Abstract
Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we introduce a method for the real-time analysis of the behaviour of mice housed in groups of up to four over several days and in enriched environments. The method combines computer vision through a depth-sensing infrared camera, machine learning for animal and posture identification, and radio-frequency identification to monitor the quality of mouse tracking. It tracks multiple mice accurately, extracts a list of behavioural traits of both individuals and the groups of mice, and provides a phenotypic profile for each animal. We used the method to study the impact of Shank2 and Shank3 gene mutations-mutations that are associated with autism-on mouse behaviour. Characterization and integration of data from the behavioural profiles of Shank2 and Shank3 mutant female mice revealed their distinctive activity levels and involvement in complex social interactions.
Highlights
Mice are routinely used as preclinical model to study the mechanisms leading to human diseases
We show that the atypical social behavior of mutant mice of both strains appeared to disturb the formation of subgroups within mixed-genotype groups of four mice
Segmentations are filtered by a dedicated machine learning to reject detections that do not match the mice (Supplementary methods - Building the detection feature vector, Detection filtering with machine learning) and detections are processed to separate mice that are in contact (Supplementary methods - Detection splitter)
Summary
Mice are routinely used as preclinical model to study the mechanisms leading to human diseases. One of the main current limitations remains the fact that none of them allow, in the long term and without manual corrections, an individual tracking of a mouse within a group with a sufficient level of details To overcome these limitations, we developed a comprehensive system, called Live Mouse Tracker (LMT). It allows the automatic live tracking, identification and characterization through behavioral labeling of up to four animals in an enriched environment with no time limit This solution makes use of RFID sensors and of an infrared/depth RGBD camera, under the control of a machine learning-based framework. We show that the atypical social behavior of mutant mice of both strains appeared to disturb the formation of subgroups within mixed-genotype groups of four mice These results demonstrate the capability of LMT to study differences in the phenotypes expressed by individuals in groups of mice over larger durations of time
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