Automatic visual tracking and social behaviour analysis with multiple mice

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.

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.

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.

Persistent animal identification leveraging non-visual markers

Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse's location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the object identification problem as an assignment problem (solved using Integer Linear Programming), (b) a novel probabilistic model of the affinity between tracklets and RFID data, and (c) a curated dataset with per-frame BB and regularly spaced ground-truth annotations for evaluating the models. The latter is a crucial part of the model, as it provides a principled probabilistic treatment of object detections given coarse localisation. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.

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.

Social behavior in 16p11.2 and 22q11.2 copy number variations: Insights from mice and humans

Genetic 16p11.2 and 22q11.2 deletions and duplications in humans may alter behavioral developmental trajectories increasing the risk of autism and schizophrenia spectrum disorders, and of attention-deficit/hyperactivity disorder. In this review, we will concentrate on 16p11.2 and 22q11.2 deletions' effects on social functioning, beyond diagnostic categorization. We highlight diagnostic and social sub-constructs discrepancies. Notably, we contrast evidence from human studies with social profiling performed in several mouse models mimicking 16p11.2 and 22q11.2 deletion syndromes. Given the complexity of social behavior, there is a need to assess distinct social processes. This will be important to better understand the biology underlying such genetic-dependent dysfunctions, as well as to give perspective on how therapeutic strategies can be improved. Bridges and divergent points between human and mouse studies are highlighted. Overall, we give challenges and future perspectives to sort the genetics of social heterogeneity.

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.

Three-Dimensional Tracking of Multiple Small Insects by a Single Camera

Many systems to monitor insect behavior have been developed recently. Yet most of these can only detect two-dimensional behavior for convenient analysis and exclude other activities, such as jumping or flying. Therefore, the development of a three-dimensional (3D) monitoring system is necessary to investigate the 3D behavior of insects. In such a system, multiple-camera setups are often used to accomplish this purpose. Here, a system with a single camera for tracking small insects in a 3D space is proposed, eliminating the synchronization problems that typically occur when multiple cameras are instead used. With this setup, two other images are obtained via mirrors fixed at other viewing angles. Using the proposed algorithms, the tracking accuracy of five individual drain flies, Clogmia albipunctata (Williston) (Diptera: Psychodidae), flitting about in a spherical arena (78 mm in diameter) is as high as 98.7%, whereas the accuracy of 10 individuals is 96.3%. With this proposed method, the 3D trajectory monitoring experiments of insects can be performed more efficiently.

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.

Big behavior: challenges and opportunities in a new era of deep behavior profiling

The assessment of rodent behavior forms a cornerstone of preclinical assessment in neuroscience research. Nonetheless, the true and almost limitless potential of behavioral analysis has been inaccessible to scientists until very recently. Now, in the age of machine vision and deep learning, it is possible to extract and quantify almost infinite numbers of behavioral variables, to break behaviors down into subcategories and even into small behavioral units, syllables or motifs. However, the rapidly growing field of behavioral neuroethology is experiencing birthing pains. The community has not yet consolidated its methods, and new algorithms transfer poorly between labs. Benchmarking experiments as well as the large, well-annotated behavior datasets required are missing. Meanwhile, big data problems have started arising and we currently lack platforms for sharing large datasets-akin to sequencing repositories in genomics. Additionally, the average behavioral research lab does not have access to the latest tools to extract and analyze behavior, as their implementation requires advanced computational skills. Even so, the field is brimming with excitement and boundless opportunity. This review aims to highlight the potential of recent developments in the field of behavioral analysis, whilst trying to guide a consensus on practical issues concerning data collection and data sharing.

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.

Rodent Activity Detector (RAD), an Open Source Device for Measuring Activity in Rodent Home Cages

Physical activity is a critical behavioral variable in many research studies and is, therefore, important to quantify. However, existing methods for measuring physical activity have limitations which include high expense, specialized caging or equipment, and high computational overhead. To address these limitations, we present an open-source, cost-effective, device for measuring rodent activity. Our device is battery powered and designed to be placed in vivarium home cages to enable high-throughput, long-term operation with minimal investigator intervention. The primary aim of this study was to assess the feasibility of using passive infrared (PIR) sensors and microcontroller-based dataloggers in a rodent home cages to collect physical activity records. To this end, we developed an open-source PIR based data-logging device called the rodent activity detector (RAD). We publish the design files and code so others can readily build the RAD in their own labs. To demonstrate its utility, we used the RAD to collect physical activity data from 40 individually housed mice for up to 10 weeks. This dataset demonstrates the ability of the RAD to (1) operate in a high-throughput installation, (2) detect high-fat diet (HFD)-induced changes in physical activity, and (3) quantify circadian rhythms in individual animals. We further validated the data output of the RAD with simultaneous video tracking of mice in multiple caging configurations, to determine the features of physical activity that it detects. The RAD is easy to build, economical, and fits in vivarium caging. The scalability of such devices will enable high-throughput studies of physical activity in research studies.

The Analysis of Temperature Changes of the Saliva Traces Left on the Fur During Laboratory Rats Soial Contacts

Automatic analysis of complex rodent social behavior, especially aggressive ones, is of important scientific interest. In this paper we analyze the properties of the data created as a result of aggressive rodent social behavior. Detection of specific aggressive behaviors is based on the event of leaving traces of saliva on the fur of the attacked individual, which are clearly visible in the thermal imaging. The traces change temperature in time in a specific way. After bite, saliva is cooled and then heated to the body temperature. Usage of this method in social behavior analysis ensures detection and tracking aggressive behaviors even if the event itself is invisible.

Automated monitoring of mouse feeding and body weight for continuous health assessment

Routine health assessment of laboratory rodents can be improved using automated home cage monitoring. Continuous, non-stressful, objective assessment of rodents unaware that they are being watched, including during their active dark period, reveals behavioural and physiological changes otherwise invisible to human caretakers. We developed an automated feeder that tracks feed intake, body weight, and physical appearance of individual radio frequency identification-tagged mice in social home cages. Here, we experimentally induce illness via lipopolysaccharide challenge and show that this automated tracking apparatus reveals sickness behaviour (reduced food intake) as early as 2-4 hours after lipopolysaccharide injection, whereas human observers conducting routine health checks fail to detect a significant difference between sick mice and saline-injected controls. Continuous automated monitoring additionally reveals pronounced circadian rhythms in both feed intake and body weight. Automated home cage monitoring is a non-invasive, reliable mode of health surveillance allowing caretakers to more efficiently detect and respond to early signs of illness in laboratory rodent populations.

Costa R. D-Track-A semi-automatic 3D video-tracking technique to analyse movements and routines of aquatic animals with application to captive dolphins

Scoring and tracking animal movements manually is a time consuming and subjective process, susceptible to errors due to fatigue. Automated and semi-automated video-based tracking methods have been developed to overcome the errors and biases of manual analyses. In this manuscript we present D-Track, an open-source semi-automatic tracking system able to quantify the 3D trajectories of dolphins, non-invasively, in the water. This software produces a three-dimensional reconstruction of the pool and tracks the animal at different depths, using standard cameras. D-Track allows the determination of spatial preferences of the animals, their speed and its variations, and the identification of behavioural routines. We tested the system with two captive dolphins during different periods of the day. Both animals spent around 85% of the time at the surface of the Deep Area of their pool (5-meters depth). Both dolphins showed a stable average speed throughout 31 sessions, with slow speeds predominant (maximum 1.7 ms^-1). Circular swimming was highly variable, with significant differences in the size and duration of the “circles”, between animals, within-animals and across sessions. The D-Track system is a novel tool to study the behaviour of aquatic animals, and it represents a convenient and inexpensive solution for laboratories and marine parks to monitor the preferences and routines of their animals.

3-D motion capture for long-term tracking of spontaneous locomotor behaviors and circadian sleep/wake rhythms in mouse

BACKGROUND

Locomotor activity provides an index of an animal's behavioral state. Here, we report a reliable and cost-effective method that allows long-term (days to months) simultaneous tracking of locomotion in mouse cohorts (here consisting of 24 animals).

NEW METHOD

The technique is based on a motion capture system used mainly for human movement study. A reflective marker was placed on the head of each mouse using a surgical procedure and labeled animals were returned to their individual home cages. Camera-recorded data of marker displacement resulting from locomotor movements were then analyzed with custom built software. To avoid any data loss, data files were saved every hour and automatically concatenated. Long-term recordings (up to 3 months) with high spatial (<1mm) and temporal (up to 100Hz) resolution of animal movements were obtained.

RESULTS

The system was validated by analyzing the spontaneous activity of mice from post-natal day 30-90. Daily motor activity increased up to 70days in correspondence with maturational changes in locomotor performance. The recorded actigrams also permitted analysis of circadian and ultradian rhythms in cohort sleep/wake behavior.

COMPARISON WITH EXISTING METHOD(S)

In contrast to traditional session-based experimental approaches, our technique allows locomotor activity to be recorded with minimal experimenter manipulation, thereby minimizing animal stress.

CONCLUSIONS

Our method enables the continuous long-term (up to several months) monitoring of tens of animals, generating manageable amounts of data at minimal costs without requiring individual dedicated devices. The actigraphic data collected allows circadian and ultradian analysis of sleep/wake behaviors to be performed.

ToxId: an efficient algorithm to solve occlusions when tracking multiple animals

Video analysis of animal behaviour is widely used in fields such as ecology, ecotoxicology, and evolutionary research. However, when tracking multiple animals, occlusion and crossing are problematic, especially when the identity of each individual needs to be preserved. We present a new algorithm, ToxId, which preserves the identity of multiple animals by linking trajectory segments using their intensity histogram and Hu-moments. We verify the performance and accuracy of our algorithm using video sequences with different animals and experimental conditions. The results show that our algorithm achieves state-of-the-art accuracy using an efficient approach without the need of learning processes, complex feature maps or knowledge of the animal shape. ToxId is also computationally efficient, has low memory requirements, and operates without accessing future or past frames.

Automatic analysis of the aggressive behavior of laboratory animals using thermal video processing

The bite detection is very important but difficult element of the social interaction analysis. Standard observation methods like human observer or a camcorder of visible light frequencies fail in this case. However, it is possible to discern cooler spots on the rodent's body that appear after body contact with another individual, and vanish after short time. These spots are assumed to be a saliva trace left on fur after bite. In this paper we have described a result of saliva trace detection by the most popular corner detectors. The analysis of traces and their parameters is also presented. The dynamic characteristic of the temperature change of the saliva trace enables the automatic discrimination of the related characteristic point from other corner points. This can be very useful for the automatic analysis of social behavior of animals in many pharmacological studies.

Automated classification of self-grooming in mice using open-source software

BACKGROUND

Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification.

NEW METHOD

We trained a freely available, automated classifier, Janelia Automatic Animal Behavior Annotator (JAABA), to quantify self-grooming duration and number of bouts based on video recordings of SAPAP3 knockout mice (a mouse line that self-grooms excessively) and wild-type animals.

RESULTS

We compared the JAABA classifier with human expert observers to test its ability to measure self-grooming in three scenarios: mice in an open field, mice on an elevated plus-maze, and tethered mice in an open field. In each scenario, the classifier identified both grooming and non-grooming with great accuracy and correlated highly with results obtained by human observers. Consistently, the JAABA classifier confirmed previous reports of excessive grooming in SAPAP3 knockout mice.

COMPARISON WITH EXISTING METHODS

Thus far, manual analysis was regarded as the only valid quantification method for self-grooming. We demonstrate that the JAABA classifier is a valid and reliable scoring tool, more cost-efficient than manual scoring, easy to use, requires minimal effort, provides high throughput, and prevents inter-rater variability.

CONCLUSION

We introduce the JAABA classifier as an efficient analysis tool for the assessment of rodent self-grooming with expert quality. In our "how-to" instructions, we provide all information necessary to implement behavioral classification with JAABA.

An unsupervised learning approach for tracking mice in an enclosed area

BACKGROUND

In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis.

RESULTS

We present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments.

CONCLUSIONS

The proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.

Low-cost three-dimensional gait analysis system for mice with an infrared depth sensor

Three-dimensional (3D) open-field gait analysis of mice is an essential procedure in genetic and nerve regeneration research. Existing gait analysis systems are generally expensive and may interfere with the natural behaviors of mice because of optical markers and transparent floors. In contrast, the proposed system captures the subjects shape from beneath using a low-cost infrared depth sensor (Microsoft Kinect) and an opaque infrared pass filter. This means that we can track footprints and 3D paw-tip positions without optical markers or a transparent floor, thereby preventing any behavioral changes. Our experimental results suggest with healthy mice that they are more active on opaque floors and spend more time in the center of the open-field, when compared with transparent floors. The proposed system detected footprints with a comparable performance to existing systems, and precisely tracked the 3D paw-tip positions in the depth image coordinates.

Ethological concepts enhance the translational value of animal models

The translational value of animal models is an issue of ongoing discussion. We argue that 'Refinement' of animal experiments is needed and this can be achieved by exploiting an ethological approach when setting up and conducting experiments. Ethology aims to assess the functional meaning of behavioral changes, due to experimental manipulation or treatment, in animal models. Although the use of ethological concepts is particularly important for studies involving the measurement of animal behavior (as is the case for most studies on neuro-psychiatric conditions), it will also substantially benefit other disciplines, such as those investigating the immune system or inflammatory response. Using an ethological approach also involves using more optimal testing conditions are employed that have a biological relevance to the animal. Moreover, using a more biological relevant analysis of the data will help to clarify the functional meaning of the modeled readout (e.g. whether it is psychopathological or adaptive in nature). We advocate for instance that more behavioral studies should use animals in group-housed conditions, including the recording of their ultrasonic vocalizations, because (1) social behavior is an essential feature of animal models for human 'social' psychopathologies, such as autism and schizophrenia, and (2) social conditions are indispensable conditions for appropriate behavioral studies in social species, such as the rat. Only when taking these elements into account, the validity of animal experiments and, thus, the translation value of animal models can be enhanced.

GroupTracker: Video tracking system for multiple animals under severe occlusion

Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation-maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias latipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://sites.google.com/site/fukunagatsu/software/group-tracker.

Reported Drosophila courtship song rhythms are artifacts of data analysis

Background

In a series of landmark papers, Kyriacou, Hall, and colleagues reported that the average inter-pulse interval of Drosophila melanogaster male courtship song varies rhythmically (KH cycles), that the period gene controls this rhythm, and that evolution of the period gene determines species differences in the rhythm’s frequency. Several groups failed to recover KH cycles, but this may have resulted from differences in recording chamber size.

Results

Here, using recording chambers of the same dimensions as used by Kyriacou and Hall, I found no compelling evidence for KH cycles at any frequency. By replicating the data analysis procedures employed by Kyriacou and Hall, I found that two factors - data binned into 10-second intervals and short recordings - imposed non-significant periodicity in the frequency range reported for KH cycles. Randomized data showed similar patterns.

Conclusions

All of the results related to KH cycles are likely to be artifacts of binning data from short songs. Reported genotypic differences in KH cycles cannot be explained by this artifact and may have resulted from the use of small sample sizes and/or from the exclusion of samples that did not exhibit song rhythms.

A real-time 3D video tracking system for monitoring primate groups

To date, assessing the solitary and social behaviors of laboratory primates' colonies relies on time-consuming manual scoring methods. Here, we describe a real-time multi-camera 3D tracking system developed to measure the behavior of socially-housed primates. Their positions are identified using non-invasive color markers such as plastic collars, thus allowing to also track colored objects and to measure their usage. Compared to traditional manual ethological scoring, we show that this system can reliably evaluate solitary behaviors (foraging, solitary resting, toy usage, locomotion) as well as spatial proximity with peers, which is considered as a good proxy of their social motivation. Compared to existing video-based commercial systems currently available to measure animal activity, this system offers many possibilities (real-time data, large volume coverage, multiple animal tracking) at a lower hardware cost. Quantitative behavioral data of animal groups can now be obtained automatically over very long periods of time, thus opening new perspectives in particular for studying the neuroethology of social behavior in primates.

Chronic and acute intranasal oxytocin produce divergent social effects in mice

Intranasal administration of oxytocin (OXT) might be a promising new adjunctive therapy for mental disorders characterized by social behavioral alterations such as autism and schizophrenia. Despite promising initial studies in humans, it is not yet clear the specificity of the behavioral effects induced by chronic intranasal OXT and if chronic intranasal OXT could have different effects compared with single administration. This is critical for the aforementioned chronic mental disorders that might potentially involve life-long treatments. As a first step to address these issues, here we report that chronic intranasal OXT treatment in wild-type C57BL/6J adult mice produced a selective reduction of social behaviors concomitant to a reduction of the OXT receptors throughout the brain. Conversely, acute intranasal OXT treatment produced partial increases in social behaviors towards opposite-sex novel-stimulus female mice, while on the other hand, it decreased social exploration of same-sex novel stimulus male mice, without affecting social behavior towards familiar stimulus male mice. Finally, prolonged exposure to intranasal OXT treatments did not alter, in wild-type animals, parameters of general health such as body weight, locomotor activity, olfactory and auditory functions, nor parameters of memory and sensorimotor gating abilities. These results indicate that a prolonged over-stimulation of a 'healthy' oxytocinergic brain system, with no inherent deficits in social interaction and normal endogenous levels of OXT, results in specific detrimental effects in social behaviors.