A fully automated home cage for long-term continuous phenotyping of mouse cognition and behavior

Automated home-cage monitoring systems present a valuable tool for comprehensive phenotyping of natu-ral behaviors. However, current systems often involve complex training routines, water or food restriction, and probe a limited range of behaviors. Here, we present a fully automated home-cage monitoring system for cognitive and behavioral phenotyping in mice. The system incorporates T-maze alternation, novel object recognition, and object-in-place recognition tests combined with monitoring of locomotion, drinking, and quiescence patterns, all carried out over long periods. Mice learn the tasks rapidly without any need for water or food restrictions. Behavioral characterization employs a deep convolutional neural network image anal-ysis. We show that combined statistical properties of multiple behaviors can be used to discriminate between mice with hippocampal, medial entorhinal, and sham lesions and predict the genotype of an Alzheimer's dis-ease mouse model with high accuracy. This technology may enable large-scale behavioral screening for genes and neural circuits underlying spatial memory and other cognitive processes.

Automated summary

Automated home-cage monitoring systems provide a valuable tool for comprehensive phenotyping of natural behaviors. This study presents a fully automated system for cognitive and behavioral phenotyping in mice, which includes various tests and long-term monitoring of locomotion, drinking, and quiescence patterns. The system achieves high accuracy in discriminating between mice with different lesions and predicting the genotype of an Alzheimer's disease mouse model.