Automated acoustic detection of mouse scratching

Generalized resistance to pruritogen-induced scratching in the C3H/HeJ strain

Previously the effect of the pruritogens, such as histamine and chloroquine, was tested in 11 inbred mouse strains, and this study aimed to identify resistant and sensitive strains, consistent with the observation that underlies the large variability in human populations. In the present study, we used the low responder C3H/HeJ (C3H) and the more sensitive C57BL/6J (C57) strain to find out if resistance and sensitivity to develop pruritus is restricted to only histamine and chloroquine or extends to other known pruritogens as well. We tested five additional commonly known pruritogens. We established dose-response relationships by injecting four concentrations of the pruritogens in the range of 0.3, 1, 3, and ten-fold in the nuchal fold. Then we assessed the scratching behavior for 30 min after injection with an automated custom-designed device based on the bilateral implantation of mini-magnets in the hind paws and on single cages placed within a magnetic coil. We found that the resistance to pruritogens is a general phenotype of the C3H strain and extends to all pruritogens tested, including not only histamine and chloroquine, but also endothelin, trypsin, 5-HT (serotonin), the short peptide SLIGRL, and Lysophosphatidic acid (LPA). C57 was more sensitive to all pruritogens and, in contrast to C3H, dose-response relationships were evident for some of the pruritogens. In general, comparable peak scratch responses were observed for the 0.3-fold concentrations of the pruritogens in C57 whereas C3H required at least the ten-fold concentration and still displayed only between 5 and 33% of the scratch responses observed in C57 for the respective pruritogen. The general resistance to pruritogens and the low level of scratching behavior found in the C3H strain is an interesting trait and represents a model for the study of the heritability of itch. It is accompanied in C3H with a higher sensitivity in assays of nociception.

Automated scratching detection system for black mouse using deep learning

The evaluation of scratching behavior is important in experimental animals because there is significant interest in elucidating mechanisms and developing medications for itching. The scratching behavior is classically quantified by human observation, but it is labor-intensive and has low throughput. We previously established an automated scratching detection method using a convolutional recurrent neural network (CRNN). The established CRNN model was trained by white mice (BALB/c), and it could predict their scratching bouts and duration. However, its performance in black mice (C57BL/6) is insufficient. Here, we established a model for black mice to increase prediction accuracy. Scratching behavior in black mice was elicited by serotonin administration, and their behavior was recorded using a video camera. The videos were carefully observed, and each frame was manually labeled as scratching or other behavior. The CRNN model was trained using the labels and predicted the first-look videos. In addition, posterior filters were set to remove unlikely short predictions. The newly trained CRNN could sufficiently detect scratching behavior in black mice (sensitivity, 98.1%; positive predictive rate, 94.0%). Thus, our established CRNN and posterior filter successfully predicted the scratching behavior in black mice, highlighting that our workflow can be useful, regardless of the mouse strain.

Dissecting the precise nature of itch-evoked scratching

Itch is a discrete and irritating sensation tightly coupled to a drive to scratch. Acute scratching developed evolutionarily as an adaptive defense against skin irritants, pathogens, or parasites. In contrast, the itch-scratch cycle in chronic itch is harmful, inducing escalating itch and skin damage. Clinically and preclinically, scratching incidence is currently evaluated as a unidimensional motor parameter and believed to reflect itch severity. We propose that scratching, when appreciated as a complex, multidimensional motor behavior, will yield greater insight into the nature of itch and the organization of neural circuits driving repetitive motor patterns. We outline the limitations of standard measurements of scratching in rodent models and present new approaches to observe and quantify itch-evoked scratching. We argue that accurate quantitative measurements of scratching are critical for dissecting the molecular, cellular, and circuit mechanisms underlying itch and for preclinical development of therapeutic interventions for acute and chronic itch disorders.

Automated detection of mouse scratching behaviour using convolutional recurrent neural network

Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models.

Effect of commercial and green synthesized ZnO NPs in murine model of chloroquine-induced pruritus

Purpose: To investigate the effects of zinc oxide nanoparticles (ZnO NPs) on chloroquine (CQ)-induced itching, and overall behavior of mice after oral administration of ZnO NPs of various sizes and doses. Background: With the wide-spread use of ZnO NPs in pharmaceuticals and cosmetics, concerns about their safety and toxicity are also increasing. Multiple aspects of ZnO NPs regarding cytotoxicity and tolerability are under investigation globally. Still, a clear conclusion about their safety has not been reached. Chloroquine phosphate is an antimalarial with known side effects of itching in humans and animals. In this study, CQ was used to induce itching in mice, and the effects of ZnO NPs on scratching and other neurological behavior of mice were observed. Methods: Female BALB/c mice were divided into eleven groups of six mice each. ZnO NPs of various sizes and doses were administered orally 1 hour before CQ (32 mg/kg body weight) was administered subcutaneously. The effect of ZnO NPs on CQ-induced pruritus was observed for the next 30 minutes. Simultaneously, overall behavioral changes (socialization and locomotion) were also recorded using a video camera. Results: A significant reduction (P˂0.001) in scratching bouts was observed at all three doses of ZnO NPs (particle sizes 100, 30 nm, and green synthesized 30 nm). Locomotion was reduced significantly (P˂0.001) in ZnO NPs-treated groups in comparison to normal saline and CQ group, additionally, a significant increase in socialization (P˂0.05) was observed in ZnO NP-treated groups as compared to CQ group. Conclusion: ZnO NPs, instead of aggravating the dermatological condition, ameliorated the pruritus. All sizes of ZnO NPs used significantly improved socialization among mice and reduced locomotion activity.

Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors

Scratching is a main behavioral response accompanied by acute and chronic itch conditions, and has been quantified as an objective correlate to assess itch in studies using laboratory animals. Scratching has been counted mostly by human annotators, which is a time-consuming and laborious process. It has been attempted to develop automated scoring methods using various strategies, but they often require specialized equipment, costly software, or implantation of device which may disturb animal behaviors. To complement limitations of those methods, we have adapted machine learning-based strategy to develop a novel automated and real-time method detecting mouse scratching from experimental movies captured using monochrome cameras such as a webcam. Scratching is identified by characteristic changes in pixels, body position, and body size by frame as well as the size of body. To build a training model, a novel two-step J48 decision tree-inducing algorithm along with a C4.5 post-pruning algorithm was applied to three 30-min video recordings in which a mouse exhibits scratching following an intradermal injection of a pruritogen, and the resultant frames were then used for the next round of training. The trained method exhibited, on average, a sensitivity and specificity of 95.19% and 92.96%, respectively, in a performance test with five new recordings. This result suggests that it can be used as a non-invasive, automated and objective tool to measure mouse scratching from video recordings captured in general experimental settings, permitting rapid and accurate analysis of scratching for preclinical studies and high throughput drug screening.