techniques. The tool is trained on a small number of manually annotated images from a basic experimental setup and outputs body masks and coordinates of the snout and tail-base for each mouse. The method was tested on several commonly used experimental conditions including bedding in the cage and fiberoptic or headstage implants on the mice. Results obtained without any human corrections after the automated analysis showed a near elimination of identities switches and a ∼15% improvement in tracking accuracy over pure deep-learning-based pose estimation tracking approaches. Our approach can be optionally ensembled with such techniques for further improvement. Finally, we demonstrated an application of this approach in studies of social behavior of mice by quantifying and comparing interactions between pairs of mice in which some lack olfaction. Together, these results suggest that our approach could be valuable for studying group behaviors in rodents, such as social interactions.

Detecting and Tracking of Multiple Mice Using Part Proposal Networks

The study of mouse social behaviors has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviors from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this article, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. First, we propose an efficient and robust deep-learning-based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian-inference integer linear programming (BILP) model that jointly assigns the part candidates to individual targets with necessary geometric constraints while establishing pair-wise association between the detected parts. There is no publicly available dataset in the research community that provides a quantitative test bed for part detection and tracking of multiple mice, and we here introduce a new challenging Multi-Mice PartsTrack dataset that is made of complex behaviors. Finally, we evaluate our proposed approach against several baselines on our new datasets, where the results show that our method outperforms the other state-of-the-art approaches in terms of accuracy. We also demonstrate the generalization ability of the proposed approach on tracking zebra and locust.

Artificial intelligence in epilepsy phenotyping

Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.

Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State

Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Automating behavioral analysis in neuroscience: Development of an open-source python software for more consistent and reliable results

BACKGROUND

The application of automated analyses in neuroscience has become a practical approach. With automation, the algorithms and tools employed perform fast and accurate data analysis. It minimizes the inherent errors of manual analysis performed by a human experimenter. It also reduces the time required to analyze a large amount of data and the need for human and financial resources.

METHODS

In this work, we describe a protocol for the automated analysis of the Morris Water Maze (MWM) and the Open Field (OF) test using the OpenCV library in Python. This simple protocol tracks mice navigation with high accuracy.

RESULTS

In the MWM, both automated and manual analysis revealed similar results regarding the time the mice stayed in the target quadrant (p = 0.109). In the OF test, both automated and manual analysis revealed similar results regarding the time the mice stayed in the center (p = 0.520) and in the border (p = 0.503) of the field.

CONCLUSIONS

The automated analysis protocol has several advantages over manual analysis. It saves time, reduces human errors, can be customized, and provides more consistent information about animal behavior during tests. We conclude that the automated protocol described here is reliable and provides consistent behavioral analysis in mice. This automated protocol could lead to deeper insight into behavioral neuroscience.

ContrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals

In recent years, supervised machine learning models trained on videos of animals with pose estimation data and behavior labels have been used for automated behavior classification. Applications include, for example, automated detection of neurological diseases in animal models. However, we identify two potential problems of such supervised learning approach. First, such models require a large amount of labeled data but the labeling of behaviors frame by frame is a laborious manual process that is not easily scalable. Second, such methods rely on handcrafted features obtained from pose estimation data that are usually designed empirically. In this paper, we propose to overcome these two problems using contrastive learning for self-supervised feature engineering on pose estimation data. Our approach allows the use of unlabeled videos to learn feature representations and reduce the need for handcrafting of higher-level features from pose positions. We show that this approach to feature representation can achieve better classification performance compared to handcrafted features alone, and that the performance improvement is due to contrastive learning on unlabeled data rather than the neural network architecture. The method has the potential to reduce the bottleneck of scarce labeled videos for training and improve performance of supervised behavioral classification models for the study of interaction behaviors in animals.

Rodent models for mood disorders - understanding molecular changes by investigating social behavior

Mood disorders, including depressive and bipolar disorders, are the group of psychiatric disorders with the highest prevalence and disease burden. However, their pathophysiology remains poorly understood. Animal models are an extremely useful tool for the investigation of molecular mechanisms underlying these disorders. For psychiatric symptom assessment in animals, a meaningful behavioral phenotype is needed. Social behaviors constitute naturally occurring complex behaviors in rodents and can therefore serve as such a phenotype, contributing to insights into disorder related molecular changes. In this narrative review, we give a fundamental overview of social behaviors in laboratory rodents, as well as their underlying neuronal mechanisms and their assessment. Relevant behavioral and molecular changes in models for mood disorders are presented and an outlook on promising future directions is given.

3D mouse pose from single-view video and a new dataset

We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health, as demonstrated by its use in successfully classifying animals based on age and genotype. We introduce the Mouse Pose Analysis Dataset, the first large scale video dataset of lab mice in their home cage with ground truth keypoint and behavior labels. The dataset also contains high resolution mouse CT scans, which we use to build the shape models for 3D pose reconstruction.

An automated, low-latency environment for studying the neural basis of behavior in freely moving rats

BACKGROUND

Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. Brain activity varies at sub-second time scales, but behavioral measures are usually coarse (often consisting of only binary trial outcomes).

RESULTS

To overcome this mismatch, we developed the Rat Interactive Foraging Facility (RIFF): a programmable interactive arena for freely moving rats with multiple feeding areas, multiple sound sources, high-resolution behavioral tracking, and simultaneous electrophysiological recordings. The paper provides detailed information about the construction of the RIFF and the software used to control it. To illustrate the flexibility of the RIFF, we describe two complex tasks implemented in the RIFF, a foraging task and a sound localization task. Rats quickly learned to obtain rewards in both tasks. Neurons in the auditory cortex as well as neurons in the auditory field in the posterior insula had sound-driven activity during behavior. Remarkably, neurons in both structures also showed sensitivity to non-auditory parameters such as location in the arena and head-to-body angle.

CONCLUSIONS

The RIFF provides insights into the cognitive capabilities and learning mechanisms of rats and opens the way to a better understanding of how brains control behavior. The ability to do so depends crucially on the combination of wireless electrophysiology and detailed behavioral documentation available in the RIFF.

From rodents to humans: Rodent behavioral paradigms for social behavioral disorders

Social cognition guides social behavior. Subjects with proper social cognition should be able to: (1) have reasonable social motivation, (2) recognize other people and infer their intentions, and (3) weigh social hierarchies and other values. The choice of appropriate behavioral paradigms enables the use of rodents to study social behavior disorders in humans, thus enabling research to go deeper into neural mechanisms. This paper reviews commonly used rodent behavioral paradigms in studies of social behavior disorders. We focused specifically on sorting out ways to transfer the study of human social behavior to rodents through behavioral paradigms.

Schrödinger Dynamics and Berry Phase of Undulatory Locomotion

Spectral mode representations play an essential role in various areas of physics, from quantum mechanics to fluid turbulence, but they are not yet extensively used to characterize and describe the behavioral dynamics of living systems. Here, we show that mode-based linear models inferred from experimental live-imaging data can provide an accurate low-dimensional description of undulatory locomotion in worms, centipedes, robots, and snakes. By incorporating physical symmetries and known biological constraints into the dynamical model, we find that the shape dynamics are generically governed by Schrödinger equations in mode space. The eigenstates of the effective biophysical Hamiltonians and their adiabatic variations enable the efficient classification and differentiation of locomotion behaviors in natural, simulated, and robotic organisms using Grassmann distances and Berry phases. While our analysis focuses on a widely studied class of biophysical locomotion phenomena, the underlying approach generalizes to other physical or living systems that permit a mode representation subject to geometric shape constraints.

Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) can be invasive and may have an impact on the social behavior being measured. To overcome these shortcomings, video-based methods for tracking unmarked animals, such as fruit flies and zebrafish, have been developed. However, tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns. In this study, we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network (R-CNN) deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology. The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior. The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

Periodic hypothalamic attractor-like dynamics during the estrus cycle

Cyclic changes in hormonal state are well-known to regulate mating behavior during the female reproductive cycle, but whether and how these changes affect the dynamics of neural activity in the female brain is largely unknown. The ventromedial hypothalamus, ventro-lateral subdivision (VMHvl) contains a subpopulation of VMHvl Esr1+,Npy2r- neurons that controls female sexual receptivity. Longitudinal single cell calcium imaging of these neurons across the estrus cycle revealed that overlapping but distinct subpopulations were active during proestrus (mating-accepting) vs. non-proestrus (rejecting) phases. Dynamical systems analysis of imaging data from proestrus females uncovered a dimension with slow ramping activity, which generated approximate line attractor-like dynamics in neural state space. During mating, the neural population vector progressed along this attractor as male mounting and intromission proceeded. Attractor-like dynamics disappeared in non-proestrus states and reappeared following re-entry into proestrus. They were also absent in ovariectomized females but were restored by hormone priming. These observations reveal that hypothalamic line attractor-like dynamics are associated with female sexual receptivity and can be reversibly regulated by sex hormones, demonstrating that attractor dynamics can be flexibly modulated by physiological state. They also suggest a potential mechanism for the neural encoding of female sexual arousal.

Ontogeny of collective behaviour

During their lifetime, superorganisms, like unitary organisms, undergo transformations that change the machinery of their collective behaviour. Here, we suggest that these transformations are largely understudied and propose that more systematic research into the ontogeny of collective behaviours is needed if we hope to better understand the link between proximate behavioural mechanisms and the development of collective adaptive functions. In particular, certain social insects engage in self-assemblage, forming dynamic and physically connected architectures with striking similarities to developing multicellular organisms, making them good model systems for ontogenetic studies of collective behaviour. However, exhaustive time series and three-dimensional data are required to thoroughly characterize the different life stages of the collective structures and the transitions between these stages. The well-established fields of embryology and developmental biology offer practical tools and theoretical frameworks that could speed up the acquisition of new knowledge about the formation, development, maturity and dissolution of social insect self-assemblages and, by extension, other superorganismal behaviours. We hope that this review will encourage an expansion of the ontogenetic perspective in the field of collective behaviour and, in particular, in self-assemblage research, which has far-reaching applications in robotics, computer science and regenerative medicine. This article is part of a discussion meeting issue 'Collective behaviour through time'.

A paradigm shift in translational psychiatry through rodent neuroethology

Mental disorders are a significant cause of disability worldwide. They profoundly affect individuals' well-being and impose a substantial financial burden on societies and governments. However, despite decades of extensive research, the effectiveness of current therapeutics for mental disorders is often not satisfactory or well tolerated by the patient. Moreover, most novel therapeutic candidates fail in clinical testing during the most expensive phases (II and III), which results in the withdrawal of pharma companies from investing in the field. It also brings into question the effectiveness of using animal models in preclinical studies to discover new therapeutic agents and predict their potential for treating mental illnesses in humans. Here, we focus on rodents as animal models and propose that they are essential for preclinical investigations of candidate therapeutic agents' mechanisms of action and for testing their safety and efficiency. Nevertheless, we argue that there is a need for a paradigm shift in the methodologies used to measure animal behavior in laboratory settings. Specifically, behavioral readouts obtained from short, highly controlled tests in impoverished environments and social contexts as proxies for complex human behavioral disorders might be of limited face validity. Conversely, animal models that are monitored in more naturalistic environments over long periods display complex and ethologically relevant behaviors that reflect evolutionarily conserved endophenotypes of translational value. We present how semi-natural setups in which groups of mice are individually tagged, and video recorded continuously can be attainable and affordable. Moreover, novel open-source machine-learning techniques for pose estimation enable continuous and automatic tracking of individual body parts in groups of rodents over long periods. The trajectories of each individual animal can further be subjected to supervised machine learning algorithms for automatic detection of specific behaviors (e.g., chasing, biting, or fleeing) or unsupervised automatic detection of behavioral motifs (e.g., stereotypical movements that might be harder to name or label manually). Compared to studies of animals in the wild, semi-natural environments are more compatible with neural and genetic manipulation techniques. As such, they can be used to study the neurobiological mechanisms underlying naturalistic behavior. Hence, we suggest that such a paradigm possesses the best out of classical ethology and the reductive behaviorist approach and may provide a breakthrough in discovering new efficient therapies for mental illnesses.

Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality

It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantification of such effects have proven difficult. We developed a scalable social behavioral assay for zebrafish named ZeChat based on unsupervised deep learning to characterize sociality at high resolution. High-dimensional and dynamic social behavioral phenotypes are automatically classified using this method. By screening a neuroactive compound library, we found that different classes of chemicals evoke distinct patterns of social behavioral fingerprints. By examining these patterns, we discovered that dopamine D3 agonists possess a social stimulative effect on zebrafish. The D3 agonists pramipexole, piribedil, and 7-hydroxy-DPAT-HBr rescued social deficits in a valproic-acid-induced zebrafish autism model. The ZeChat platform provides a promising approach for dissecting the pharmacology of social behavior and discovering novel social-modulatory compounds.

A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior

Accurate and efficient quantification of animal behavior facilitates the understanding of the brain. An emerging approach within machine learning (ML) field is to combine multiple ML-based algorithms to quantify animal behavior. These so-called hybrid models have emerged because of limitations associated with supervised [e.g., random forest (RF)] and unsupervised [e.g., hidden Markov model (HMM)] ML models. For example, RF models lack temporal information across video frames, and HMM latent states are often difficult to interpret. We sought to develop a hybrid model, and did so in the context of a study of mouse risk assessment behavior. We used DeepLabCut to estimate the positions of mouse body parts. Positional features were calculated using DeepLabCut outputs and were used to train RF and HMM models with equal number of states, separately. The per-frame predictions from RF and HMM models were then passed to a second HMM model layer ("reHMM"). The outputs of the reHMM layer showed improved interpretability over the initial HMM output. Finally, we combined predictions from RF and HMM models with selected positional features to train a third HMM model ("reHMM+"). This reHMM+ layered hybrid model unveiled distinctive temporal and human-interpretable behavioral patterns. We applied this workflow to investigate risk assessment to trimethylthiazoline and snake feces odor, finding unique behavioral patterns to each that were separable from attractive and neutral stimuli. We conclude that this layered, hybrid ML workflow represents a balanced approach for improving the depth and reliability of ML classifiers in chemosensory and other behavioral contexts.

Probing the antidepressant potential of psilocybin: integrating insight from human research and animal models towards an understanding of neural circuit mechanisms

Interest in the therapeutic potential of serotonergic psychedelic compounds including psilocybin has surged in recent years. While human clinical research suggests psilocybin holds promise as a rapid and long-lasting antidepressant, little is known about how its acute mechanisms of action mediate enduring alterations in cognition and behavior. Human neuroimaging studies point to both acute and sustained modulation of functional connectivity in key cortically dependent brain networks. Emerging evidence in preclinical models highlights the importance of psilocybin-induced neuroplasticity and alterations in the prefrontal cortex (PFC). Overviewing research in both humans and preclinical models suggests avenues to increase crosstalk between fields. We review how acute modulation of PFC circuits may contribute to long-term structural and functional alterations to mediate antidepressant effects. We highlight the potential for preclinical circuit and behavioral neuroscience approaches to provide basic mechanistic insight into how psilocybin modulates cognitive and affective neural circuits to support further development of psilocybin as a promising new treatment for depression.

Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction

Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results. Yet there is no effort that investigates across different pose representation schemes. We conduct an indepth study on various pose representations with a focus on their effects on the motion prediction task. Moreover, recent approaches build upon off-the-shelf RNN units for motion prediction. These approaches process input pose sequence sequentially and inherently have difficulties in capturing long-term dependencies. In this paper, we propose a novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction which simultaneously models local motion contexts and a global context. We further explore a geodesic loss and a forward kinematics loss for the motion prediction task, which have more geometric significance than the widely employed L2 loss. Interestingly, we applied our method to a range of articulate objects including human, fish, and mouse. Empirical results show that our approach outperforms the state-of-the-art methods in short-term prediction and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions. Our codes are released.

Treadmill Exercise as a Preventive Measure Against Age-Related Anxiety and Social Behavioral Disorders in Rats: When Is It Worth Starting?

OBJECTIVE

To determine the appropriate time points to start regular exercise which could reduce age-related anxiety and impaired social behavior.

METHODS

For this study, 8-week-old male Wistar rats were divided into three groups: no exercise (NoEX), short-term exercise (S-Ex), and long-term exercise (L-Ex) groups. S-Ex-group rats started treadmill exercise at 12 months of age, while L-Ex rats started from at 2 months of age. Exercise rats were forced to walk on the treadmill three times per week, with 1- to 2-day intervals for 10 minutes during the first 2 weeks, at 10 m/min until 17 months of age, and at 8 m/min thereafter. At 19 months of age, behavioral tests were performed to assess the effects of exercise on age-induced behavioral change as well as quantitative polymerase chain reaction were done to uncover the mechanism behind the behavioral changes.

RESULTS

Anxiety-like behavior was improved by long-term exercise. Additionally, rats belonging to the S-Ex and L-Ex groups showed improved social behavior and increased curiosity about interesting objects. The qPCR data showed that treadmill exercise suppressed the expression of immediate-early genes in the prefrontal cortex of the aged rats.

CONCLUSION

This study suggests that long-term exercise represses early response genes, and in this way, it increases resistance to stress, diminishes anxiety-related behavior, and improves social behavior. These findings underscore the need to consider appropriate time to start exercise to prevent stress induced anxiety related behavior.

CA2 physiology underlying social memory

In recent years, convergent evidence has emerged in support of the idea of social brain networks, specific brain regions that are interconnected and support social behaviors. One of these regions is the CA2 area of the hippocampus, a small region strongly connected with cortical and subcortical areas implicated in social behaviors. Furthermore, CA2 area is enriched in receptors for several neuromodulators that are related to various aspects of social behaviors, suggesting that this area could be a key component of social information processing in the brain. In this review, recent findings related to the physiological mechanisms underlying the role of CA2 in social memory are discussed.

Beyond the three-chamber test: toward a multimodal and objective assessment of social behavior in rodents

MAIN: In recent years, substantial advances in social neuroscience have been realized, including the generation of numerous rodent models of autism spectrum disorder. Still, it can be argued that those methods currently being used to analyze animal social behavior create a bottleneck that significantly slows down progress in this field. Indeed, the bulk of research still relies on a small number of simple behavioral paradigms, the results of which are assessed without considering behavioral dynamics. Moreover, only few variables are examined in each paradigm, thus overlooking a significant portion of the complexity that characterizes social interaction between two conspecifics, subsequently hindering our understanding of the neural mechanisms governing different aspects of social behavior. We further demonstrate these constraints by discussing the most commonly used paradigm for assessing rodent social behavior, the three-chamber test. We also point to the fact that although emotions greatly influence human social behavior, we lack reliable means for assessing the emotional state of animals during social tasks. As such, we also discuss current evidence supporting the existence of pro-social emotions and emotional cognition in animal models. We further suggest that adequate social behavior analysis requires a novel multimodal approach that employs automated and simultaneous measurements of multiple behavioral and physiological variables at high temporal resolution in socially interacting animals. We accordingly describe several computerized systems and computational tools for acquiring and analyzing such measurements. Finally, we address several behavioral and physiological variables that can be used to assess socio-emotional states in animal models and thus elucidate intricacies of social behavior so as to attain deeper insight into the brain mechanisms that mediate such behaviors. CONCLUSIONS: In summary, we suggest that combining automated multimodal measurements with machine-learning algorithms will help define socio-emotional states and determine their dynamics during various types of social tasks, thus enabling a more thorough understanding of the complexity of social behavior.

Using Machine Learning to Identify Associations between the Environment, Occurrence, and Outcomes of Songbird Displacements at Supplemental Feeders

The context and outcome of aggressive interactions between individuals has important fitness consequences. Displacements—an aggressive interaction wherein one individual is chased from a location by another—also have implications for social hierarchy formation and geographic distribution in songbirds. Morphological correlates, like body size, and social correlates, such as dominance rank, have been shown to mediate displacements in songbirds. However, the role of the physical environment, namely temperature, humidity, and time of day, which may influence an individual’s energy needs and thus displacement motivation, has remained understudied. We monitored songbird feeding and displacement behaviors using computerized automated feeders. We observed asymmetric differences across species in displacement involvement. To identify the conditions of the social and physical environment that are associated with the occurrence and outcome of songbird displacements at supplemental feeders, we use the machine learning approach, random forest, which is a novel method to the fields of ornithology and animal behavior. From our random forest models, we found that the attributes of the physical environment (i.e., humidity and the time of day) are associated with the occurrence of a displacement event, whereas the attributes of the social environment (i.e., species of the displacer and displaced individuals) are associated with which species are involved. These results provide context to develop further observational and experimental hypotheses to tease apart the inner workings of these multifactorial behaviors on a larger scale and provide a proof of concept for our analytical methods in the study of avian behavior.

Nanoformulation improves antitumor efficacy of MAOI immune checkpoint blockade therapy without causing aggression-related side effects

MAOIs, a well-established class of antidepressant that operate through the inhibition of monoamine oxidase to increase available serotonin, have recently been identified as a surprisingly effective candidate for the circumvention of tumor-induced immune suppression due to their abilities to enhance antitumor T cell activity through autocrine serotonin signaling and depolarize alternatively activated tumor-associated macrophages through a reduction in reactive oxygen species production. However, this impressive class of antidepressants-turned-cancer-drugs can induce aggressive behavioral side effects when administered in immunotherapeutic doses. In this study, we investigated the possibility of avoiding these neurological side effects while simultaneously improving antitumor activity by establishing crosslinked multilamellar liposomal vesicles (cMLVs) containing the MAOI phenelzine (PLZ). Our results showed that cMLV-PLZ treatment increases antitumor efficacy in a B16-OVA mouse melanoma model compared to treatment with free phenelzine. We also found that nanoformulation resulted in the complete elimination of MAOI-related aggression. These findings suggest a promising direction for the future of MAOIs repurposed for cancer immunotherapies.

Neural mechanisms underlying the temporal organization of naturalistic animal behavior

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.

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.

Leaving flatland: Advances in 3D behavioral measurement

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

Automatization and technological advances have led to a larger number of methods and systems to monitor and measure locomotor activity and more specific behavior of a wide variety of animal species in various environmental conditions in laboratory settings. In rodents, the majority of these systems require the animals to be temporarily taken away from their home-cage into separate observation cage environments which requires manual handling and consequently evokes distress for the animal and may alter behavioral responses. An automated high-throughput approach can overcome this problem. Therefore, this review describes existing automated methods and technologies which enable the measurement of locomotor activity and behavioral aspects of rodents in their most meaningful and stress-free laboratory environment: the home-cage. In line with the Directive 2010/63/EU and the 3R principles (replacement, reduction, refinement), this review furthermore assesses their suitability and potential for group-housed conditions as a refinement strategy, highlighting their current technological and practical limitations. It covers electrical capacitance technology and radio-frequency identification (RFID), which focus mainly on voluntary locomotor activity in both single and multiple rodents, respectively. Infrared beams and force plates expand the detection beyond locomotor activity toward basic behavioral traits but discover their full potential in individually housed rodents only. Despite the great premises of these approaches in terms of behavioral pattern recognition, more sophisticated methods, such as (RFID-assisted) video tracking technology need to be applied to enable the automated analysis of advanced behavioral aspects of individual animals in social housing conditions.

Critical Problems for Research in Animal Sheltering, a Conceptual Analysis

Animal shelter research has seen significant increases in participation over the past several decades from academic organizations, private organizations, public entities, and even corporations that aims to improve shelter programs, processes, operations, and outcomes for the various stakeholders/participants involved in a shelter system (animals, humans, the community, wildlife, and the environment). These efforts are scattered through a huge variety of different research areas that are challenging to define and scope for organizations seeking to start new lines of research inquiry. This work aims to enumerate some of the most critical outstanding problems for research in animal sheltering in a conceptual framework that is intended to help direct research conversations toward the research topics of highest impact (with the highest quality outcomes possible). To this end, we define seven (7) key areas for research: animal behavior, adoptions and special needs populations, medical conditions, disease transmission, community, ecology, and wellness (one health), operations, and public-private-academic-corporate collaboration. Within each of these areas, we review specific problems and highlight examples of successes in each area in the past several decades. We close with a discussion of some of the topics that were not detailed in this manuscript but, nonetheless, deserve some mention. Through this enumeration, we hope to spur conversation around innovative methodologies, technologies, and concepts in both research and practice in animal sheltering.

Make war not love: The neural substrate underlying a state-dependent switch in female social behavior

Female mice exhibit opposing social behaviors toward males depending on their reproductive state: virgins display sexual receptivity (lordosis behavior), while lactating mothers attack. How a change in reproductive state produces a qualitative switch in behavioral response to the same conspecific stimulus is unknown. Using single-cell RNA-seq, we identify two distinct subtypes of estrogen receptor-1-positive neurons in the ventrolateral subdivision of the female ventromedial hypothalamus (VMHvl) and demonstrate that they causally control sexual receptivity and aggressiveness in virgins and lactating mothers, respectively. Between- and within-subject bulk-calcium recordings from each subtype reveal that aggression-specific cells acquire an increased responsiveness to social cues during the transition from virginity to maternity, while the responsiveness of the mating-specific population appears unchanged. These results demonstrate that reproductive-state-dependent changes in the relative activity of transcriptomically distinct neural subtypes can underlie categorical switches in behavior associated with physiological state changes.

Intelligent high-throughput intervention testing platform in Daphnia

We present a novel platform for testing the effects of interventions on the life- and healthspan of a short-lived freshwater organism with complex behavior and physiology-the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine-learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using a long-term dose-response profile of metformin and a short-term assay of well-studied substances such as caffeine and alcohol.

MouseVenue3D: A Markerless Three-Dimension Behavioral Tracking System for Matching Two-Photon Brain Imaging in Free-Moving Mice

Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional (3-D) space. However, there is no available three-dimensional behavior capture system that focuses on rodents. Here, we present MouseVenue3D, an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless rodents. We improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration module. Then, we validated this process in behavior recognition tasks, and showed that 3-D behavioral data achieved higher accuracy than 2-D data. Subsequently, MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving mouse. Finally, we successfully decoded spontaneous neuronal activity from the 3-D behavior of mice. Our findings reveal that subtle, spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.

Bayesian Multi-Targets Strategy to Track Apis mellifera Movements at Colony Level

Simple Summary

The number of honey bee, Apis mellifera L., colonies has reduced around the globe, and one potential cause is their unintended exposure to sublethal stressors such as agricultural pesticides. The quantification of such effects at colony level is a very complex task due to the innumerable collective activities done by the individual within colonies. Here, we present a Bayesian and computational approach capable of tracking the movements of bees within colonies, which allows the comparison of the collective activities of colonies that received bees previously exposed to uncontaminated diets or to diets containing sublethal concentrations of an agricultural pesticide (a commercial formulation containing the synthetic fungicides thiophanate-methyl and chlorothalonil). Our Bayesian tracking technique proved successful and superior to comparable algorithms, allowing the estimation of dynamical parameters such as entropy and kinetic energy. Our efforts demonstrated that fungicide-contaminated colonies behaved differently from uncontaminated colonies, as the former exhibited anticipated collective activities in peripheral hive areas and had reduced swarm entropy and kinetic energies. Such findings may facilitate the electronic monitoring of potential unintended effects in social pollinators, at colony level, mediated by environmental stressors (e.g., pesticides, electromagnetic fields, noise, and light intensities) alone or in combination.

Abstract

Interactive movements of bees facilitate the division and organization of collective tasks, notably when they need to face internal or external environmental challenges. Here, we present a Bayesian and computational approach to track the movement of several honey bee, Apis mellifera, workers at colony level. We applied algorithms that combined tracking and Kernel Density Estimation (KDE), allowing measurements of entropy and Probability Distribution Function (PDF) of the motion of tracked organisms. We placed approximately 200 recently emerged and labeled bees inside an experimental colony, which consists of a mated queen, approximately 1000 bees, and a naturally occurring beehive background. Before release, labeled bees were fed for one hour with uncontaminated diets or diets containing a commercial mixture of synthetic fungicides (thiophanate-methyl and chlorothalonil). The colonies were filmed (12 min) at the 1st hour, 5th and 10th days after the bees’ release. Our results revealed that the algorithm tracked the labeled bees with great accuracy. Pesticide-contaminated colonies showed anticipated collective activities in peripheral hive areas, far from the brood area, and exhibited reduced swarm entropy and energy values when compared to uncontaminated colonies. Collectively, our approach opens novel possibilities to quantify and predict potential alterations mediated by pollutants (e.g., pesticides) at the bee colony-level.

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system ("3DDD Social Mouse Tracker") is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A statistical model that relates 3D behavior and neural activity reveals multiplexed 'social receptive fields' of neurons in barrel cortex. Our approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments.

Automated procedure to assess pup retrieval in laboratory mice

All mammalian mothers form some sort of caring bond with their infants that is crucial to the development of their offspring. The Pup Retrieval Test (PRT) is the leading procedure to assess pup-directed maternal care in laboratory rodents, used in a wide range of basic and preclinical research applications. Most PRT protocols require manual scoring, which is prone to bias and spatial and temporal inaccuracies. This study proposes a novel procedure using machine learning algorithms to enable reliable assessment of PRT performance. Automated tracking of a dam and one pup was established in DeepLabCut and was combined with automated behavioral classification of "maternal approach", "carrying" and "digging" in Simple Behavioral Analysis (SimBA). Our automated procedure estimated retrieval success with an accuracy of 86.7%, whereas accuracies of "approach", "carry" and "digging" were estimated at respectively 99.3%, 98.6% and 85.0%. We provide an open-source, step-by-step protocol for automated PRT assessment, which aims to increase reproducibility and reliability, and can be easily shared and distributed.

Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning

Alarm signal propagation through ant colonies provides an empirically tractable context for analysing information flow through a natural system, with useful insights for network dynamics in other social animals. Here, we develop a methodological approach to track alarm spread within a group of harvester ants, Pogonomyrmex californicus. We initially alarmed three ants and tracked subsequent signal transmission through the colony. Because there was no actual standing threat, the false alarm allowed us to assess amplification and adaptive damping of the collective alarm response. We trained a random forest regression model to quantify alarm behaviour of individual workers from multiple movement features. Our approach translates subjective categorical alarm scores into a reliable, continuous variable. We combined these assessments with automatically tracked proximity data to construct an alarm propagation network. This method enables analyses of spatio-temporal patterns in alarm signal propagation in a group of ants and provides an opportunity to integrate individual and collective alarm response. Using this system, alarm propagation can be manipulated and assessed to ask and answer a wide range of questions related to information and misinformation flow in social networks.