Biology subjects

Hohlbaum, K.

Publications and source records attributed to Hohlbaum, K..

8 recordsLinked to original sources

bioRxiv2026

Different Paradigms from Computer Vision Align with Human Assessment of the Mouse Grimace Scale

Animal welfare is a central aspect in animal-based research where mice are most commonly used. Their facial expression can be analyzed to assess their well-being status using the Mouse Grimace Scale. However, its manual application becomes increasingly impractical when used on a large number of animals. This lead to the ongoing integration of computer vision methods to automate the analysis. While such methods have proven effective qualitatively, a systematic assessment to verify their reliability largely remains an open research gap. In this work, we attempted to close this gap as we evaluated three dominant paradigms (i.e., classification from supervised learning features, self-supervised learning features, or landmark locations) for the binary (i.e., well-being un-/impaired) classification of facial mouse images. Our quantitative results showed that such methods can be employed successfully with as low as 16% type II error rates. For qualitative assessment, we visualized the decision-making process and demonstrated that mainly pixels associated with the mouse rather than its environment are used. We further discovered that visual characteristics of the mice beyond those described by the Mouse Grimace Scale contributed to the classification. Our work showed that the automated well-being status assessment in mice is trustworthy and urges towards widespread adoption.

animal behavior and cognition↗

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

GrimACE: Automated, multimodal cage-side assessment of pain and well-being in mice

Pain and welfare monitoring is essential for ethical animal testing, but current cage-side assessments are qualitative and subjective. Here we present the GrimACE, the first fully standardised and automated cage-side monitoring tool for mice, the most widely used animals in research. The GrimACE uses computer vision to provide automated mouse grimace scale (MGS) assessment together with pose estimation in a dark, safe environment. We validated the system by analysing pain after brain surgeries (craniotomies) with head implants under two analgesia regimes. Human-expert and automated MGS scores showed very high correlation (Pearson's r=0.87). Both expert and automated scores revealed that a moderate increase in pain can be detected for up to 48 hours after surgeries, but that both a single dose of meloxicam (5mg/kg s.c.) or 3 doses of buprenorphine (0.1mg/kg) + meloxicam (5mg/kg s.c.) provide adequate and comparable pain management. Simultaneous pose estimation demonstrated that mice receiving buprenorphine + meloxicam showed increased movement 4h after surgery, indicative of hyperactivity, a well-known side-effect of opioid treatment. Significant weight loss was also detected in the buprenorphine + meloxicam treatment group compared to the meloxicam-only group. Additionally, detailed BehaviorFlow analysis and automated MGS scoring of control animals suggests that habituation to the GrimACE system is unnecessary, and that measurements can be repeated multiple times, ensuring standardised post-operative recovery monitoring.

neuroscience↗

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

The threshold for intracranial self-stimulation does not increase in rats exposed to chronic unpredictable stress, a systematic review and meta-analysis

The chronic unpredictable stress model is a laboratory rodent model of stress-induced anhedonia. The sucrose preference test, often used to validate it, suffers from being unreliable. Intracranial self-stimulation offers an alternative and is often cited as supporting evidence of the validity of the model. Our aim was to assess whether an increased self-stimulation threshold is found after stress and if such a change correlates with decreases in sweet consumption. We searched PubMed, Embase, and Web of Science for studies in rats exposed to chronic unpredictable stress that employed intracranial self-stimulation. Thresholds, for stressed and control animals, in 23 experiments (11 studies) were pooled. Over 50% of the data was contributed by one research group, so a three-level meta-analytical random effects model was fit to account for methodological differences between different networks of researchers. After this adjustment, we did not find that the self-stimulation thresholds were increased in stressed rats. Pioneering experiments with positive results failed to be replicated by others, although no specific factor could be pointed to as a likely explanation. What is more, the available evidence suggests a lack of connection between sweet preference and self-stimulation, although this relationship has been seldom investigated. Methods known to mitigate biases were frequently absent, as was a transparent report of crucial study details. Our findings challenge the claim made in support of the validity of the model. Further efforts would be well-invested in assessing how reliably other tests of anhedonia have found the effects of the chronic unpredictable stress model.

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

A Buprenorphine depot formulation provides effective sustained post-surgical analgesia for 72h in mouse femoral fracture models

Adequate pain management is essential for ethical and scientific reasons in animal experiments and should completely cover the period of expected pain without the need for frequent re-application. However, current depot formulations of Buprenorphine are only available in the USA and have limited duration of action. Recently, a new microparticulate Buprenorphine formulation (BUP-Depot) for sustained release has been developed as an alternative product within Europe. Pharmacokinetics indicate a potential effectiveness for about 72h. Here, we investigated whether the administration of the BUP-Depot ensures continuous and sufficient analgesia in two mouse femoral fracture models and could therefore serve as a potent alternative to the application of Tramadol via drinking water. Both protocols were examined for analgesic effectiveness, side effects on experimental readout, and effects on fracture healing outcomes in male and female C57BL/6N mice. The BUP-Depot provided effective analgesia for 72h, comparable to the effectiveness of Tramadol in drinking water. Fracture healing outcome was not different between analgesic regimes. The availability of a Buprenorphine depot formulation for laboratory animals in Europe would be a beneficial addition for extended pain relief in mice, thereby increasing animal welfare.

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↗