Novel approach to automatically classify rat social behavior using a video tracking system

Review of Rat (Rattus norvegicus), Mouse (Mus musculus), Guinea pig (Cavia porcellus), and Rabbit (Oryctolagus cuniculus) Indicators for Welfare Assessment

The monitoring and assessment of animals is important for their health and welfare. The appropriate selection of multiple, validated, and feasible welfare assessment indicators is required to effectively identify compromises or improvements to animal welfare. Animal welfare indicators can be animal or resource based. Indicators can be collated to form assessment tools (e.g., grimace scales) or animal welfare assessment models (e.g., 5 Domains) and frameworks (e.g., 5 Freedoms). The literature contains a wide variety of indicators, with both types needed for effective animal welfare assessment; however, there is yet to be an ideal constellation of indicators for animal-based welfare assessment in small mammals such as guinea pigs (Cavia Porcellus), mice (Mus musculus), rabbits (Oryctolagus cuniculus), and rats (Rattus norvegicus). A systematic review of grey and peer-reviewed literature was performed to determine the types of animal-based welfare indicators available to identify and assess animal health and welfare in these small mammals maintained across a wide variety of conditions. The available indicators were categorised and scored against a selection of criteria, including potential ease of use and costs. This review and analysis aim to provide the basis for further research into animal welfare indicators for these species. Future applications of this work may include improvements to animal welfare assessments or schemes, guiding better management, and implementing future strategies to enable better animal welfare.

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.

Density of predating Asian hornets at hives disturbs the 3D flight performance of honey bees and decreases predation success

Automated 3D image-based tracking systems are new and promising devices to investigate the foraging behavior of flying animals with great accuracy and precision. 3D analyses can provide accurate assessments of flight performance in regard to speed, curvature, and hovering. However, there have been few applications of this technology in ecology, particularly for insects. We used this technology to analyze the behavioral interactions between the Western honey bee Apis mellifera and its invasive predator the Asian hornet, Vespa velutina nigrithorax. We investigated whether predation success could be affected by flight speed, flight curvature, and hovering of the Asian hornet and honey bees in front of one beehive. We recorded a total of 603,259 flight trajectories and 5175 predator-prey flight interactions leading to 126 successful predation events, representing 2.4% predation success. Flight speeds of hornets in front of hive entrances were much lower than that of their bee prey; in contrast to hovering capacity, while curvature range overlapped between the two species. There were large differences in speed, curvature, and hovering between the exit and entrance flights of honey bees. Interestingly, we found hornet density affected flight performance of both honey bees and hornets. Higher hornet density led to a decrease in the speed of honey bees leaving the hive, and an increase in the speed of honey bees entering the hive, together with more curved flight trajectories. These effects suggest some predator avoidance behavior by the bees. Higher honey bee flight curvature resulted in lower hornet predation success. Results showed an increase in predation success when hornet number increased up to 8 individuals, above which predation success decreased, likely due to competition among predators. Although based on a single colony, this study reveals interesting outcomes derived from the use of automated 3D tracking to derive accurate measures of individual behavior and behavioral interactions among flying species.

Multidimensional nature of dominant behavior: Insights from behavioral neuroscience

Social interactions for many species of animals are critical for survival, wellbeing, and reproduction. Optimal navigation of a social system increases chances for survival and reproduction, therefore there is strong incentive to fit into social structures. Social animals rely heavily on dominant-submissive behaviors in establishment of stable social hierarchies. There is a link between extreme manifestation of dominance/submissiveness and behavioral deviations. To understand neural substrates affiliated with a specific hierarchical rank, there is a real need for reliable animal behavioral models. Different paradigms have been consolidated over time to study the neurobiology of social rank behavior in a standardized manner using rodent models to unravel the neural pathways and substrates involved in normal and abnormal intraspecific social interactions. This review summarizes and discusses the commonly used behavioral tests and new directions for the assessment of dominance in rodents. We discuss the hierarchy inheritable nature and other critical issues regarding hierarchical rank manifestation which may help in designing social-rank-related studies that serve as promising pre-clinical tools in behavioral psychiatry.

Analixity: An open source, low-cost analysis system for the elevated plus maze test, based on computer vision techniques

Manual analysis of behavioral tests in rodents involves inspection of video recordings by a researcher that assesses rodent movements to quantify parameters related with a behavior of interest. The assessment of the researcher during the quantification of such parameters can introduce variability among experimental conditions or among sessions of analysis. Here, we introduce Analixity, a video processing software for the elevated plus maze test (EPM), in which quantification of behavioral parameters is automatic, reducing the time spent in analysis and solving the variability problem. Analixity is an adaptable multiplatform open-source system. Analixity generates an Excel file with the quantified behavioral variables, such as time spent in open and closed arms and in the center zone, number of entries to each zone and total distance traveled during the test. For validation, we compared results obtained by Analixity with results obtained by manual analysis. We did not find statistically significant differences. In addition, we compared the results obtained by Analixity with results obtained by the commercial software ANY-maze. We did not find statistically significant differences in the quantification of parameters such as time spent in open arms, time spent in closed arms, time spent in center zone, number of closed arms, open arms entries, and anxiety index. We concluded that Analixity is an open-source software as reliable and effective as a commercial software.

Animal behavior and animal personality from a non-human perspective: Getting help from the machine

We can now track the position of every fly's leg or immerse a tiny fish inside a virtual world by monitoring its gaze in real time. Yet capturing animals' posture or gaze is not like understanding their behavior. Instead, behaviors are still often interpreted by human observers in an anthropomorphic manner. Even newer tools that automatically classify behaviors rely on human observers for the choice of behaviors. In this perspective, we suggest a roadmap toward a "human-free" interpretation of behavior. We present several recent advances, including our recent work on animal personalities. Personality both underlies behavioral differences among individuals and is consistent over time. A mathematical formulation of this idea has allowed us to measure mouse traits objectively, map behaviors across species (humans included), and explore the biological basis of behavior. Our goal is to enable "machine translation" of raw movement data into intelligible human concepts en route to improving our understanding of animals and people.

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object

A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.

BACHD rats expressing full-length mutant huntingtin exhibit differences in social behavior compared to wild-type littermates

Background

Huntington disease (HD) is a devastating inherited neurodegenerative disorder characterized by progressive motor, cognitive, and psychiatric symptoms without any cure to slow down or stop the progress of the disease. The BACHD rat model for HD carrying the human full-length mutant huntingtin protein (mHTT) with 97 polyQ repeats has been recently established as a promising model which reproduces several HD-like features. While motor and cognitive functions have been characterized in BACHD rats, little is known about their social phenotype.

Objective

This study focuses especially on social behavior since evidence for social disturbances exists in human patients. Our objective was to compare social behavior in BACHD and wild-type (WT) rats at different ages, using two different measures of sociability.

Methods

Animals were tested longitudinally at the age of 2, 4 and 8 months in the social interaction test to examine different parameters of sociability. A separate cohort of 7 month old rats was tested in the three chamber social test to measure both sociability and social novelty. Gene expression analyses in 8 months old animals were performed by real time qRT-PCR to evaluate a potential involvement of D1 and D2 dopaminergic receptors and the contribution of Brain-derived neurotrophic factor (BDNF) to the observed behavioral alterations.

Results

In the social interaction test, BACHD rats showed age-dependent changes in behaviour when they were-re introduced to their cagemate after a 24 hours-period of individual housing. The time spent on nape attacks increased with aging. Furthermore, a significant higher level of pinning at 2 months of age was shown in the BACHD rats compared to wild-types, followed by a reduction at 4 and 8 months. On the other hand, BACHD rats exhibited a decreased active social behaviour compared to wild-types, reflected by genotype-effects on approaching, following and social nose contact. In the three chamber social test, BACHD rats seemed to show a mild deficit in preference for social novelty, but no changes in social interest. Molecular analyses revealed that BACHD animals exposed to the social interaction test displayed decreased mRNA levels of the total form of BDNF in ventral striatum and unaltered striatal expression of D1 and D2 dopamine receptors.

Conclusions

Taken together, these results indicate deficits in several parameters representative of sociability. Altered BDNF expression in the ventral striatum may contribute to the deficits in sociability in 8 months old BACHD rats. These data support the validity of the BACHD rat model in mimicking features of certain social deficits that could be relevant to symptoms in patients.

Short- and long-term behavioral analysis of social interaction, ultrasonic vocalizations and social motivation in a chronic phencyclidine model

Phencyclidine (PCP) has been suggested to induce symptoms of schizophrenia. However, animal models using PCP administration have produced ambiguous results thus far. It seems that acute effects are similar to symptoms of schizophrenia, however, it is not clear if PCP can induce permanent behavioral changes that reflect schizophrenic-like symptoms. Therefore, we assessed the ability of chronic PCP administration (3mg/kg, 14 days) to induce short or long lasting behavioral changes in rats. Social behavior, including ultrasonic vocalizations and motivation for social contact were investigated at different time points, up to 29-36 days, after cessation of PCP treatment. During a social separation test, performed at 5 and 36 days, PCP treated rats spent less time near the divider that separates them from their familiar cage mate compared with saline (SAL) treated rats. Further, at short term, PCP was able to induce a decrease in social behavior. In contrast, at long-term, PCP treated animals spent more time in contact when exposed to an unfamiliar partner as compared to SAL treated rats. But, this difference was not observed when exposed to a familiar partner. We did not find any difference in ultrasonic vocalizations at all time points. The results of our study indicate that PCP is unable to induce overt long term deficits in social interaction behavior. Rather, it seems that PCP diminishes motivation for social contact. The long-term consequences of chronic PCP administration on social behavior in rodent models remain complex, and future studies addressing this are still needed.