Computerized video analysis of social interactions in mice

Quantifying social roles in multi-animal videos using subject-aware deep-learning

Analyzing social behaviors is critical for many fields, including neuroscience, psychology, and ecology. While computational tools have been developed to analyze videos containing animals engaging in limited social interactions under specific experimental conditions, automated identification of the social roles of freely moving individuals in a multi-animal group remains unresolved. Here we describe a deep-learning-based system - named LabGym2 - for identifying and quantifying social roles in multi-animal groups. This system uses a subject-aware approach: it evaluates the behavioral state of every individual in a group of two or more animals while factoring in its social and environmental surroundings. We demonstrate the performance of subject-aware deep-learning in different species and assays, from partner preference in freely-moving insects to primate social interactions in the field. Our subject-aware deep learning approach provides a controllable, interpretable, and efficient framework to enable new experimental paradigms and systematic evaluation of interactive behavior in individuals identified within a group.

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.

Dominance hierarchy regulates social behavior during spatial movement

Rodents establish dominance hierarchy as a social ranking system in which one subject acts as dominant over all the other subordinate individuals. Dominance hierarchy regulates food access and mating opportunities, but little is known about its significance in other social behaviors, for instance during collective navigation for foraging or migration. Here, we implemented a simplified goal-directed spatial task in mice, in which animals navigated individually or collectively with their littermates foraging for food. We compared between conditions and found that the social condition exerts significant influence on individual displacement patterns, even when efficient navigation rules leading to reward had been previously learned. Thus, movement patterns and consequent task performance were strongly dependent on contingent social interactions arising during collective displacement, yet their influence on individual behavior was determined by dominance hierarchy. Dominant animals did not behave as leaders during collective displacement; conversely, they were most sensitive to the social environment adjusting their performance accordingly. Social ranking in turn was associated with specific spontaneous neural activity patterns in the prefrontal cortex and hippocampus, with dominant mice showing higher firing rates, larger ripple oscillations, and stronger neuronal entrainment by ripples than subordinate animals. Moreover, dominant animals selectively increased their cortical spiking activity during collective movement, while subordinate mice did not modify their firing rates, consistent with dominant animals being more sensitive to the social context. These results suggest that dominance hierarchy influences behavioral performance during contingent social interactions, likely supported by the coordinated activity in the hippocampal-prefrontal circuit.

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.

Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL

Understandings of the three-dimensional social behaviors of freely moving large-size mammals are valuable for both agriculture and life science, yet challenging due to occlusions in close interactions. Although existing animal pose estimation methods captured keypoint trajectories, they ignored deformable surfaces which contained geometric information essential for social interaction prediction and for dealing with the occlusions. In this study, we develop a Multi-Animal Mesh Model Alignment (MAMMAL) system based on an articulated surface mesh model. Our self-designed MAMMAL algorithms automatically enable us to align multi-view images into our mesh model and to capture 3D surface motions of multiple animals, which display better performance upon severe occlusions compared to traditional triangulation and allow complex social analysis. By utilizing MAMMAL, we are able to quantitatively analyze the locomotion, postures, animal-scene interactions, social interactions, as well as detailed tail motions of pigs. Furthermore, experiments on mouse and Beagle dogs demonstrate the generalizability of MAMMAL across different environments and mammal species.

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.

Graphene Sensor Arrays for Rapid and Accurate Detection of Pancreatic Cancer Exosomes in Patients' Blood Plasma Samples

Biosensors based on graphene field effect transistors (GFETs) have the potential to enable the development of point-of-care diagnostic tools for early stage disease detection. However, issues with reproducibility and manufacturing yields of graphene sensors, but also with Debye screening and unwanted detection of nonspecific species, have prevented the wider clinical use of graphene technology. Here, we demonstrate that our wafer-scalable GFETs array platform enables meaningful clinical results. As a case study of high clinical relevance, we demonstrate an accurate and robust portable GFET array biosensor platform for the detection of pancreatic ductal adenocarcinoma (PDAC) in patients' plasma through specific exosomes (GPC-1 expression) within 45 min. In order to facilitate reproducible detection in blood plasma, we optimized the analytical performance of GFET biosensors via the application of an internal control channel and the development of an optimized test protocol. Based on samples from 18 PDAC patients and 8 healthy controls, the GFET biosensor arrays could accurately discriminate between the two groups while being able to detect early cancer stages including stages 1 and 2. Furthermore, we confirmed the higher expression of GPC-1 and found that the concentration in PDAC plasma was on average more than 1 order of magnitude higher than in healthy samples. We found that these characteristics of GPC-1 cancerous exosomes are responsible for an increase in the number of target exosomes on the surface of graphene, leading to an improved signal response of the GFET biosensors. This GFET biosensor platform holds great promise for the development of an accurate tool for the rapid diagnosis of pancreatic cancer.

Defensive and offensive behaviours in a Kleefstra syndrome mouse model

Kleefstra syndrome in humans is characterized by a general delay in development, intellectual disability and autistic features. The mouse model of this disease (Ehmt1±) expresses anxiety, autistic-like traits, and aberrant social interactions with non-cagemates. To investigate how Ehmt1± mice behave with unfamiliar conspecifics, we allowed adult, male animals to freely interact for 10 min in a neutral, novel environment within a host-visitor setting. In trials where the Ehmt1± mice were hosts, there were defensive and offensive behaviors. Our key finding was that Ehmt1± mice displayed defensive postures, attacking and biting; in contrast, wild-type (WT) interacting with other WT did not enact such behaviors. Further, if there was a fight between an Ehmt1± and a WT mouse, the Ehmt1± animal was the most aggressive and always initiated these behaviors.

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.

Excessive self-grooming, gene dysregulation and imbalance between the striosome and matrix compartments in the striatum of Shank3 mutant mice

Autism is characterized by atypical social communication and stereotyped behaviors. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are detected in 1-2% of patients with autism and intellectual disability, but the mechanisms underpinning the symptoms remain largely unknown. Here, we characterized the behavior of Shank3 Δ11/Δ11 mice from 3 to 12 months of age. We observed decreased locomotor activity, increased stereotyped self-grooming and modification of socio-sexual interaction compared to wild-type littermates. We then used RNAseq on four brain regions of the same animals to identify differentially expressed genes (DEGs). DEGs were identified mainly in the striatum and were associated with synaptic transmission (e.g., Grm2, Dlgap1), G-protein-signaling pathways (e.g., Gnal, Prkcg1, and Camk2g), as well as excitation/inhibition balance (e.g., Gad2). Downregulated and upregulated genes were enriched in the gene clusters of medium-sized spiny neurons expressing the dopamine 1 (D1-MSN) and the dopamine 2 receptor (D2-MSN), respectively. Several DEGs (Cnr1, Gnal, Gad2, and Drd4) were reported as striosome markers. By studying the distribution of the glutamate decarboxylase GAD65, encoded by Gad2, we showed that the striosome compartment of Shank3 Δ11/Δ11 mice was enlarged and displayed much higher expression of GAD65 compared to wild-type mice. Altogether, these results indicate altered gene expression in the striatum of Shank3-deficient mice and strongly suggest, for the first time, that the excessive self-grooming of these mice is related to an imbalance in the striatal striosome and matrix compartments.

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.

Increasing resolution in stress neurobiology: from single cells to complex group behaviors

Stress can have severe psychological and physiological consequences. Thus, inappropriate regulation of the stress response is linked to the etiology of mood and anxiety disorders. The generation and implementation of preclinical animal models represent valuable tools to explore and characterize the mechanisms underlying the pathophysiology of stress-related psychiatric disorders and the development of novel pharmacological strategies. In this commentary, we discuss the strengths and limitations of state-of-the-art molecular and computational advances employed in stress neurobiology research, with a focus on the ever-increasing spatiotemporal resolution in cell biology and behavioral science. Finally, we share our perspective on future directions in the fields of preclinical and human stress research.

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.

Characterization of social behavior in young and middle-aged ChAT-IRES-Cre mouse

The cholinergic system is an important modulator of brain processes. It contributes to the regulation of several cognitive functions and emotional states, hence altering behaviors. Previous works showed that cholinergic (nicotinic) receptors of the prefrontal cortex are needed for adapted social behaviors. However, these data were obtained in mutant mice that also present alterations of several neurotransmitter systems, in addition to the cholinergic system. ChAT-IRES-Cre mice, that express the Cre recombinase specifically in cholinergic neurons, are useful tools to investigate the role of the cholinergic circuits in behavior. However, their own behavioral phenotype has not yet been fully characterized, in particular social behavior. In addition, the consequences of aging on the cholinergic system of ChAT-IRES-Cre mice has never been studied, despite the fact that aging is known to compromise the cholinergic system efficiency. The aim of the current study was thus to characterize the social phenotype of ChAT-IRES-Cre mice both at young (2–3 months) and middle (10–11 months) ages. Our results reveal an alteration of the cholinergic system, evidenced by a decrease of ChAT, CHT and VAChT gene expression in the striatum of the mice, that was accompanied by mild social disturbances and a tendency towards anxiety. Aging decreased social dominance, without being amplified by the cholinergic alterations. Altogether, this study shows that ChAT-IRES-Cre mice are useful models for studying the cholinergic system‘s role in social behavior using appropriate modulating technics (optogenetic or DREADD).

Blueprints for measuring natural behavior

Until recently laboratory tasks for studying behavior were highly artificial, simplified, and designed without consideration for the environmental or social context. Although such an approach offers good control over behavior, it does not allow for researching either voluntary responses or individual differences. Importantly for neuroscience studies, the activity of the neural circuits involved in producing unnatural, artificial behavior is variable and hard to predict. In addition, different ensembles may be activated depending on the strategy the animal adopts to deal with the spurious problem. Thus, artificial and simplified tasks based on responses, which do not occur spontaneously entail problems with modeling behavioral impairments and underlying brain deficits. To develop valid models of human disorders we need to test spontaneous behaviors consistently engaging well-defined, evolutionarily conserved neuronal circuits. Such research focuses on behavioral patterns relevant for surviving and thriving under varying environmental conditions, which also enable high reproducibility across different testing settings.

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Documenting a mouse's "real world" behavior in the "small world" of a laboratory cage with continuous video recordings offers insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Nevertheless, there are challenges in the design of a small world, the behavior selected for analysis, and the form of the analysis used. Here we offer insights into small world analyses by describing how acute behavioral procedures can guide continuous behavioral methodology. We show how algorithms can identify behavioral acts including walking and rearing, circadian patterns of action including sleep duration and waking activity, and the organization of patterns of movement into home base activity and excursions, and how they are altered with aging. We additionally describe how specific tests can be incorporated within a mouse's living arrangement. We emphasize how machine learning can condense and organize continuous activity that extends over extended periods of time.

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.

O mouse, where art thou? The Mouse Position Surveillance System (MoPSS)-an RFID-based tracking system

Existing methods for analysis of home cage-based preference tests are either time-consuming, not suitable for group management, expensive, and/or based on proprietary equipment that is not freely available. To correct this, we developed an automated system for group-housed mice based on radio frequency identification: the Mouse Position Surveillance System (MoPSS). The system uses an Arduino microcontroller with compatible components; it is affordable and easy to rebuild for every laboratory because it uses free and open-source software and open-source hardware with the RFID readers as the only proprietary component. The MoPSS was validated using female C57BL/6J mice and manual video comparison. It proved to be accurate even for fast-moving mice (up to 100% accuracy after logical reconstruction), and is already implemented in several studies in our laboratory. Here, we provide the complete construction description as well as the validation data and the results of an example experiment. This tracking system will allow group-based preference testing with individually identified mice to be carried out in a convenient manner. This facilitation of preference tests creates the foundation for better housing conditions from the animals' perspective.

Social Determinants of Inter-Individual Variability and Vulnerability: The Role of Dopamine

Individuals differ in their traits and preferences, which shape their interactions, their prospects for survival and their susceptibility to diseases. These correlations are well documented, yet the neurophysiological mechanisms underlying the emergence of distinct personalities and their relation to vulnerability to diseases are poorly understood. Social ties, in particular, are thought to be major modulators of personality traits and psychiatric vulnerability, yet the majority of neuroscience studies are performed on rodents in socially impoverished conditions. Rodent micro-society paradigms are therefore key experimental paradigms to understand how social life generates diversity by shaping individual traits. Dopamine circuitry is implicated at the interface between social life experiences, the expression of essential traits, and the emergence of pathologies, thus proving a possible mechanism to link these three concepts at a neuromodulatory level. Evaluating inter-individual variability in automated social testing environments shows great promise for improving our understanding of the link between social life, personality, and precision psychiatry – as well as elucidating the underlying neurophysiological mechanisms.

Brain Adaptation to Acute Stress: Effect of Time, Social Buffering, and Nicotinic Cholinergic System

Both social behavior and stress responses rely on the activity of the prefrontal cortex (PFC) and basolateral nucleus of the amygdala (BLA) and on cholinergic transmission. We previously showed in adult C57BL/6J (B6) mice that social interaction has a buffering effect on stress-related prefrontal activity, depending on the β2-/- cholinergic nicotinic receptors (nAChRs, β2-/- mice). The latency for this buffer to emerge being short, we question here whether the associated brain plasticity, as reflected by regional c-fos protein quantification and PFC-BLA functional connectivity, is modulated by time. Overall, we show that time normalized the stress-induced PFC hyperactivation in B6 mice and PFC hypo-activation in β2-/- mice, with no effect on BLA. It also triggered a multitude of functional links between PFC subareas, and between PFC and BLA in B6 mice but not β2-/- mice, showing a central role of nAChRs in this plasticity. Coupled with social interaction and time, stress led to novel and drastic diminution of functional connectivity within the PFC in both genotypes. Thus, time, emotional state, and social behavior induced dissociated effects on PFC and BLA activity and important cortico-cortical reorganizations. Both activity and plasticity were under the control of the β2-nAChRs.

The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice

The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation-a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.

Environmental exposure to 17β-trenbolone during adolescence inhibits social interaction in male mice

Puberty is a critical period for growth and development. This period is sensitive to external stimuli, which ultimately affects the development of nerves and the formation of social behaviour. 17β-Trenbolone (17β-TBOH) is an endocrine disrupting chemicals (EDCs), which had been widely reported in aquatic vertebrates. But there is little known about the effects of 17β-TBOH on mammals, especially on adolescent neurodevelopment. In this study, we found that 17β-TBOH acute 1 h exposure can cause the activation of the dopamine circuit in pubertal male balb/c mice. At present, there is little known about the effects of puberty exposure of endocrine disruptors on these neurons/nerve pathways. Through a series of behavioural tests, exposure to 80 μgkg^-1 d^-1 of 17β-TBOH during adolescence increased the anxiety-like behaviour of mice and reduced the control of wheel-running behaviour and the response of social interaction behaviour. The results of TH immunofluorescence staining showed that exposure to 17β-TBOH reduced dopamine axon growth in the medial prefrontal cortex (mPFC). In addition, the results of real-time PCR showed that exposure to 17β-TBOH not only down-regulated the expression of dopamine axon development genes, but also affected the balance of excitatory/inhibitory signals in mPFC. In this research, we reveal the effects of 17β-TBOH exposure during adolescence on mammalian behaviour and neurodevelopment, and provide a reference for studying the origin of adolescent diseases.

Prevalence and Photobiology of Photosynthetic Dinoflagellate Endosymbionts in the Nudibranch Berghia stephanieae

Simple Summary

Some sea slugs have evolved highly specialized feeding habits and solely prey upon a reduced number of species. This is the case of Berghia stephanieae, a sea slug that feeds exclusively on glass anemones, such as Exaiptasia diaphana. Glass anemones host photosynthetic microalgae that B. stephanieae ingest when preying upon E. diaphana. The association between these photosynthetic microalgae and sea slugs appears to be limited in time, particularly if B. stephanieae is deprived of prey hosting these microalgae. In the present study, we validate the use of a non-invasive and non-destructive approach that allows monitoring the persistence of this association in live sea slugs by measuring chlorophyll fluorescence. A complete loss of photosynthetic microalgae was observed within 8 days when animals were deprived of food or fed glass anemones with no microalgae (bleached anemones). As such, the association between B. stephanieae and photosynthetic microalgae acquired when preying glass anemones is not a true symbiosis. Future studies may use the technique here described to monitor the prevalence of the association between sea slugs and photosynthetic microalgae, particularly under bleaching events that will impair sea slugs to acquire microalgae by preying upon their invertebrate hosts.

Abstract

Berghia stephanieae is a stenophagous sea slug that preys upon glass anemones, such as Exaiptasia diaphana. Glass anemones host photosynthetic dinoflagellate endosymbionts that sea slugs ingest when consuming E. diaphana. However, the prevalence of these photosynthetic dinoflagellate endosymbionts in sea slugs appears to be short-lived, particularly if B.stephanieae is deprived of prey that host these microalgae (e.g., during bleaching events impacting glass anemones). In the present study, we investigated this scenario, along with food deprivation, and validated the use of a non-invasive and non-destructive approach employing chlorophyll fluorescence as a proxy to monitor the persistence of the association between sea slugs and endosymbiotic photosynthetic dinoflagellates acquired through the consumption of glass anemones. Berghia stephanieae deprived of a trophic source hosting photosynthetic dinoflagellate endosymbionts (e.g., through food deprivation or by feeding on bleached E. diaphana) showed a rapid decrease in minimum fluorescence (F_o) and photosynthetic efficiency (F_v/F_m) when compared to sea slugs fed with symbiotic anemones. A complete loss of endosymbionts was observed within 8 days, confirming that no true symbiotic association was established. The present work opens a new window of opportunity to rapidly monitor in vivo and over time the prevalence of associations between sea slugs and photosynthetic dinoflagellate endosymbionts, particularly during bleaching events that prevent sea slugs from incorporating new microalgae through trophic interactions.

Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model

Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such analysis. Because of the potential to create rich descriptions for mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. However, identifying social behaviours from various views is still challenging due to the lack of correspondence across data sources. To address this problem, we here propose a novel multi-view latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views. Furthermore, a novel multi-view latent-attention variational autoencoder model is introduced in learning the acquired features, enabling us to learn discriminative features in each view. Experimental results on the standard CRMI13 and our multi-view Parkinson's Disease Mouse Behaviour (PDMB) datasets demonstrate that our proposed model outperforms the other state of the arts technologies, has lower computational cost than the other graphical models and effectively deals with the imbalanced data problem.

Improved 3D tracking and automated classification of rodents' behavioral activity using depth-sensing cameras

Analysis of rodents' behavior/activity is of fundamental importance in many research fields. However, many behavioral experiments still rely on manual scoring, with obvious problems in reproducibility. Despite important advances in video-analysis systems and computational ethology, automated behavior quantification is still a challenge. The need for large training datasets, background stability requirements, and reduction to two-dimensional analysis (impairing full posture characterization), limit their use. Here we present a novel integrated solution for behavioral analysis of individual rats, combining video segmentation, tracking of body parts, and automated classification of behaviors, using machine learning and computer vision methods. Low-cost depth cameras (RGB-D) are used to enable three-dimensional tracking and classification in dark conditions and absence of color contrast. Our solution automatically tracks five anatomical landmarks in dynamic environments and recognizes seven distinct behaviors, within the accuracy range of human annotations. The developed free software was validated in experiments where behavioral differences between Wistar Kyoto and Wistar rats were automatically quantified. The results reveal the capability for effective automated phenotyping. An extended annotated RGB-D dataset is also made publicly available. The proposed solution is an easy-to-use tool, with low-cost setup and powerful 3D segmentation methods (in static/dynamic environments). The ability to work in dark conditions means that natural animal behavior is not affected by recording lights. Furthermore, automated classification is possible with only ~30 minutes of annotated videos. By creating conditions for high-throughput analysis and reproducible quantitative measurements of animal behavior experiments, we believe this contribution can greatly improve behavioral analysis research.

epiTracker: A Framework for Highly Reliable Particle Tracking for the Quantitative Analysis of Fish Movements in Tanks

Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs an error-free segmentation of individual tracks throughout time. While automated tracking algorithms exist that are quick and easy to use, inevitable errors will occur during tracking. To solve this problem, we introduce a robust algorithm called epiTracker for segmentation and tracking of multiple animals in two-dimensional (2D) videos along with an easy-to-use correction method that allows one to obtain error-free segmentation. We have implemented two graphical user interfaces to allow user-friendly control of the functions. Using six labeled 2D datasets, the effort to obtain accurate labels is quantified and compared to alternative available software solutions. Both the labeled datasets and the software are publicly available.

Effects of water restriction on social behavior and 5-HT neurons density in the dorsal and median raphe nuclei in mice

We explored here the hypothesis that temporary chronic water restriction in mice affects social behavior, via its action on the density of 5-HT neurons in dorsal and median raphe nuclei (DRN and MRN). For that, we submitted adult C57BL/6 J mice to mild and controlled temporary dehydration, i.e., 6 h of water access every 48 h for 15 days. We investigated their social behavior in a social interaction task known to allow free and reciprocal social contact. Results showed that temporary dehydration increases significantly time spent in social contact and social dominance. It also expands 5-HT neuron density within both DRN and MRN and the behavioral and neuronal plasticity were positively correlated. Our findings suggest that disturbance in 5-HT neurotransmission caused by temporary dehydration stress unbalances choice processes of animals in social context.

Markerless Rat Behavior Quantification With Cascade Neural Network

Quantifying rat behavior through video surveillance is crucial for medicine, neuroscience, and other fields. In this paper, we focus on the challenging problem of estimating landmark points, such as the rat's eyes and joints, only with image processing and quantify the motion behavior of the rat. Firstly, we placed the rat on a special running machine and used a high frame rate camera to capture its motion. Secondly, we designed the cascade convolution network (CCN) and cascade hourglass network (CHN), which are two structures to extract features of the images. Three coordinate calculation methods—fully connected regression (FCR), heatmap maximum position (HMP), and heatmap integral regression (HIR)—were used to locate the coordinates of the landmark points. Thirdly, through a strict normalized evaluation criterion, we analyzed the accuracy of the different structures and coordinate calculation methods for rat landmark point estimation in various feature map sizes. The results demonstrated that the CCN structure with the HIR method achieved the highest estimation accuracy of 75%, which is sufficient to accurately track and quantify rat joint motion.

Assessment of nest building and social interaction behavior in mice exposed to acute social defeat stress using a three-dimensional depth camera

Nest building is an instinctive behavior toward protection from predators, body temperature regulation, and courtship. Previously, we discovered that acute and chronic social defeat stress suppresses the onset of nest-building behavior in male mice (C57BL/6J). Here, we analyzed nest building and other behavioral deficits induced by acute social defeat stress (ASDS). We utilized a customized cage and specifically developed observational programs for nest building, social avoidance, and other behaviors using an infrared depth camera to acquire three-dimensional (3D) data of animal behavior (Negura system). We determined the volume of nesting materials from these 3D depth images. Mice exposed to ASDS showed increased spontaneous activities, decreased rearing, and delayed nest building; however, nest-building activity was gradually recovered during the dark period of the 24 hr observation interval. At the endpoint following 24 hr, the ASDS and control groups showed no differences in nest volumes. Furthermore, we observed the time courses of both nest building and social avoidance behaviors and their relationship using the Negura system. Our data demonstrated a weak positive correlation between nest-building delay and social avoidance in ASDS mice. The Negura system can observe various behaviors that reflect the effects of social defeat stress.

EthoLoop: automated closed-loop neuroethology in naturalistic environments

Accurate tracking and analysis of animal behavior is crucial for modern systems neuroscience. However, following freely moving animals in naturalistic, three-dimensional (3D) or nocturnal environments remains a major challenge. Here, we present EthoLoop, a framework for studying the neuroethology of freely roaming animals. Combining real-time optical tracking and behavioral analysis with remote-controlled stimulus-reward boxes, this system allows direct interactions with animals in their habitat. EthoLoop continuously provides close-up views of the tracked individuals and thus allows high-resolution behavioral analysis using deep-learning methods. The behaviors detected on the fly can be automatically reinforced either by classical conditioning or by optogenetic stimulation via wirelessly controlled portable devices. Finally, by combining 3D tracking with wireless neurophysiology we demonstrate the existence of place-cell-like activity in the hippocampus of freely moving primates. Taken together, we show that the EthoLoop framework enables interactive, well-controlled and reproducible neuroethological studies in large-field naturalistic settings.

NSs amyloid formation is associated with the virulence of Rift Valley fever virus in mice

Amyloid fibrils result from the aggregation of host cell-encoded proteins, many giving rise to specific human illnesses such as Alzheimer's disease. Here we show that the major virulence factor of Rift Valley fever virus, the protein NSs, forms filamentous structures in the brain of mice and affects mortality. NSs assembles into nuclear and cytosolic disulfide bond-dependent fibrillary aggregates in infected cells. NSs structural arrangements exhibit characteristics typical for amyloids, such as an ultrastructure of 12 nm-width fibrils, a strong detergent resistance, and interactions with the amyloid-binding dye Thioflavin-S. The assembly dynamics of viral amyloid-like fibrils can be visualized in real-time. They form spontaneously and grow in an amyloid fashion within 5 hours. Together, our results demonstrate that viruses can encode amyloid-like fibril-forming proteins and have strong implications for future research on amyloid aggregation and toxicity in general.

Chronic restraint stress increases social interaction in C57BL/6J mice monitoring through MiceProfiler analysis

The social deficit is a prevailing symptom in stress-induced depression. Although social interaction behavior has been widely studied in humans and rodents, it is imprecise to record the social behavior between two free-moving mice via perusal. In the present study, we applied an approach to analyze the social behavior in mice using a software named "MiceProfiler." C57BL/6J mice were stressed via chronic restraint stress (CRS) and housed in three populations of different sizes as follows: single, three in a cage, and six in a cage. The MiceProfiler was used to analyze the video of behavioral repertoire and, the result showed that stressed and single housed mice exhibited more social interaction both in the contact time and contact activities. Furthermore, we investigated the effect of CRS on social behavior when the mice were housed in larger populations size (three or six in a cage) and found that, the CRS procedure promoted social interaction. However, the larger population size resulted in the less total contact time, less time of head-tail, and moving in an opposite way. Besides, the CRS mice showed less social avoidance while the mice from a larger population presented less active contact. And the CRS mice also exhibited a higher social hierarchy compared with the control. Our data indicated that mild restraint stress might increase the intercommunication between mice. Collectively, our findings provided a new evidence for social behavior study and the MiceProfiler could be a new tool to measure the social behaviors of rodents.

An information-theory approach to geometry for animal groups

One of the hardest problems in studying animal behaviour is to quantify patterns of social interaction at the group level. Recent technological developments in global positioning system (GPS) devices have opened up new avenues for locating animals with unprecedented spatial and temporal resolution. Likewise, advances in computing power have enabled new levels of data analyses with complex mathematical models to address unresolved problems in animal behaviour, such as the nature of group geometry and the impact of group-level interactions on individuals. Here, we present an information theory-based tool for the analysis of group behaviour. We illustrate its affordances with GPS data collected from a freely interacting pack of 15 Siberian huskies (Canis lupus familiaris). We found that individual freedom in movement decisions was limited to about 4%, while a subject's location could be predicted with 96% median accuracy by the locations of other group members. Dominant individuals were less affected by other individuals' locations than subordinate ones, and same-sex individuals influenced each other more strongly than opposite-sex individuals. We also found that kinship relationships increased the mutual dependencies of individuals. Moreover, the network stability of the pack deteriorated with an upcoming feeding event. Together, we conclude that information theory-based approaches, coupled with state-of-the-art bio-logging technology, provide a powerful tool for future studies of animal social interactions beyond the dyadic level.

Cognition- and circuit-based dysfunction in a mouse model of 22q11.2 microdeletion syndrome: effects of stress

Genetic microdeletion at the 22q11 locus is associated with very high risk for schizophrenia. The 22q11.2 microdeletion (Df(h22q11)/+) mouse model shows cognitive deficits observed in this disorder, some of which can be linked to dysfunction of the prefrontal cortex (PFC). We used behavioral (n = 10 per genotype), electrophysiological (n = 7 per genotype per group), and neuroanatomical (n = 5 per genotype) techniques to investigate schizophrenia-related pathology of Df(h22q11)/+ mice, which showed a significant decrease in the total number of parvalbumin positive interneurons in the medial PFC. The Df(h22q11)/+ mice when tested on PFC-dependent behavioral tasks, including gambling tasks, perform significantly worse than control animals while exhibiting normal behavior on hippocampus-dependent tasks. They also show a significant decrease in hippocampus-medial Prefrontal cortex (H-PFC) synaptic plasticity (long-term potentiation, LTP). Acute platform stress almost abolished H-PFC LTP in both wild-type and Df(h22q11)/+ mice. H-PFC LTP was restored to prestress levels by clozapine (3 mg/kg i.p.) in stressed Df(h22q11)/+ mice, but the restoration of stress-induced LTP, while significant, was similar between wild-type and Df(h22q11)/+ mice. A medial PFC dysfunction may underlie the negative and cognitive symptoms in human 22q11 deletion carriers, and these results are relevant to the current debate on the utility of clozapine in such subjects.

Assessment of Laboratory Mouse Activity in Video Recordings Using Deep Learning Methods

Analysis of laboratory animal behavior allows assessment of animal wellbeing. We present a method for the classification of different activities of laboratory mice by analyzing video clips using three deep learning methods. Animals placed in observation cages are filmed and short video clips are labelled as belonging to one of five defined behaviors. Subsequently, three different methods based on convolutional neural networks (CNNS) are applied to classify the clips. The best performing method - a two-stream network that analyzes individual frames as well as the video's optical flow - achieves an accuracy of 86.4%, including detection of important behavioral patterns such as self-grooming. These results show that the presented analysis protocol allows automated assessment of animal behavior by algorithmic analysis of videos of mice on observation boxes.

Transplantation of fecal microbiota from patients with alcoholism induces anxiety/depression behaviors and decreases brain mGluR1/PKC ε levels in mouse

Recent studies have revealed that the gut microbiota participates in the psychiatric behavior changes in disorders associated with alcohol. But it still remains unknown whether alcoholism is involved in changes in gut microbiota and its underlying mechanism is also not clear. Here, we tested the gut microbiota of patients with alcoholism and conducted fecal microbiota transplantation (FMT) from patients with alcoholism to C57BL/6J mice whose gut microbiota had been sharply suppressed with antibiotics (ABX). Then we evaluated their alcohol preference degree, anxiety, and depression-like behaviors and social interaction behaviors, together with molecular changes in the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc). Our data indicated that the gut microbiota of patients with alcoholism was drastically different from those of the healthy adults. The abundance of p_Firmicutes was significantly increased whereas p_Bacteroidetes was decreased. Compared to mice transplanted with fecal microbiota from healthy male adults, the mice accepting fecal microbiota from patients with alcoholism showed (a) anxiety-like and depression-like behaviors, (b) decreased social interaction behaviors, (c) spontaneous alcohol preference, and (d) decreased brain-derived neurotrophic factor (BDNF), alpha 1 subunit of GABA type A receptor (α1GABA_A R) in mPFC and decreased metabotropic glutamate receptors 1 (mGluR1), protein kinase C (PKC) ε in NAc. Overall, our results suggest that fecal microbiota from patients with alcoholism did induce a status like alcohol dependence in C57BL/6J mice. The decreased expression of BDNF, α1GABA_A R, and mGluR1/ PKC ε may be the underlying mechanism.

Understanding the circuit basis of cognitive functions using mouse models

Understanding how cognitive functions arise from computations occurring in the brain requires the ability to measure and perturb neural activity while the relevant circuits are engaged for specific cognitive processes. Rapid technical advances have led to the development of new approaches to transiently activate and suppress neuronal activity as well as to record simultaneously from hundreds to thousands of neurons across multiple brain regions during behavior. To realize the full potential of these approaches for understanding cognition, however, it is critical that behavioral conditions and stimuli are effectively designed to engage the relevant brain networks. Here, we highlight recent innovations that enable this combined approach. In particular, we focus on how to design behavioral experiments that leverage the ever-growing arsenal of technologies for controlling and measuring neural activity in order to understand cognitive functions.

Three-Dimensional Pose Estimation for Laboratory Mouse From Monocular Images

Video-based activity and behavior analysis of mice has garnered wide attention in biomedical research. Animal facilities hold large numbers of mice housed in "home-cages" densely stored within ventilated racks. Automated analysis of mice activity in their home-cages can provide a new set of sensitive measures for detecting abnormalities and time-resolved deviation from the baseline behavior. Large-scale monitoring in animal facilities requires minimal footprint hardware that integrates seamlessly with the ventilated racks. The compactness of hardware imposes the use of fisheye lenses positioned in close proximity to the cage. In this paper, we propose a systematic approach to accurately estimate the 3D pose of the mouse from single-monocular fisheye-distorted images. Our approach employs a novel adaptation of a structured forest algorithm. We benchmark our algorithm against existing methods. We demonstrate the utility of the pose estimates in predicting mouse behavior in a continuous video.

Fast, versatile and quantitative annotation of complex images

We report a generic smartphone app for quantitative annotation of complex images. The app is simple enough to be used by children, and annotation tasks are distributed across app users, contributing to efficient annotation. We demonstrate its flexibility and speed by annotating >30,000 images, including features of rice root growth and structure, stem cell aggregate morphology, and complex worm (Caenorhabditis elegans) postures, for which we show that the speed of annotation is >130-fold faster than state-of-the-art techniques with similar accuracy.

Is xenon a suitable euthanasia agent for mice?

OBJECTIVE

To compare behavioural and electrophysiological variables of mice undergoing gas euthanasia with either xenon (Xe) or carbon dioxide (CO₂).

STUDY DESIGN

Single animals chronically instrumented for electroencephalography (EEG) recording were randomized to undergo euthanasia with either CO₂ or Xe (n = 6 animals per group).

ANIMALS

Twelve adult (>6 weeks old) male C57Bl6/n mice.

METHODS

Mice were surgically instrumented with EEG and electromyogram electrodes. Following a 7-day recovery period, animals were placed individually in a sealed chamber and a 5-minute baseline recorded in 21% O₂. Gas [100% Xe (n = 6) or 100% CO₂ (n = 6)] was then added to the chamber at 30% chamber volume minute^-1 (2.8 L minute^-1) until cessation of breathing. EEG, behaviour (jumping and freezing) and locomotion speed were recorded throughout.

RESULTS

Mice undergoing single gas euthanasia with Xe did not show jumping or freezing behaviours and had reduced locomotion speed compared to baseline, in contrast to CO₂, which resulted in increases in these variables. EEG recordings revealed sedative effects from Xe but heightened arousal from CO₂.

CONCLUSIONS

Our data suggest that Xe may be less aversive than CO₂ when using a 30% chamber volume minute^-1 fill rate and could improve the welfare of mice undergoing gas euthanasia.

Robust mouse tracking in complex environments using neural networks

The ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background in order to achieve proper foreground/background detection (segmentation). Modifying environmental conditions for experimental scalability opposes ethological relevance. The biobehavioral research community needs methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we applied a state-of-the-art neural network-based tracker for single mice. We compare three different neural network architectures across visually diverse mice and different environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pretrained network for the behavior and neuroscience communities.

A multiscale analysis in CD38-/- mice unveils major prefrontal cortex dysfunctions

Autism spectrum disorder (ASD) is characterized by early onset of behavioral and cognitive alterations. Low plasma levels of oxytocin (OT) have also been found in ASD patients; recently, a critical role for the enzyme CD38 in the regulation of OT release was demonstrated. CD38 is important in regulating several Ca²⁺-dependent pathways, but beyond its role in regulating OT secretion, it is not known whether a deficit in CD38 expression leads to functional modifications of the prefrontal cortex (PFC), a structure involved in social behavior. Here, we report that CD38^-/- male mice show an abnormal cortex development, an excitation-inhibition balance shifted toward a higher excitation, and impaired synaptic plasticity in the PFC such as those observed in various mouse models of ASD. We also show that a lack of CD38 alters social behavior and emotional responses. Finally, examining neuromodulators known to control behavioral flexibility, we found elevated monoamine levels in the PFC of CD38^-/- adult mice. Overall, our study unveiled major changes in PFC physiologic mechanisms and provides new evidence that the CD38^-/- mouse could be a relevant model to study pathophysiological brain mechanisms of mental disorders such as ASD.-Martucci, L. L., Amar, M., Chaussenot, R., Benet, G., Bauer, O., de Zélicourt, A., Nosjean, A., Launay, J.-M., Callebert, J., Sebrié, C., Galione, A., Edeline, J.-M., de la Porte, S., Fossier, P., Granon, S., Vaillend, C., Cancela, J.-M., A multiscale analysis in CD38^-/- mice unveils major prefrontal cortex dysfunctions.

Altered social behavior in mice carrying a cortical Foxp2 deletion

Genetic disruptions of the forkhead box transcription factor FOXP2 in humans cause an autosomal-dominant speech and language disorder. While FOXP2 expression pattern are highly conserved, its role in specific brain areas for mammalian social behaviors remains largely unknown. Here we studied mice carrying a homozygous cortical Foxp2 deletion. The postnatal development and gross morphological architecture of mutant mice was indistinguishable from wildtype (WT) littermates. Unbiased behavioral profiling of adult mice revealed abnormalities in approach behavior towards conspecifics as well as in the reciprocal responses of WT interaction partners. Furthermore mutant mice showed alterations in acoustical parameters of ultrasonic vocalizations, which also differed in function of the social context. Cell type-specific gene expression profiling of cortical pyramidal neurons revealed aberrant regulation of genes involved in social behavior. In particular Foxp2 mutants showed the downregulation of Mint2 (Apba2), a gene involved in approach behavior in mice and autism spectrum disorder in humans. Taken together these data demonstrate that cortical Foxp2 is required for normal social behaviors in mice.

Nitrogen gas produces less behavioural and neurophysiological excitation than carbon dioxide in mice undergoing euthanasia

Carbon dioxide (CO₂) is one of the most commonly used gas euthanasia agents in mice, despite reports of aversion and nociception. Inert gases such as nitrogen (N₂) may be a viable alternative to carbon dioxide. Here we compared behavioural and electrophysiological reactions to CO₂ or N₂ at either slow fill or rapid fill in C57Bl/6 mice undergoing gas euthanasia. We found that mice euthanised with CO₂ increased locomotor activity compared to baseline, whereas mice exposed to N₂ decreased locomotion. Furthermore, mice exposed to CO₂ showed significantly more vertical jumps and freezing episodes than mice exposed to N₂. We further found that CO₂ exposure resulted in increased theta:delta of the EEG, a measure of excitation, whereas the N₂ decreased theta:delta. Differences in responses were not oxygen-concentration dependent. Taken together, these results demonstrate that CO₂ increases both behavioural and electrophysiological excitation as well as producing a fear response, whereas N₂ reduces behavioural activity and central neurological depression and may be less aversive although still produces a fear response. Further studies are required to evaluate N₂ as a suitable euthanasia agent for mice.