Biology subjects

Lewejohann, L.

Publications and source records attributed to Lewejohann, L..

12 recordsLinked to original sources

bioRxiv2026

Automatic pain face analysis in mice: Applied to a varied dataset with non-standardized conditions

Biomedical research relies on scientifically validated tools to assess pain, suffering, and distress in laboratory animals to ensure their well-being. In mice, the most frequently used laboratory animals, the Mouse Grimace Scale (MGS) provides a reliable tool for the assessment of facial expression changes caused by impaired well-being. However, no automated tool can yet reliably assess all features of the MGS across different mouse strains under varying experimental or housing conditions in real-time, as the variability present in recorded image datasets poses substantial challenges for computer vision models. Despite this technical difficulty, variability across subsets in terms of mouse strain, treatments, laboratory, and image acquisition setup is essential for paving the way toward MGS assessment under non-standardized conditions in the home cage rather than standardized cage-side recording setups. Against this background, a large and diverse dataset containing five subsets is introduced and a deep learning model was trained to predict the average MGS scores ranging between 0 and 2. It achieved a root mean squared error (RMSE) of 0.26 when trained on all subsets of the dataset, outperforming the average human rater in terms of error magnitude. The correlation between human raters and automated MGS scores was very high (Pearsons r=0.85). In the cross-dataset evaluation, one subset was excluded from training and used for testing the model. This approach yielded higher errors compared to models trained and tested on the same subsets. A model restricted to the feature of orbital tightening showed lower performance than one trained on all facial features of the MGS. Overall, the most reliable model for predicting average MGS scores for a novel dataset is the one trained on the combined subsets. Performance may be further enhanced by fine-tuning the model using human-generated MGS scores for a portion of the novel subset.

animal behavior and cognition↗

bioRxiv2025

Towards Fully Automated Investigation of Social Learning in Mice

Mice have been demonstrated to learn from each other in social interactions, the extent to which this takes place and the strategies involved, however, largely remain to be elucidated beyond spatially and temporally confined tests of social memory retention. Here, we present a method which utilizes and modifies 1) a commercially available tool for automated behavioral testing, the IntelliCage and 2) an open-source solution for 24/7 live animal tracking, the Live Mouse Tracker, to create a powerful method for the investigation of learning behavior in semi-naturalistic group settings. We see a wide range of possible applications, such as for instance the investigation of learning in social interactions, which we present here as an example. In the present study, co-learning did not facilitate place learning over individual learning. While automated annotation of behaviors was effective, markerless animal identification proved unreliable in a highly enriched environment with manifold opportunity of occlusion from video tracking. In response, we here present a rationale for identifying the reliable portion of tracking data, to which we confine the behavioral analysis. Co-learning animals engaged more often in some prosocial interactions with their teammates than with other animals, but they did overall not interact with each other more frequently than with individual learners. Correlative analysis of learning behavior and social interactions did not reveal any particular association between behavior and learning success. While the mechanisms of social learning in mice could not be conclusively elucidated within the scope of this study, we report on the development of a promising tool for presenting manifold learning tasks to mice while tracking their individual and social behaviors in a fully automated manner. Further, we discuss limitations of the current configuration and present an outlook on further improving the method.

animal behavior and cognition↗

bioRxiv2024

Mechanical problem solving in mice

Behavioral learning is a complex phenomenon that involves cognitive, perceptual, motor and emotional contributions. Yet, most animal studies focus on reductionist tasks that aim to isolate one of these aspects [1, 2, 3, 4]. Here, we use a lockbox -- a complex, multi-step mechanical puzzle -- to study learning dynamics in freely behaving mice. The mice engaged spontaneously with the task and learned to solve it within just a few trials. To dissect different contributions to this rapid form of learning, we combined deep learning-based behavioral tracking in a multi-camera setup with probabilistic inference and computational modeling. We find that the learning progress of the mice was initially dominated by the acquisition of motor skills, i.e., the increased ability to manipulate the individual mechanisms, while a cognitive strategy for the task sequence emerged only later. The lockbox paradigm may hence offer a promising framework for studying the interaction between low-level motor learning and high-level decision-making strategies in a single, ethologically relevant task.

animal behavior and cognition↗

bioRxiv2024

In search of a conditioned place preference test to assess the severity of experimental procedures

To compare the severity of experimental procedures and behavioural tests from an animals perspective, novel methods are required. Theoretically, one feasible approach could be the use of a conditioned place preference test (CPP). This test employs the preference for a certain area in a test apparatus being associated with an experimental treatment. Traditionally, the CPP is used to investigate, for example, the effects of drugs. Instead we aimed to develop a protocol that would enable us to compare the effects of different experimental procedures conducted with mice. Nine experiments with C57BL/6J mice were performed, varying the setup, the procedure duration, the stimuli as well as the presentation order. None of the tested protocols resulted in a distinct preference. Moreover, even simple protocols using food reward as a treatment failed to result in a conditioned place preference. In summary, none of the protocols was sufficient to form a reliable association between conditioned and unconditioned stimulus. We have scrutinized the experimental setup in detail, and we cannot present a solution yet. However, hopefully, our findings will help to create a working CPP to compare the severity of different experimental procedures for mice.

animal behavior and cognition↗

bioRxiv2023

Development and Application of Home Cage Monitoring in Laboratory Mice and Rats: a Systematic Review

Traditionally, in biomedical animal research, laboratory rodents are individually examined in test apparatuses outside their home cages at selected time points. However, the outcome of such tests can be influenced by the novel environment, the time of day, separation from the social group, or the presence of an experimenter. Moreover, valuable information may be missed when the animals are only monitored in short periods. These issues can be overcome by longitudinal 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. Both the absolute ([~] x26) and relative ([~] x7) number of HCM-related publications increased from 1974 to 2020. In both mice and rats, 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. More than 70 % of the studies did not involve a disease model, but the percentage of studies using disease models increased since the 2000s. In most studies, animals were kept for short (up to 4 weeks) length periods in the HCM systems; intermediate length 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 hours, while 24-hour measurements have been more frequently since the 2000s. The systematic review demonstrated that manual monitoring is decreasing 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 publication year, 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 could be investigated in the home cage in recent times. Over the period covered in this study, techniques for HCM of mice and rats has improved considerably. This development is ongoing and further progress and 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.

animal behavior and cognition↗

bioRxiv2022

Development of an IntelliCage based Cognitive Bias Test for Mice.

The cognitive bias test is used to measure the emotional state of animals with regard to future expectations. Thus, the test offers a unique possibility to assess animal welfare with regard to housing and testing conditions of laboratory animals. So far, however, the performance of such a test is time consuming and requires the presence of an experimenter. Therefore, we developed an automated and home-cage based cognitive bias test based on the IntelliCage system. We present several developmental steps to improve the experimental design leading to a successful measurement of cognitive bias in group-housed female C57BL/6J mice. The automated and home-cage based test design allows to obtain individual data from group-housed mice, to test the mice in their familiar environment, and during their active phase. By connecting the test-cage to the home-cage via a gating system, the mice participated in the test on a self-chosen schedule, indicating high motivation to actively participate in the experiment. We propose that this should have a positive effect on the animals themselves as well as on the data. Unexpectedly, the mice showed an optimistic cognitive bias after enrichment was removed and additional restraining. An optimistic expectation of the future as a consequence of worsening environmental conditions, however, can also be interpreted as an active coping strategy in which a potential profit is sought to be maximized through a higher willingness to take risks.

animal behavior and cognition↗

bioRxiv2022

Effects of more natural housing conditions on the muscular and skeletal characteristics of female C57BL/6J mice

BackgroundEnrichment of home cages in laboratory experiments offers clear advantages, but has been criticized in some respects. First, there is a lack of definition, which makes methodological uniformity difficult. Second, there is concern that the enrichment of home cages may increase the variance of results in experiments. Here, the influence of more natural housing conditions on physiological parameters of female C57BL/6J mice was investigated from an animal welfare point of view. For this purpose, the animals were kept in three different housing conditions: conventional cage housing, enriched housing and the semi-naturalistic environment. The focus was on musculoskeletal changes after long-term environmental enrichment. ResultsThe housing conditions had a long-term effect on the body weight of the test animals. The more complex and natural the home cage, the heavier the animals. This was associated with increased adipose deposits in the animals. There were no significant changes in muscle and bone characteristics except for single clues (femur diameter, bone resorption marker CTX-1). Additionally, the animals in the semi naturalistic environment (SNE) were found to have the fewest bone anomalies. Housing in the SNE appears to have the least effect on stress hormone concentrations. The lowest oxygen uptake was observed in enriched cage housing. ConclusionsDespite increasing values, observed body weights were in the normal and strain-typical range. Overall, musculoskeletal parameters were slightly improved and age-related effects appear to have been attenuated. The variances in the results were not increased by more natural housing. This confirms the suitability of the applied housing conditions to ensure and increase animal welfare in laboratory experiments.

physiology↗

bioRxiv2021

Rating enrichment items by group-housed laboratory mice in multiple binary choice tests using an RFID-based tracking system

There is growing evidence that enrichment of housing conditions of laboratory animals has positive effects on behavior, growth, and health. Laboratory mice spend most of their lives in their housing rather than in experimental apparatus, so improving housing conditions is a first-choice approach to improving their welfare. Despite the increasing popularity of enrichment, little is known about whether it is also perceived as being beneficial from the animals point of view. This is especially true due to the fact that enrichment has become an umbrella term that encompasses a wide variety of different elements. Therefore, we categorized enrichment items according to their prospective use into the categories structural, housing, and foraging. In multiple binary choice tests we let 12 female C57BL/6J mice chose and rank 5 enrichment items per category. All possible pair combinations of enrichment items within each category were presented counterbalanced for a 46-hour period in a home cage based system consisting of two interconnected cages. A new analyzing method combined the binary decisions and ranked the enrichment items within each category by calculating worth values and consensus errors. Mice ranked the lattice ball (foraging), the rope (structural) and the second plane (structural) in upper positions. No clear preferences were determined for different types of housing enrichment during inactive times (light phase) whereas these objects were actively explored during the dark phase. Here the floorhouse and the paperhouse revealed high worth values. Overall, a high consensus error in ranking positions was observed reflecting strong individual differences in preferences. This highlights the importance of a varied enrichment approach as not all mice prefer the same item at all times. Given the known overall beneficial effects of enrichment, these data will help to provide appropriate enrichment elements to improve animal welfare and refine animal experimentation.

animal behavior and cognition↗

bioRxiv2020

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

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

animal behavior and cognition↗

bioRxiv2020

Alternate without alternative: Neither preference nor simple learning behaviour shown by C57BL/6J mice in the T-maze

In rodents, the T-maze test is commonly used to investigate spontaneous alternating behaviour but it can also be used to investigate memory, stimuli discrimination or preference between goods. However, especially regarding T-maze preference tests there is no recommended protocol and researchers frequently report reproduction difficulties of this test using mice. Here, we aimed to develop an efficient protocol with female C57BL/6J mice, conducting two preference tests with different design: In a first test, on two consecutive days with five trials, thirteen mice had to choose between two fluids. In a second preference test, on five consecutive days with two (week 1) or three (week 2) trials, twelve mice had to choose between one arm containing bedding mixed with millet and one containing only bedding. This test design resembled a simple learning test (learn where to find the rewarded and the unrewarded arm on the basis of spatial, olfactory and visual cues). In both experiments, mice took only a few seconds per trial to run the maze and make their choice. However, in both experiments mice failed to show any preference for one of the arms. Instead, they alternated choices. We therefore believe the T-maze test to be rather unsuitable to test preference or learning behaviour with C57BL/6J mice.

animal behavior and cognition↗

bioRxiv2019

Repeatability analysis improves the reliability of behavioral data

Reliability of data has become a major concern in the course of the reproducibility crisis. Especially when studying animal behavior, confounding factors such as novel test apparatus can lead to a wide variability of data. At worst, effects of novelty or stress related behavior can mask treatment effects and the behavioral data may be misinterpreted. Habituation to the test situation is a common practice to circumvent novelty induced increases in variance and to improve the reliability of the respective measurements. However, there is a lack of published empirical knowledge regarding reasonable habituation procedures and a method validation seems to be overdue. This study aimed at setting up a simple strategy to increase reliability of behavioral data. Therefore, exemplary data from mice tested in an Open Field (OF) arena were used to elucidate the potential of habituation and how reliability of measures can be confirmed by means of a repeatability analysis using the software R. On seven consecutive days, male C57BL/6J, BALB/cJ and 129S1/SvImJ mice were tested in an OF arena once daily and individual mouse behavior (distance travelled, average activity) was recorded. A repeatability analysis was conducted in order to estimate the reliability of measured animal behavior with regard to repeated trials of habituation. Our data analysis revealed that monitoring animal behavior during habituation is important to determine when individual differences of the measurements are stable. Here, the mixed effect model framework proved to be a powerful tool for estimating repeatability values. Repeatability values from distance travelled and average activity increased over the habituation period, revealing that around 60 % of the variance of the data can be explained by individual differences between mice. The first day of habituation was statistically significantly different from the following 6 days in terms of distance travelled and average activity. A habituation period of three days appeared to be sufficient in this study. Overall these results emphasize the importance of habituation and in depth analysis of habituation data to define the correct starting point of the experiment for improving the reliability and reproducibility of experimental data.

animal behavior and cognition↗

bioRxiv2019

Towards a fully automated surveillance of well-being status in laboratory mice using deep learning

Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communication, a variety of behavioral, biochemical, physiological, and physical parameters are employed to evaluate the well-being of an animal. Especially in the field of biomedical research, scientifically sound tools to assess pain, suffering, and distress for experimental animals are highly demanded due to ethical and legal reasons. For mice, the most commonly used laboratory animals, a valuable tool is the Mouse Grimace Scale (MGS), a coding system for facial expressions of pain in mice which has been shown to be accurate and reliable. Currently, MGS scoring is very time and effort consuming as it is manually performed by humans being thoroughly trained in using this method. Therefore, we aim to develop a fully automated system for the surveillance of well-being in mice. Our work introduces a semi-automated pipeline as a first step towards this goal. We use and provide a new data set of images of black-furred laboratory mice that were moving freely, thus the images contain natural variation with regard to perspective and background. The analysis of this data set is therefore more challenging but reflects realistic conditions as it would be obtainable without human intervention. Images were obtained after anesthesia (with isoflurane or ketamine/xylazine combination) and surgery (castration). We deploy two pre-trained state of the art deep convolutional neural network (CNN) architectures (ResNet50 and InceptionV3) and compare to a third CNN architecture without pre-training. Depending on the particular treatment, we achieve an accuracy of up to 99% for binary \"pain\"/\"no-pain\" classification.\n\nAuthor summaryIn the field of animal research, it is crucial to assess the well-being of an animal. For mice, the most commonly used laboratory animals, there is a variety of indicators for well-being. Especially the facial expression of a mouse can give us important information on its well-being state. However, currently the surveillance of well-being can only be ensured if a human is present. Therefore, we developed a first approach towards a fully automated surveillance of the well-being status of a mouse. We trained neural networks on face images of black-furred mice, which were either untreated or underwent anesthesia or surgery, to distinguish between an impaired and unimpaired well-being state. Our systems successfully learnt to assess whether the well-being of a mouse was impaired and, depending on the particular treatment, its decision was correct in up to 99%. A tool that visualizes the features used for the decision making process indicated that the decision was mainly based on the facial expressions of a mouse.

animal behavior and cognition↗