The power of automated behavioural homecage technologies in characterizing disease progression in laboratory mice: A review

A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors

Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automate analysis but often fail under variable conditions or ignore temporal dynamics. We developed a scalable pipeline for continuous, real-time acquisition and classification of behavior across multiple animals and conditions. At its core is a self-supervised vision model paired with a lightweight classifier that enables robust performance with minimal manual labeling. Our system achieves expert-level performance and can operate indefinitely across diverse recording environments. As a proof-of-concept, we recorded 97 mice over 2 weeks to test whether sex hormones influence circadian behaviors. We discovered sex- and estrogen-dependent rhythms in behaviors such as digging and nesting. We introduce the Circadian Behavioral Analysis Suite (CBAS), a modular toolkit that supports high-throughput, long-timescale behavioral phenotyping, allowing for the temporal analysis of behaviors that were previously difficult or impossible to observe.

Highly accurate and precise determination of mouse mass using computer vision

Changes in body mass are key indicators of health in humans and animals and are routinely monitored in animal husbandry and preclinical studies. In rodent studies, the current method of manually weighing the animal on a balance causes at least two issues. First, directly handling the animal induces stress, possibly confounding studies. Second, these data are static, limiting continuous assessment and obscuring rapid changes. A non-invasive, continuous method of monitoring animal mass would have utility in multiple biomedical research areas. We combine computer vision with statistical modeling to demonstrate the feasibility of determining mouse body mass by using video data. Our methods determine mass with a 4.8% error across genetically diverse mouse strains with varied coat colors and masses. This error is low enough to replace manual weighing in most mouse studies. We conclude that visually determining rodent mass enables non-invasive, continuous monitoring, improving preclinical studies and animal welfare.

Longitudinal quantitative assessment of TMEV-IDD-induced MS phenotypes in two inbred mouse strains using automated video tracking technology

Multiple sclerosis (MS) is a chronic disabling disease of the central nervous system affecting over 2.5 million people worldwide. Theiler's murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) is a murine model that reproduces the progressive form of MS and serves as a reference model for studying virus-induced demyelination. Certain mouse strains such as SJL are highly susceptible to this virus and serve as a prototype strain for studying TMEV infection. Other strains such as SWR are also susceptible, but their disease course following TMEV infection differs from SJL's. The quantification of motor and behavioral deficits following the induction of TMEV-IDD could help identify the differences between the two strains. Motor deficits have commonly been measured with the rotarod apparatus, but a multicomponent assessment tool has so far been lacking. For that purpose, we present a novel way of quantifying locomotor deficits, gait alterations and behavioral changes in this well-established mouse model of multiple sclerosis by employing automated video analysis technology (The PhenoTyper, Noldus Information Technology). We followed 12 SJL and 12 SWR female mice and their mock-infected counterparts over a period of 9 months following TMEV-IDD induction. We demonstrated that SJL and SWR mice both suffer significant gait alterations and reduced exploration following TMEV infection. However, SJL mice also display an earlier and more severe decline in spontaneous locomotion, especially in velocity, as well as in overall activity. Maintenance behaviors such as eating and grooming are not affected in either of the two strains. The system also showed differences in mock-infected mice from both strains, highlighting an age-related decline in spontaneous locomotion in the SJL strain, as opposed to hyperactivity in the SWR strain. Our study confirms that this automated video tracking system can reliably track the progression of TMEV-IDD for 9 months. We have also shown how this system can be utilized for longitudinal phenotyping in mice by describing useful parameters that quantify locomotion, gait and behavior.

A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors

Measuring animal behavior over long timescales has been traditionally limited to behaviors that are easily measurable with real-time sensors. More complex behaviors have been measured over time, but these approaches are considerably more challenging due to the intensive manual effort required for scoring behaviors. Recent advances in machine learning have introduced automated behavior analysis methods, but these often overlook long-term behavioral patterns and struggle with classification in varying environmental conditions. To address this, we developed a pipeline that enables continuous, parallel recording and acquisition of animal behavior for an indefinite duration. As part of this pipeline, we applied a recent breakthrough self-supervised computer vision model to reduce training bias and overfitting and to ensure classification robustness. Our system automatically classifies animal behaviors with a performance approaching that of expert-level human labelers. Critically, classification occurs continuously, across multiple animals, and in real time. As a proof-of-concept, we used our system to record behavior from 97 mice over two weeks to test the hypothesis that sex and estrogen influence circadian rhythms in nine distinct home cage behaviors. We discovered novel sex- and estrogen-dependent differences in circadian properties of several behaviors including digging and nesting rhythms. We present a generalized version of our pipeline and novel classification model, the "circadian behavioral analysis suite," (CBAS) as a user-friendly, open-source software package that allows researchers to automatically acquire and analyze behavioral rhythms with a throughput that rivals sensor-based methods, allowing for the temporal and circadian analysis of behaviors that were previously difficult or impossible to observe.

Highly Accurate and Precise Determination of Mouse Mass Using Computer Vision

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

A systematic review of the development and application of home cage monitoring in laboratory mice and rats

BACKGROUND

Traditionally, in biomedical animal research, laboratory rodents are individually examined in test apparatuses outside of their home cages at selected time points. However, the outcome of such tests can be influenced by various factors and valuable information may be missed when the animals are only monitored for short periods. These issues can be overcome by longitudinally monitoring mice and rats in their home cages. To shed light on the development of home cage monitoring (HCM) and the current state-of-the-art, a systematic review was carried out on 521 publications retrieved through PubMed and Web of Science.

RESULTS

Both the absolute (~ × 26) and relative (~ × 7) number of HCM-related publications increased from 1974 to 2020. There was a clear bias towards males and individually housed animals, but during the past decade (2011-2020), an increasing number of studies used both sexes and group housing. In most studies, animals were kept for short (up to 4 weeks) time periods in the HCM systems; intermediate time periods (4-12 weeks) increased in frequency in the years between 2011 and 2020. Before the 2000s, HCM techniques were predominantly applied for less than 12 h, while 24-h measurements have been more frequent since the 2000s. The systematic review demonstrated that manual monitoring is decreasing in relation to automatic techniques but still relevant. Until (and including) the 1990s, most techniques were applied manually but have been progressively replaced by automation since the 2000s. Independent of the year of publication, the main behavioral parameters measured were locomotor activity, feeding, and social behaviors; the main physiological parameters were heart rate and electrocardiography. External appearance-related parameters were rarely examined in the home cages. Due to technological progress and application of artificial intelligence, more refined and detailed behavioral parameters have been investigated in the home cage more recently.

CONCLUSIONS

Over the period covered in this study, techniques for HCM of mice and rats have improved considerably. This development is ongoing and further progress as well as validation of HCM systems will extend the applications to allow for continuous, longitudinal, non-invasive monitoring of an increasing range of parameters in group-housed small rodents in their home cages.

The Relevance of Animal Models of Social Isolation and Social Motivation for Understanding Schizophrenia: Review and Future Directions

BACKGROUND AND HYPOTHESES

Social dysfunction in schizophrenia includes symptoms of withdrawal and deficits in social skills, social cognition, and social motivation. Based on the course of illness, with social withdrawal occurring prior to psychosis onset, it is likely that the severity of social withdrawal/isolation contributes to schizophrenia neuropathology.

STUDY DESIGN

We review the current literature on social isolation in rodent models and provide a conceptual framework for its relationship to social withdrawal and neural circuit dysfunction in schizophrenia. We next review preclinical tasks of social behavior used in schizophrenia-relevant models and discuss strengths and limitations of existing approaches. Lastly, we consider new effort-based tasks of social motivation and their potential for translational studies in schizophrenia.

STUDY RESULTS

Social isolation rearing in rats produces profound differences in behavior, pharmacologic sensitivity, and neurochemistry compared to socially reared rats. Rodent models relevant to schizophrenia exhibit deficits in social behavior as measured by social interaction and social preference tests. Newer tasks of effort-based social motivation are being developed in rodents to better model social motivation deficits in neuropsychiatric disorders.

CONCLUSIONS

While experimenter-imposed social isolation provides a viable experimental model for understanding some biological mechanisms linking social dysfunction to clinical and neural pathology in schizophrenia, it bypasses critical antecedents to social isolation in schizophrenia, notably deficits in social reward and social motivation. Recent efforts at modeling social motivation using effort-based tasks in rodents have the potential to quantify these antecedents, identify models (eg, developmental, genetic) that produce deficits, and advance pharmacological treatments for social motivation.

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

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

PiE: an open-source pipeline for home cage behavioral analysis

Over the last two decades a growing number of neuroscience labs are conducting behavioral assays in rodents. The equipment used to collect this behavioral data must effectively limit environmental and experimenter disruptions, to avoid confounding behavior data. Proprietary behavior boxes are expensive, offer limited compatible sensors, and constrain analysis with closed-source hardware and software. Here, we introduce PiE, an open-source, end-to-end, user-configurable, scalable, and inexpensive behavior assay system. The PiE system includes the custom-built behavior box to hold a home cage, as well as software enabling continuous video recording and individual behavior box environmental control. To limit experimental disruptions, the PiE system allows the control and monitoring of all aspects of a behavioral experiment using a remote web browser, including real-time video feeds. To allow experiments to scale up, the PiE system provides a web interface where any number of boxes can be controlled, and video data easily synchronized to a remote location. For the scoring of behavior video data, the PiE system includes a standalone desktop application that streamlines the blinded manual scoring of large datasets with a focus on quality control and assay flexibility. The PiE system is ideal for all types of behavior assays in which video is recorded. Users are free to use individual components of this setup independently, or to use the entire pipeline from data collection to analysis. Alpha testers have included scientists without prior coding experience. An example pipeline is demonstrated with the PiE system enabling the user to record home cage maternal behavior assays, synchronize the resulting data, conduct blinded scoring, and import the data into R for data visualization and analysis.

Bouts of rest and physical activity in C57BL/6J mice

The objective was to exploit the raw data output from a scalable home cage (type IIL IVC) monitoring (HCM) system (DVC®), to characterize pattern of undisrupted rest and physical activity (PA) of C57BL/6J mice. The system's tracking algorithm show that mice in isolation spend 67% of the time in bouts of long rest (≥40s). Sixteen percent is physical activity (PA), split between local movements (6%) and locomotion (10%). Decomposition revealed that a day contains ˜7100 discrete bouts of short and long rest, local and locomotor movements. Mice travel ˜330m per day, mainly during the dark hours, while travelling speed is similar through the light-dark cycle. Locomotor bouts are usually <0.2m and <1% are >1m. Tracking revealed also fits of abnormal behaviour. The starting positions of the bouts showed no preference for the rear over the front of the cage floor, while there was a strong bias for the peripheral (75%) over the central floor area. The composition of bouts has a characteristic circadian pattern, however, intrusive husbandry routines increased bout fragmentation by ˜40%. Extracting electrode activations density (EAD) from the raw data yielded results close to those obtained with the tracking algorithm, with 81% of time in rest (<1 EAD s-1) and 19% in PA. Periods ≥40 s of file when no movement occurs and there is no EAD may correspond to periods of sleep (˜59% of file time). We confirm that EAD correlates closely with movement distance (rs>0.95) and the data agreed in ˜97% of the file time. Thus, albeit EAD being less informative it may serve as a proxy for PA and rest, enabling monitoring group housed mice. The data show that increasing density from one female to two males, and further to three male or female mice had the same effect size on EAD (˜2). In contrast, the EAD deviated significantly from this stepwise increase with 4 mice per cage, suggesting a crowdedness stress inducing sex specific adaptations. We conclude that informative metrics on rest and PA can be automatically extracted from the raw data flow in near-real time (< 1 hrs). As discussed, these metrics relay useful longitudinal information to those that use or care for the animals.

Galactic Cosmic Irradiation Alters Acute and Delayed Species-Typical Behavior in Male and Female Mice

Exposure to space galactic cosmic radiation is a principal consideration for deep space missions. While the effects of space irradiation on the nervous system are not fully known, studies in animal models have shown that exposure to ionizing radiation can cause neuronal damage and lead to downstream cognitive and behavioral deficits. Cognitive health implications put humans and missions at risk, and with the upcoming Artemis missions in which female crew will play a major role, advance critical analysis of the neurological and performance responses of male and female rodents to space radiation is vital. Here, we tested the hypothesis that simulated Galactic Cosmic Radiation (GCRSim) exposure disrupts species-typical behavior in mice, including burrowing, rearing, grooming, and nest-building that depend upon hippocampal and medial prefrontal cortex circuitry. Behavior comprises a remarkably well-integrated representation of the biology of the whole animal that informs overall neural and physiological status, revealing functional impairment. We conducted a systematic dose-response analysis of mature (6-month-old) male and female mice exposed to either 5, 15, or 50 cGy 5-ion GCRSim (H, Si, He, O, Fe) at the NASA Space Radiation Laboratory (NSRL). Behavioral performance was evaluated at 72 h (acute) and 91-days (delayed) postradiation exposure. Specifically, species-typical behavior patterns comprising burrowing, rearing, and grooming as well as nest building were analyzed. A Neuroscore test battery (spontaneous activity, proprioception, vibrissae touch, limb symmetry, lateral turning, forelimb outstretching, and climbing) was performed at the acute timepoint to investigate early sensorimotor deficits postirradiation exposure. Nest construction, a measure of neurological and organizational function in rodents, was evaluated using a five-stage Likert scale 'Deacon' score that ranged from 1 (a low score where the Nestlet is untouched) to 5 (a high score where the Nestlet is completely shredded and shaped into a nest). Differential acute responses were observed in females relative to males with respect to species-typical behavior following 15 cGy exposure while delayed responses were observed in female grooming following 50 cGy exposure. Significant sex differences were observed at both timepoints in nest building. No deficits in sensorimotor behavior were observed via the Neuroscore. This study revealed subtle, sexually dimorphic GCRSim exposure effects on mouse behavior. Our analysis provides a clearer understanding of GCR dose effects on species typical, sensorimotor and organizational behaviors at acute and delayed timeframes postirradiation, thereby setting the stage for the identification of underlying cellular and molecular events.

The impact of stress and anesthesia on animal models of infectious disease

Stress and general anesthesia have an impact on the functional response of the organism due to the detrimental effects on cardiovascular, immunological, and metabolic function, which could limit the organism's response to an infectious event. Animal studies have formed an essential step in understanding and mitigating infectious diseases, as the complexities of physiology and immunity cannot yet be replicated in vivo. Using animals in research continues to come under increasing societal scrutiny, and it is therefore crucial that the welfare of animals used in disease research is optimized to meet both societal expectations and improve scientific outcomes. Everyday management and procedures in animal studies are known to cause stress, which can not only cause poorer welfare outcomes, but also introduces variables in disease studies. Whilst general anesthesia is necessary at times to reduce stress and enhance animal welfare in disease research, evidence of physiological and immunological disruption caused by general anesthesia is increasing. To better understand and quantify the effects of stress and anesthesia on disease study and welfare outcomes, utilizing the most appropriate animal monitoring strategies is imperative. This article aims to analyze recent scientific evidence about the impact of stress and anesthesia as uncontrolled variables, as well as reviewing monitoring strategies and technologies in animal models during infectious diseases.

Excessive rest time during active phase is reliably detected in a mouse model of myotonic dystrophy type 1 using home cage monitoring

Myotonic dystrophy type 1 (DM1) is a dominantly inherited neuromuscular disease caused by the abnormal expansion of CTG-repeats in the 3'-untranslated region of the Dystrophia Myotonica Protein Kinase (DMPK) gene, characterized by multisystemic symptoms including muscle weakness, myotonia, cardio-respiratory problems, hypersomnia, cognitive dysfunction and behavioral abnormalities. Sleep-related disturbances are among the most reported symptoms that negatively affect the quality of life of patients and that are present in early and adult-onset forms of the disease. DMSXL mice carry a mutated human DMPK transgene containing >1,000 CTGrepeats, modeling an early onset, severe form of DM1. They exhibit a pathologic neuromuscular phenotype and also synaptic dysfunction resulting in neurological and behavioral deficits similar to those observed in patients. Additionally, they are underweight with a very high mortality within the first month after birth presenting several welfare issues. To specifically explore sleep/rest-related behaviors of this frail DM1 mouse model we used an automated home cage-based system that allows 24/7 monitoring of their activity non-invasively. We tested male and female DMSXL mice and their wild-type (WT) littermates in Digital Ventilated Cages (DVCR) assessing activity and rest parameters on day and night for 5 weeks. We demonstrated that DMSXL mice show reduced activity and regularity disruption index (RDI), higher percentage of zero activity per each hour and longer periods of rest during the active phase compared to WT. This novel rest-related phenotype in DMSXL mice, assessed unobtrusively, could be valuable to further explore mechanisms and potential therapeutic interventions to alleviate the very common symptom of excessive daytime sleepiness in DM1 patients.

A protocol to induce systemic autophagy and increase energy metabolism in mice using PEGylated arginine deiminase

Obesity is a prevalent metabolic disorder worldwide. Here, we describe a comprehensive protocol using pegylated arginine deiminase (ADI-EPG 20) to apply the concept that arginine depletion induces systemic autophagy to drive whole-body energy metabolism and weight loss in mice. We detail the steps for cohort setup, mouse husbandry, and treatment and provide expected results under these conditions. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022a, 2022b).

The prevention of home-cage grid climbing affects muscle strength in mice

Experimenters and treatment methods are the major contributors to data variability in behavioral neuroscience. However, home cage characteristics are likely associated with data variability. Mice housed in breeding cages spontaneously exhibit behavioral patterns such as biting into the wire grid and climbing on the grid lid. We aimed to clarify the effect of covering the stainless steel wire grid lid in commonly used home cage with Plexiglas to prevent climbing on muscle strength in mice. Furthermore, we investigated the effects of climbing prevention on activity and anxiety-like behavior, and the impact of climbing prevention during the postnatal development period and adulthood on muscle strength. Muscle strength, anxiety-like behavior, and locomotor activity were assessed by a battery of tests (wire hang, suspension, grip strength, rotarod, elevated-plus maze, and open field tests). Mice prevented from climbing the wire grid during postnatal development displayed lower muscle strength than those able to climb. Moreover, mice prevented from climbing for 3 weeks following maturity had weakened muscles. The muscle strength was decreased with 3 weeks of climbing prevention in even 1-year-old mice. In summary, the stainless steel wire grid in the home cage contributed to the development and maintenance of muscle strength in mice.

Voluntary wheel running behaviour as a tool to assess the severity in a mouse pancreatic cancer model

Laboratory animals frequently undergo routine experimental procedures such as handling, restraining and injections. However, as a known source of stress, these procedures potentially impact study outcome and data quality. In the present study, we, therefore, performed an evidence-based severity assessment of experimental procedures used in a pancreatic cancer model including surgical tumour induction and subsequent chemotherapeutic treatment via repeated intraperitoneal injections. Cancer cell injection into the pancreas was performed during a laparotomy under general anaesthesia. After a four-day recovery phase, mice received either drug treatment (galloflavin and metformin) or the respective vehicle substances via daily intraperitoneal injections. In addition to clinical scoring, an automated home-cage monitoring system was used to assess voluntary wheel running (VWR) behaviour as an indicator of impaired well-being. After surgery, slightly elevated clinical scores and minimal body weight reductions, but significantly decreased VWR behaviour were observed. During therapy, body weight declined in response to chemotherapy, but not after vehicle substance injection, while VWR activity was decreased in both cases. VWR behaviour differed between treatment groups and revealed altered nightly activity patterns. In summary, by monitoring VWR a high impact of repeated injections on the well-being of mice was revealed and substance effects on well-being were distinguishable. However, no differences in tumour growth between treatment groups were observed. This might be due to the severity of the procedures uncovered in this study, as exaggerated stress responses are potentially confounding factors in preclinical studies. Finally, VWR was a more sensitive indicator of impairment than clinical scoring in this model.

Automated Home-Cage Monitoring During Acute Experimental Colitis in Mice

For ethical and legal reasons it is necessary to assess the severity of procedures in animal experimentation. To estimate the degree of pain, suffering, distress or lasting harm, objective methods that provide gradebale parameters need to be tested and validated for various models. In this context, automated home-cage monitoring becomes more important as a contactless, objective, continuous and non-invasive method. The aim of this study was to examine a recently developed large scale automated home-cage monitoring system (Digital Ventilated Cage, DVC^®) with regard to the applicability and added value for severity assessment in a frequently used acute colitis mouse model. Acute colitis was induced in female C57BL/6J mice by varying doses of DSS (1.5 and 2.5%), matched controls received water only (0%). Besides DVC^® activity monitoring and nest scoring, model specific parameters like body weight, clinical colitis score, and intestinal histo-pathology were used. In a second approach, we questioned whether DVC^® can be used to detect an influence of different handling methods on the behavior of mice. Therefore, we compared activity patterns of mice that underwent tunnel vs. tail handling for routine animal care procedures. In DSS treated mice, disease specific parameters confirmed induction of a graded colitis. In line with this, DVC^® revealed reduced activity in these animals. Furthermore, the system displayed stress-related activity changes due to the restraining procedures necessary in DSS-treatment groups. However, no significant differences between tunnel vs. tail handling procedures were detected. For further analysis of the data, a binary classifier was applied to categorize two severity levels (burdened vs. not burdened) based on activity and body weight. In all DSS-treatment groups data points were allocated to the burdened level, in contrast to a handling group. The fraction of "burdened" animals reflected well the course of colitis development. In conclusion, automated home-cage monitoring by DVC^® enabled severity assessment in a DSS-induced colitis model equally well as gold standard clinical parameters. In addition, it revealed changes in activity patterns due to routine handling procedures applied in experimental model work. This indicates that large scale home-cage monitoring can be integrated into routine severity assessment in biomedical research.

Machine Learning in Modeling of Mouse Behavior

Mouse behavior is a primary outcome in evaluations of therapeutic efficacy. Exhaustive, continuous, multiparametric behavioral phenotyping is a valuable tool for understanding the pathophysiological status of mouse brain diseases. Automated home cage behavior analysis produces highly granulated data both in terms of number of features and sampling frequency. Previously, we demonstrated several ways to reduce feature dimensionality. In this study, we propose novel approaches for analyzing 33-Hz data generated by CleverSys software. We hypothesized that behavioral patterns within short time windows are reflective of physiological state, and that computer modeling of mouse behavioral routines can serve as a predictive tool in classification tasks. To remove bias due to researcher decisions, our data flow is indifferent to the quality, value, and importance of any given feature in isolation. To classify day and night behavior, as an example application, we developed a data preprocessing flow and utilized logistic regression (LG), support vector machines (SVM), random forest (RF), and one-dimensional convolutional neural networks paired with long short-term memory deep neural networks (1DConvBiLSTM). We determined that a 5-min video clip is sufficient to classify mouse behavior with high accuracy. LG, SVM, and RF performed similarly, predicting mouse behavior with 85% accuracy, and combining the three algorithms in an ensemble procedure increased accuracy to 90%. The best performance was achieved by combining the 1DConv and BiLSTM algorithms yielding 96% accuracy. Our findings demonstrate that computer modeling of the home-cage ethome can clearly define mouse physiological state. Furthermore, we showed that continuous behavioral data can be analyzed using approaches similar to natural language processing. These data provide proof of concept for future research in diagnostics of complex pathophysiological changes that are accompanied by changes in behavioral profile.

Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives

The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more "natural" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus PhenoTyper® home-cages and the video-based tracking software, EthoVision® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a "home-cage", we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new developments. Examples include the automated recognition of specific behaviors, unambiguous tracking of individuals in a social context, the development of more animal-centered measures of behavior and ways of dealing with large datasets. Together, we advocate that by embracing standardized home-cage monitoring platforms like the PhenoTyper, we are poised to directly assess issues pertaining to reproducibility, and more importantly, measure features of rodent behavior under more ethologically relevant scenarios.

Automated home-cage for the evaluation of innate non-reflexive pain behaviors in a mouse model of inflammatory pain

The failure to develop analgesic drugs is attributed not only to the complex and diverse pathophysiology of pain in humans but also to the poor experimental design and poor preclinical assessment of pain. Although considerable efforts have been devoted to overcoming the relevant problems, many features of the behavioral pain assessment remain to be characterized. For example, a decreased locomotor activity as a common presentation of pain-like behavior has yet to be described. Studies on mice experimentally induced with carrageenan have provided opportunities to explore pain-related behaviors in automated home-cage monitoring. Through this approach, the locomotor activities of mice with carrageenan-induced inflammatory pain can be precisely and objectively captured. Here, we found that the mobile behaviors of mice reduced, and their immobility increased, indicating that carrageenan induction in mice caused a significant decrease in locomotor activity. These non-reflexive pain behaviors were strongly correlated with the reflexive pain behaviors measured via von Frey and plantar tests. Furthermore, the pharmacological intervention using indomethacin improved the locomotor activity of mice with carrageenan-induced pain. Thus, the analysis of the locomotor activity in automated home-cage monitoring is useful for studying the behavioral analgesia and the pharmacological screening of analgesic drugs. The combined evaluation of reflexive and non-reflexive pain behaviors enhances the translational utility of preclinical pain research in rodents.

Cage-lid hanging behavior as a translationally relevant measure of pain in mice

ABSTRACT

The development of new analgesic drugs has been hampered by the inability to translate preclinical findings to humans. This failure is due in part to the weak connection between commonly used pain outcome measures in rodents and the clinical symptoms of chronic pain. Most rodent studies rely on the use of experimenter-evoked measures of pain and assess behavior under ethologically unnatural conditions, which limits the translational potential of preclinical research. Here, we addressed this problem by conducting an unbiased, prospective study of behavioral changes in mice within a natural homecage environment using conventional preclinical pain assays. Unexpectedly, we observed that cage-lid hanging, a species-specific elective behavior, was the only homecage behavior reliably impacted by pain assays. Noxious stimuli reduced hanging behavior in an intensity-dependent manner, and the reduction in hanging could be restored by analgesics. Finally, we developed an automated approach to assess hanging behavior. Collectively, our results indicate that the depression of hanging behavior is a novel, ethologically valid, and translationally relevant pain outcome measure in mice that could facilitate the study of pain and analgesic development.

Major oscillations in spontaneous home-cage activity in C57BL/6 mice housed under constant conditions

The mouse is the most important mammalian model in life science research and the behavior of the mouse is a key read-out of experimental interventions and genetic manipulations. To serve this purpose a solid understanding of the mouse normal behavior is a prerequisite. Using 14-19 months of cumulative 24/7 home-cage activity recorded with a non-intrusive technique, evidence is here provided for a highly significant circannual oscillation in spontaneous activity (1-2 SD of the mean, on average 65% higher during peak of highs than lows; P = 7E-50) of male and female C57BL/6 mice held under constant conditions. The periodicity of this hitherto not recognized oscillation is in the range of 2-4 months (average estimate was 97 days across cohorts of cages). It off-sets responses to environmental stimuli and co-varies with the feeding behavior but does not significantly alter the preference for being active during the dark hours. The absence of coordination of this rhythmicity between cages with mice or seasons of the year suggest that the oscillation of physical activity is generated by a free-running intrinsic oscillator devoid of external timer. Due to the magnitude of this rhythmic variation it may be a serious confounder in experiments on mice if left unrecognized.

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.

The Promise of Automated Home-Cage Monitoring in Improving Translational Utility of Psychiatric Research in Rodents

Large number of promising preclinical psychiatric studies in rodents later fail in clinical trials, raising concerns about the efficacy of this approach to generate novel pharmacological interventions. In this mini-review we argue that over-reliance on behavioral tests that are brief and highly sensitive to external factors play a critical role in this failure and propose that automated home-cage monitoring offers several advantages that will increase the translational utility of preclinical psychiatric research in rodents. We describe three of the most commonly used approaches for automated home cage monitoring in rodents [e.g., operant wall systems (OWS), computerized visual systems (CVS), and automatic motion sensors (AMS)] and review several commercially available systems that integrate the different approaches. Specific examples that demonstrate the advantages of automated home-cage monitoring over traditional tests of anxiety, depression, cognition, and addiction-like behaviors are highlighted. We conclude with recommendations on how to further expand this promising line of preclinical research.

Selective Knockout of the Vesicular Monoamine Transporter 2 (Vmat2) Gene in Calbindin2/Calretinin-Positive Neurons Results in Profound Changes in Behavior and Response to Drugs of Abuse

The vesicular monoamine transporter 2 (VMAT2) has a range of functions in the central nervous system, from sequestering toxins to providing conditions for the quantal release of monoaminergic neurotransmitters. Monoamine signaling regulates diverse functions from arousal to mood, movement, and motivation, and dysregulation of VMAT2 function is implicated in various neuropsychiatric diseases. While all monoamine-releasing neurons express the Vmat2 gene, only a subset is positive for the calcium-binding protein Calbindin 2 (Calb2; aka Calretinin, 29 kDa Calbindin). We recently showed that about half of the dopamine neurons in the mouse midbrain are positive for Calb2 and that Calb2 is an early developmental marker of midbrain dopamine cells. Calb2-positive neurons have also been identified in other monoaminergic areas, yet the role of Calb2-positive monoaminergic neurons is poorly understood. To selectively address the impact of Calb2-positive monoaminergic neurons in behavioral regulation, we took advantage of the Cre-LoxP system to create a new conditional knockout (cKO) mouse line in which Vmat2 expression is deleted selectively in Calb2-Cre-positive neurons. In this Vmat2^{lox/lox;Calb2−Cre} cKO mouse line, gene targeting of Vmat2 was observed in several distinct monoaminergic areas. By comparing control and cKO mice in a series of behavioral tests, specific dissimilarities were identified. In particular, cKO mice were smaller than control mice and showed heightened sensitivity to the stereotypy-inducing effects of amphetamine and slight reductions in preference toward sucrose and ethanol, as well as a blunted response in the elevated plus maze test. These data uncover new knowledge about the role of genetically defined subtypes of neurons in the brain’s monoaminergic systems.

Three Pillars of Automated Home-Cage Phenotyping of Mice: Novel Findings, Refinement, and Reproducibility Based on Literature and Experience

Animal models of neurodegenerative and neuropsychiatric disorders require extensive behavioral phenotyping. Currently, this presents several caveats and the most important are: (i) rodents are nocturnal animals, but mostly tested during the light period; (ii) the conventional behavioral experiments take into consideration only a snapshot of a rich behavioral repertoire; and (iii) environmental factors, as well as experimenter influence, are often underestimated. Consequently, serious concerns have been expressed regarding the reproducibility of research findings on the one hand, and appropriate welfare of the animals (based on the principle of 3Rs-reduce, refine and replace) on the other hand. To address these problems and improve behavioral phenotyping in general, several solutions have been proposed and developed. Undisturbed, 24/7 home-cage monitoring (HCM) is gaining increased attention and popularity as demonstrating the potential to substitute or complement the conventional phenotyping methods by providing valuable data for identifying the behavioral patterns that may have been missed otherwise. In this review, we will briefly describe the different technologies used for HCM systems. Thereafter, based on our experience, we will focus on two systems, IntelliCage (NewBehavior AG and TSE-systems) and Digital Ventilated Cage (DVC®, Tecniplast)-how they have been developed and applied during recent years. Additionally, we will touch upon the importance of the environmental/experimenter artifacts and propose alternative suggestions for performing phenotyping experiments based on the published evidence. We will discuss how the integration of telemetry systems for deriving certain physiological parameters can help to complement the description of the animal model to offer better translation to human studies. Ultimately, we will discuss how such HCM data can be statistically interpreted and analyzed.

Not all mice are alike: Mixed-strain housing alters social behaviour

The use of millions of mice in scientific studies worldwide emphasises the continuing need for a reduction of sample sizes, however, not at the expense of scientific validity. Split-plot designs have been suggested to enhance statistical power while allowing a reduction of animal numbers in comparison to traditional experimental designs. Recently, a promising approach of a split-plot design has been implemented and proven useful using mixed-strain housing of at least three different mouse strains. However, the impact of co-housing different strains of mice in one cage on animal welfare has still to be defined. This study aimed at comparing the effects of mixed-strain and same-strain housing of female C57BL/6J and DBA/2N mice on welfare and behaviour in two experimental phases. In a first phase, mice were housed in either mixed- or same-strain pairs. Home cage behaviour, activity rhythm, body weight, and faecal corticosterone metabolites were assessed. Furthermore, tests for anxiety-like and exploratory behaviour as well as spatial learning were performed. In a second phase, sociability was investigated in newly formed mixed-strain quartets. Mixed-strain housing did not induce alterations in anxiety, locomotion, learning, stereotypic behaviour, and stress hormone levels. However, changes in social behaviours and activity rhythm were observed. Increased agonistic and decreased socio-positive behaviours might point towards mild impacts on welfare in C57BL/6J mice under co-housing conditions. Altogether, scientific research may greatly benefit from co-housing mice of different strains within the same cages (e.g. for the realisation of a split-plot design), provided that strains are carefully selected for compatibility.

Phenotyping in Mice Using Continuous Home Cage Monitoring and Ultrasonic Vocalization Recordings

Over the last century, the study of mouse behavior has uncovered insights into brain molecular mechanisms while revealing potential causes of many neurological disorders. To this end, researchers have widely exploited the use of mutant strains, including those generated in mutagenesis screens and those produced using increasingly sophisticated genome engineering technologies. It is now relatively easy to access mouse models carrying alleles that faithfully recapitulate changes found in human patients or bearing variants of genes that provide data on those genes' functions. Concurrent with these developments has been an appreciation of the limitations of some current testing platforms, especially those monitoring complex behaviors. Out-of-cage observational testing is useful in describing overt persistent phenotypes but risks missing sporadic or intermittent events. Furthermore, measuring the progression of a phenotype, potentially over many months, can be difficult while relying on assays that may be susceptible to changes in the testing environment. In recent years, there has also been increasing awareness that measurement of behaviors in isolation can be limiting, given that mice attempt to hide behavioral cues of vulnerability. To overcome these limitations, laboratory animal science is capitalizing on progress in data capture and processing expertise. Moreover, as additional recording modes become commonplace, ultrasonic vocalization recording is an appealing focus, as mice use vocalizations in various social contexts. Using video and audio technologies, we record the voluntary, unprovoked behaviors and vocalizations of mice in social groups. Adoption of these approaches is undoubtedly set to increase, as they capture the round-the-clock behavior of mouse strains. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Continuous recording of home cage activity using the Home Cage Analyzer (HCA) system Support Protocol: Subcutaneous insertion of a radio frequency identification microchip in the inguinal area Basic Protocol 2: Continuous recording of mouse ultrasonic vocalizations in the home cage.

Temporal dissociation of phencyclidine: Induced locomotor and social alterations in rats using an automated homecage monitoring system - implications for the 3Rs and preclinical drug discovery

BACKGROUND

Rodent behavioural assays are widely used to delineate the mechanisms of psychiatric disorders and predict the efficacy of drug candidates. Conventional behavioural paradigms are restricted to short time windows and involve transferring animals from the homecage to unfamiliar apparatus which induces stress. Additionally, factors including environmental perturbations, handling and the presence of an experimenter can impact behaviour and confound data interpretation. To improve welfare and reproducibility these issues must be resolved. Automated homecage monitoring offers a more ethologically relevant approach with reduced experimenter bias.

AIM

To evaluate the effectiveness of an automated homecage system at detecting locomotor and social alterations induced by phencyclidine (PCP) in group-housed rats. PCP is an N-methyl-D-aspartate (NMDA) receptor antagonist commonly utilised to model aspects of schizophrenia.

METHODS

Rats housed in groups of three were implanted with radio frequency identification (RFID) tags. Each homecage was placed over a RFID reader baseplate for the automated monitoring of the social and locomotor activity of each individual rat. For all rats, we acquired homecage data for 24 h following administration of both saline and PCP (2.5 mg/kg).

RESULTS

PCP resulted in significantly increased distance travelled from 15 to 60 min post injection. Furthermore, PCP significantly enhanced time spent isolated from cage mates and this asociality occured from 60 to 105 min post treatment.

CONCLUSIONS

Unlike conventional assays, in-cage monitoring captures the temporal duration of drug effects on multiple behaviours in the same group of animals. This approach could benefit psychiatric preclinical drug discovery through improved welfare and increased between-laboratory replicability.

Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

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.

Studies on long term behavioural changes in group-housed rat models of brain and spinal cord injury using an automated home cage recording system

BACKGROUND

Neurotrauma patients face major neurological sequelae. The failure in the preclinical-to-clinical translation of candidate therapies could be due to poor evaluation of rodent behaviours after neurotrauma.

NEW METHOD

A home cage automated system was used to study the long term behaviour of individual rats with traumatic brain injury (TBI), spinal cord injury (SCI) and non-CNS injured controls, whilst group-housed in their home cages. Naïve rats were used as baseline controls. Automated locomotor activity and body temperature recordings were carried out 24 h /day for 3 days/week during 12 weeks post-injury. Behavioural patterns, including aggression, rearing, grooming, feeding and drinking were analysed from automated video recordings during week 1, 6 and 12.

RESULTS

SCI animals showed a lower locomotor activity compared to TBI or control animals during light and dark phases. TBI animals showed a higher aggression during the dark phase in the first week post-injury compared to SCI or control animals. Individual grooming and rearing were reduced in SCI animals compared to TBI and control animals in the first week post-injury during the dark phase. No differences in drinking or feeding were detected between groups. Locomotor activity did not differ between naïve male and female rats, but body temperature differ between light and dark phases for both.

STANDARD METHODS

Injury severity was compared to standard SCI and TBI behaviour scores (BBB and mNSS, respectively) and histological analysis.

CONCLUSIONS

This study demonstrates the practical benefits of using a non-intrusive automated home cage recording system to observe long term individual behaviour of group-housed SCI and TBI rats.

Non-intrusive high throughput automated data collection from the home cage

Automated home cage monitoring represents a key technology to collect animal activity information directly from the home cage. The availability of 24/7 cage data enables extensive and quantitative assessment of mouse behavior and activity over long periods of time than possible otherwise. When home cage monitoring is performed directly at the home cage rack, it is possible to leverage additional advantages, including, e.g., partial (or total) reduction of animal handling, no need for setting up external data collection system as well as not requiring dedicated labs and personnel to perform tests. In this work we introduce a home cage-home rack monitoring system that is capable of continuously detecting spontaneous animal activity occurring in the home cage directly from the home cage rack. The proposed system is based on an electrical capacitance sensing technology that enables non-intrusive and continuous home cage monitoring. We then present a few animal activity metrics that are validated via comparison against a video camera-based tracking system. The results show that the proposed home-cage monitoring system can provide animal activity metrics that are comparable to the ones derived via a conventional video tracking system, with the advantage of system scalability, limited amount of both data generated and computational capabilities required to derive metrics.

Towards large scale automated cage monitoring - Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique

BACKGROUND AND AIMS

Automated recording of laboratory animal's home cage behavior is receiving increasing attention since such non-intruding surveillance will aid in the unbiased understanding of animal cage behavior potentially improving animal experimental reproducibility.

MATERIAL AND METHODS

Here we investigate activity of group held female C57BL/6J mice (mus musculus) housed in standard Individually Ventilated Cages across three test-sites: Consiglio Nazionale delle Ricerche (CNR, Rome, Italy), The Jackson Laboratory (JAX, Bar Harbor, USA) and Karolinska Insititutet (KI, Stockholm, Sweden). Additionally, comparison of female and male C57BL/6J mice was done at KI. Activity was recorded using a capacitive-based sensor placed non-intrusively on the cage rack under the home cage collecting activity data every 250 msec, 24/7. The data collection was analyzed using non-parametric analysis of variance for longitudinal data comparing sites, weekdays and sex.

RESULTS

The system detected an increase in activity preceding and peaking around lights-on followed by a decrease to a rest pattern. At lights off, activity increased substantially displaying a distinct temporal variation across this period. We also documented impact on mouse activity that standard animal handling procedures have, e.g. cage-changes, and show that such procedures are stressors impacting in-cage activity. These key observations replicated across the three test-sites, however, it is also clear that, apparently minor local environmental differences generate significant behavioral variances between the sites and within sites across weeks. Comparison of gender revealed differences in activity in the response to cage-change lasting for days in male but not female mice; and apparently also impacting the response to other events such as lights-on in males. Females but not males showed a larger tendency for week-to-week variance in activity possibly reflecting estrous cycling.

CONCLUSIONS

These data demonstrate that home cage monitoring is scalable and run in real time, providing complementary information for animal welfare measures, experimental design and phenotype characterization.

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.

Behavioural characterization of C57BL/6N and BALB/c female mice in social home cage - Effect of mixed housing in complex environment

Developing reliable mouse models for social behaviour is challenging. Different tests have been proposed, but most of them consist of rather artificial confrontations of unfamiliar mice in novel arenas or are relying on social stress induced by aggressive conspecifics. Natural social interaction in home cage in laboratory has not been investigated well. IntelliCage is a fully automated home-cage system, where activity of the group-housed mice can be monitored along with various cognitive tasks. Here we report the behavioural profile of C57BL/6N (B6) and BALB/c (BALB) female mice in IntelliCage when separated by strain, followed by monitoring of activity and formation of 'home-base' after mixing two strains. For that purpose, 3 cages were connected. Significant differences between the strains were established in baseline behaviour in conventional tests and in IntelliCage. The B6 mice showed reduced anxiety-like behaviour in open field and light-dark box, slightly enhanced exploratory activity in IntelliCage during initial adaptation and clearly distinct circadian activity. Mixing of two strains resulted in reduction of body weight and anhedonia in B6 mice. In addition, the B6 mice showed clear preference to previous home-cage, and formed a new home-base faster than BALB mice. In contrast, BALB mice showed enhanced activity and moving between the cages without showing any preference to previous home-cage. It could be argued that social challenge caused changes in both strains and different coping styles are responsible for behavioural manifestations. Altogether, this approach could be useful in modelling and validating mouse models for disorders with disturbed social behaviour.

Individualized tracking of self-directed motor learning in group-housed mice performing a skilled lever positioning task in the home cage

Skilled forelimb function in mice is traditionally studied through behavioral paradigms that require extensive training by investigators and are limited by the number of trials individual animals are able to perform within a supervised session. We developed a skilled lever positioning task that mice can perform within their home cage. The task requires mice to use their forelimb to precisely hold a lever mounted on a rotary encoder within a rewarded position to dispense a water reward. A Raspberry Pi microcomputer is used to record lever position during trials and to control task parameters, thus making this low-footprint apparatus ideal for use within animal housing facilities. Custom Python software automatically increments task difficulty by requiring a longer hold duration, or a more accurate hold position, to dispense a reward. The performance of individual animals within group-housed mice is tracked through radio-frequency identification implants, and data stored on the microcomputer may be accessed remotely through an active internet connection. Mice continuously engage in the task for over 2.5 mo and perform ~500 trials/24 h. Mice required ~15,000 trials to learn to hold the lever within a 10° range for 1.5 s and were able to further refine movement accuracy by limiting their error to a 5° range within each trial. These results demonstrate the feasibility of autonomously training group-housed mice on a forelimb motor task. This paradigm may be used in the future to assess functional recovery after injury or cortical reorganization induced by self-directed motor learning. NEW & NOTEWORTHY We developed a low-cost system for fully autonomous training of group-housed mice on a forelimb motor task. We demonstrate the feasibility of tracking both end-point, as well as kinematic performance of individual mice, with each performing thousands of trials over 2.5 mo. The task is run and controlled by a Raspberry Pi microcomputer, which allows for cages to be monitored remotely through an active internet connection.

Are rodent models of Parkinson's disease behaving as they should?

In recent years our understanding of Parkinson's disease has expanded both in terms of pathological hallmarks as well as relevant genetic influences. In parallel with the aetiological discoveries a multitude of PD animal models have been established. The vast majority of these are rodent models based on environmental, genetic and mechanistic insight. A major challenge in many of these models is their ability to only recapitulate some of the complex disease features seen in humans. Although symptom alleviation and clinical signs are of utmost importance in therapeutic research many of these models lack comprehensive behavioural testing. While non-motor symptoms become increasingly important as early diagnostic markers in PD, they are poorly characterized in rodents. In this review we look at well-established and more recent animal models of PD in terms of behavioural characterization and discuss how they can best contribute to progression in Parkinson's research.

Home-cage anxiety levels in a transgenic rat model for Spinocerebellar ataxia type 17 measured by an approach-avoidance task: The light spot test

BACKGROUND

Measuring anxiety in a reliable manner is essential for behavioural phenotyping of rodent models such as the rat model for Spinocerebellar ataxia type 17 (SCA17) where anxiety is reported in patients. An automated tool for assessing anxiety within the home cage can minimize human intervention, stress of handling, transportation and novelty.

NEW METHOD

We applied the anxiety test "light spot" (LS) (white led directed at the food-hopper) to our transgenic SCA17 rat model in the PhenoTyper 4500^® to extend the knowledge of this automated tool for behavioural phenotyping and to verify an anxiety-like phenotype at three different disease stages for use in future therapeutic studies.

RESULTS

Locomotor activity was increased in SCA17 rats at 6 and 9 months during the first 15min of the LS, potentially reflecting increased risk assessment. Both genotypes responded to the test with lower duration in the LS zone and higher time spent inside the shelter compared to baseline.

COMPARISON WITH EXISTING METHODS

We present the first data of a rat model subjected to the LS. The LS can be considered more biologically relevant than a traditional test as it measures anxiety in a familiar situation.

CONCLUSIONS

The LS successfully evoked avoidance and shelter-seeking in rats. SCA17 rats showed a stronger approach-avoidance conflict reflected by increased activity in the area outside the LS. This home cage test, continuously monitoring pre- and post-effects, provides the opportunity for in-depth analysis, making it a potentially useful tool for detecting subtle or complex anxiety-related traits in rodents.

The 9 to 5 Rodent - Time for Change? Scientific and animal welfare implications of circadian and light effects on laboratory mice and rats

Rodents, particularly rats and mice, are the most commonly used laboratory animals and are extensively used in neuroscience research, including as translational models for human disorders. It is common practice to carry out scientific procedures on rats and mice during the daytime, which is the inactive period for these nocturnal species. However, there is increasing evidence for circadian and light-induced effects on rodent physiology and behaviour which may affect the validity of results obtained from mice and rats in neuroscience studies. For example, testing animals during their inactive periods may produce abnormal results due to cognitive deficits, lack of motivation to perform the task or stress from being disturbed during the resting period. In addition, conducting procedures during an animal's resting period may also pose an animal welfare issue, as procedures may be experienced as more stressful than if these were done during the active phase. In this paper we set out the need to consider the impact of time of day and lighting conditions, when scientific procedures or routine husbandry are performed, on both the welfare of mice and rats used in neuroscience research and on data quality. Wherever possible, husbandry and experimental procedures should be conducted at times of day when the animals would be active, and under naturalistic lighting conditions, to minimise stress and maximise data quality and translatability.

Automating mouse weighing in group homecages with Raspberry Pi micro-computers

BACKGROUND

Operant training systems make use of water or food restriction and make it necessary to weigh animals to ensure compliance with experimental endpoints. In other applications periodic weighing is necessary to assess drug side-effects, or as an endpoint in feeding experiments. Periodic weighing while essential can disrupt animal circadian rhythms and social structure.

NEW METHOD

Automatic weighing system within paired mouse homecages. Up to 10 mice freely move between two cages (28×18×9cm) which were connected by a weighing chamber mounted on a load cell. Each mouse was identified using an RFID tag placed under the skin of the neck. A single-board computer (Raspberry Pi; RPi) controls the task, logging RFID tag, load cell weights, and time stamps from each RFID detection until the animal leaves the chamber. Collected data were statistically analyzed to estimate mouse weights. We anticipate integration with tasks where automated imaging or behaviour is assessed in homecages.

RESULTS

Mice frequently move between the two cages, an average of 42+-16 times/day/mouse at which time we obtained weights. We report accurate determination of mouse weight and long term monitoring over 53days. Comparison with existing methods Although commercial systems are available for automatically weighing rodents, they only work with single animals, or are not open source nor cost effective for specific custom application.

CONCLUSIONS

This automated system permits automated weighing of mice ∼40 times per day. The system employs inexpensive hardware and open-source Python code.

Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools

•

Automated assessment of mouse home-cage behaviour is robust and reliable.

•

Analysis over multiple light/dark cycles improves ability to classify behaviours.

•

Combined RFID and video analysis enables home-cage analysis in group housed animals.

An important factor in reducing variability in mouse test outcomes has been to develop assays that can be used for continuous automated home cage assessment. Our experience has shown that this has been most evidenced in long-term assessment of wheel-running activity in mice. Historically, wheel-running in mice and other rodents have been used as a robust assay to determine, with precision, the inherent period of circadian rhythms in mice. Furthermore, this assay has been instrumental in dissecting the molecular genetic basis of mammalian circadian rhythms. In teasing out the elements of this test that have determined its robustness – automated assessment of an unforced behaviour in the home cage over long time intervals – we and others have been investigating whether similar test apparatus could be used to accurately discriminate differences in distinct behavioural parameters in mice. Firstly, using these systems, we explored behaviours in a number of mouse inbred strains to determine whether we could extract biologically meaningful differences. Secondly, we tested a number of relevant mutant lines to determine how discriminative these parameters were. Our findings show that, when compared to conventional out-of-cage phenotyping, a far deeper understanding of mouse mutant phenotype can be established by monitoring behaviour in the home cage over one or more light:dark cycles.

Use of electric field sensors for recording respiration, heart rate, and stereotyped motor behaviors in the rodent home cage

BACKGROUND

Numerous environmental and genetic factors can contribute significantly to behavioral and cardiorespiratory variability observed experimentally. Affordable technologies that allow for noninvasive home cage capture of physio-behavioral variables should enhance understanding of inter-animal variability including after experimental interventions.

NEW METHOD

We assessed whether EPIC electric field sensors (Plessey Semiconductors) embedded within or attached externally to a rodent's home cage could accurately record respiration, heart rate, and motor behaviors.

COMPARISON WITH EXISTING METHODS

Current systems for quantification of behavioral variables require expensive specialty equipment, while measures of respiratory and heart rate are often provided by surgically implanted or chronically affixed devices.

RESULTS

Sensors accurately encoded imposed sinusoidal changes in electric field tested at frequencies ranging from 0.5-100Hz. Mini-metronome arm movements were easily detected, but response magnitude was highly distance dependent. Sensors accurately reported respiration during whole-body plethysmography. In anesthetized rodents, PVC tube-embedded sensors provided accurate mechanical detection of both respiratory and heart rate. Comparable success was seen in naturally behaving animals at rest or sleeping when sensors were attached externally. Video-verified motor behaviors (sniffing, grooming, chewing, and rearing) were detectable and largely separable by their characteristic voltage fluctuations. Larger movement-related events had comparably larger voltage dynamics that easily allowed for a broad approximation of overall motor activity. Spectrograms were used to quickly depict characteristic frequencies in long-lasting recordings, while filtering and thresholding software allowed for detection and quantification of movement-related physio-behavioral events.

CONCLUSIONS

EPIC electric field sensors provide a means for affordable non-contact home cage detection of physio-behavioral variables.

Differential Effectiveness of Clinically-Relevant Analgesics in a Rat Model of Chemotherapy-Induced Mucositis

Chemotherapy-induced intestinal mucositis is characterized by pain and a pro-inflammatory tissue response. Rat models are frequently used in mucositis disease investigations yet little is known about the presence of pain in these animals, the ability of analgesics to ameliorate the condition, or the effect that analgesic administration may have on study outcomes. This study investigated different classes of analgesics with the aim of determining their analgesic effects and impact on research outcomes of interest in a rat model of mucositis. Female DA rats were allocated to 8 groups to include saline and chemotherapy controls (n = 8). Analgesics included opioid derivatives (buprenorphine; 0.05mg/kg and tramadol 12.5mg/kg) and NSAID (carprofen; 15mg/kg) in combination with either saline or 5-Fluorouracil (5-FU; 150mg/kg). Research outcome measures included daily clinical parameters, pain score and gut histology. Myeloperoxidase assay was performed to determine gut inflammation. At the dosages employed, all agents had an analgesic effect based on behavioural pain scores. Jejunal myeloperoxidase activity was significantly reduced by buprenorphine and tramadol in comparison to 5-FU control animals (53%, p = 0.0004 and 58%, p = 0.0001). Carprofen had no ameliorating effect on myeloperoxidase levels. None of the agents reduced the histological damage caused by 5-FU administration although tramadol tended to increase villus length even when administered to healthy animals. These data provide evidence that carprofen offers potential as an analgesic in this animal model due to its pain-relieving efficacy and minimal effect on measured parameters. This study also supports further investigation into the mechanism and utility of opioid agents in the treatment of chemotherapy-induced mucositis.

Animal models of chronic pain: Advances and challenges for clinical translation

Chronic pain is a global problem that has reached epidemic proportions. An estimated 20% of adults suffer from pain, and another 10% are diagnosed with chronic pain each year (Goldberg and McGee, ). Despite the high prevalence of chronic pain (an estimated 1.5 billion people are afflicted worldwide), much remains to be understood about the underlying causes of this condition, and there is an urgent requirement for better pain therapies. The discovery of novel targets and the development of better analgesics rely on an assortment of preclinical animal models; however, there are major challenges to translating discoveries made in animal models to realized pain therapies in humans. This review discusses common animal models used to recapitulate clinical chronic pain conditions (such as neuropathic, inflammatory, and visceral pain) and the methods for assessing the sensory and affective components of pain in animals. We also discuss the advantages and limitations of modeling chronic pain in animals as well as highlighting strategies for improving the predictive validity of preclinical pain studies. © 2016 Wiley Periodicals, Inc.

Analysis of Individual Mouse Activity in Group Housed Animals of Different Inbred Strains using a Novel Automated Home Cage Analysis System

Central nervous system disorders such as autism as well as the range of neurodegenerative diseases such as Huntington's disease are commonly investigated using genetically altered mouse models. The current system for characterizing these mice usually involves removing the animals from their home-cage environment and placing them into novel environments where they undergo a battery of tests measuring a range of behavioral and physical phenotypes. These tests are often only conducted for short periods of times in social isolation. However, human manifestations of such disorders are often characterized by multiple phenotypes, presented over long periods of time and leading to significant social impacts. Here, we have developed a system which will allow the automated monitoring of individual mice housed socially in the cage they are reared and housed in, within established social groups and over long periods of time. We demonstrate that the system accurately reports individual locomotor behavior within the group and that the measurements taken can provide unique insights into the effects of genetic background on individual and group behavior not previously recognized.

Update of neurotrophic factors in neurobiology of addiction and future directions

Drug addiction is a chronic brain disease and drugs of abuse cause long lasting neuroadaptations. Addiction is characterized by the loss of control over drug use despite harmful consequences, and high rates of relapse even after long periods of abstinence. Neurotrophic factors (NTFs) are well known for their actions on neuronal survival in the peripheral nervous system. Moreover, NTFs have been shown to be involved in synaptic plasticity in the brain. Brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) are two of the most studied NTFs and both of them have been reported to increase craving when administered into the mesocorticolimbic dopaminergic system after drug self-administration. Here we review recent data on BDNF and GDNF functions in addiction-related behavior and discuss them in relation to previous findings. Finally, we give an insight into how new technologies could aid in further elucidating the role of these factors in drug addiction.

Video tracking analysis of behavioral patterns during estrus in goats

Here, we report a new method for measuring behavioral patterns during estrus in goats based on video tracking analysis. Data were collected from cycling goats, which were in estrus (n = 8) or not in estrus (n = 8). An observation pen (2.5 m × 2.5 m) was set up in the corner of the female paddock with one side adjacent to a male paddock. The positions and movements of goats were tracked every 0.5 sec for 10 min by using a video tracking software, and the trajectory data were used for the analysis. There were no significant differences in the durations of standing and walking or the total length of movement. However, the number of approaches to a male and the duration of staying near the male were higher in goats in estrus than in goats not in estrus. The proposed evaluation method may be suitable for detailed monitoring of behavioral changes during estrus in goats.