An assessment of the spontaneous locomotor activity of BALB/c mice.

The surrounding environment affects the behavior of an animal. Therefore, long-term observation of an animal's behavior in its natural breeding environment is an effective approach to predict and understand animal behavior in greater detail. Spontaneous locomotor activity (SLA), the movement of animals in their breeding environment, is one of the most important behavioral indices for experimental animals. We here established an SLA measurement system using image analyses to obtain basic data from BALB/c mice. To record the movement of the mice, we used an infrared video camera. SLA of mice were calculated by detecting their geometric center in each frame. This system could detect the mouse correctly more than 99.999% in all frames. Further, we investigated the effects of habituation, age, and sex on the SLA of BALB/c mice. Three days of habituation were required to decrease the SLA of mice placed in novel cages. The 16- and 32-week-old mice were less active than 4-week-old mice. No significant differences were detected between males and females. We also found that BALB/c and C57BL/6 mice differed in their active and resting rhythms. In conclusion, we developed an SLA measurement system and obtained basic SLA data from BALB/c mice.

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.

The assessment of mouse spontaneous locomotor activity using motion picture.

Spontaneous locomotor activity (SLA) is a useful parameter reflecting physical and mental status of experimental animals. Here we aimed to establish a novel and simple method to assess mouse SLA using motion picture. Movement of C57BL/6 mice was continuously recorded by an infrared video camera connected with a single board computer. The geometric center of mouse outline in each frame was calculated using an image processing library, OpenCV in a programming language Python. Moving distance of the geometric center every second was utilized as an index of mouse SLA. Twenty-four hours assessment of SLA showed that mice repeated active and resting phase. Mice moved more actively during the dark period compared with the light period. Time-frequency analysis of SLA followed by unsupervised clustering classified their active and resting phase. Administration of a sedative, chlorpromazine (5 mg/kg) abolished mouse SLA for 8 h. In contrast, administration of a central nervous stimulant, caffeine (25 mg/kg) increased SLA for 3 h. In conclusion, we here established the automatic measurement system of mouse SLA using motion picture. This system is composed of common equipment and analysis software written in freely available programming language. We also confirmed that it is applicable for drug assessment.