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

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Ho, Hinze 
Kejzar, Nejc 
Sasaguri, Hiroki 
Saito, Takashi 
Saido, Takaomi C 


Automated home-cage monitoring systems present a valuable tool for comprehensive phenotyping of natural 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 analysis. 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 disease 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.



Alzheimer's disease, automated classification, home-cage system, longitudinal monitoring, machine learning, mouse behavioural cognitive testing, Mice, Animals, Cognition, Hippocampus, Alzheimer Disease, Computers, Behavior, Animal

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Elsevier BV
UK Dementia Research Institute (via University College London) (553684)
Wellcome Trust (206682/Z/17/Z)