A novel fully-automated system for lifelong continuous phenotyping of mouse cognition and behaviour

Comprehensive ethologically-relevant behavioural phenotyping in rodent experiments is essential for deciphering the neural basis of animal cognition. Automated home-cage monitoring systems present a valuable tool to fulfil this need. However, they often involve complex animal training routines, water or food deprivation, and probe a limited range of behaviours. Here, we present a new fully automated AI-driven home-cage system for cognitive and behavioural phenotyping in mice. The system incorporates spontaneous alternation T-maze, novel-object recognition and object-in-place recognition tests combined with monitoring of an animal’s position, water consumption, quiescence and locomotion patterns, all carried out continuously and simultaneously in an unsupervised fashion over long periods of time. Mice learnt the tasks rapidly without any need for water or food restrictions. We applied ethomics approach to show that combined statistical properties of multiple behaviours can be used to discriminate between mice with hippocampal, medial entorhinal and sham lesions and accurately predict genotype of Alzheimer’s disease mouse models on an individual animal level, surpassing the performance of several gold standard cognitive tests. This technology could enable large-scale behavioural screening for genes and neural circuits underlying spatial memory and other cognitive processes.

[1]  L. Saksida,et al.  The spontaneous location recognition task for assessing spatial pattern separation and memory across a delay in rats and mice , 2021, Nature Protocols.

[2]  L. Leocani,et al.  Apparatus design and behavioural testing protocol for the evaluation of spatial working memory in mice through the spontaneous alternation T-maze , 2021, Scientific Reports.

[3]  A. Kaffman,et al.  The Promise of Automated Home-Cage Monitoring in Improving Translational Utility of Psychiatric Research in Rodents , 2020, Frontiers in Neuroscience.

[4]  Andrew J.D. Nelson,et al.  Distributed interactive brain circuits for object-in-place memory: A place for time? , 2020, Brain and neuroscience advances.

[5]  H. Langerman,et al.  Alzheimer’s Disease – Why We Need Early Diagnosis , 2019, Degenerative neurological and neuromuscular disease.

[6]  Sreemathi Logan,et al.  Accelerated decline in cognition in a mouse model of increased oxidative stress , 2019, GeroScience.

[7]  Matthias Bethge,et al.  Pretraining boosts out-of-domain robustness for pose estimation , 2019, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[8]  Kevin M. Cury,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

[9]  H. Lipp,et al.  Automated dissection of permanent effects of hippocampal or prefrontal lesions on performance at spatial, working memory and circadian timing tasks of C57BL/6 mice in IntelliCage , 2017, Behavioural Brain Research.

[10]  B. Winblad,et al.  APP mouse models for Alzheimer's disease preclinical studies , 2017, The EMBO journal.

[11]  S. Itohara,et al.  Cognitive deficits in single App knock-in mouse models , 2016, Neurobiology of Learning and Memory.

[12]  S. Itohara,et al.  Single App knock-in mouse models of Alzheimer's disease , 2014, Nature Neuroscience.

[13]  Michel Boulouard,et al.  Object recognition test in mice , 2013, Nature Protocols.

[14]  Timothy J Bussey,et al.  The touchscreen operant platform for testing learning and memory in rats and mice , 2013, Nature Protocols.

[15]  O. Feinerman,et al.  Automated long-term tracking and social behavioural phenotyping of animal colonies within a semi-natural environment , 2013, Nature Communications.

[16]  M. Kieser,et al.  To test or not to test: Preliminary assessment of normality when comparing two independent samples , 2012, BMC Medical Research Methodology.

[17]  H. Seung,et al.  Serial two-photon tomography: an automated method for ex-vivo mouse brain imaging , 2011, Nature Methods.

[18]  G. Barker,et al.  When Is the Hippocampus Involved in Recognition Memory? , 2011, The Journal of Neuroscience.

[19]  Thomas Serre,et al.  Automated home-cage behavioural phenotyping of mice. , 2010, Nature communications.

[20]  N. Sousa,et al.  A hitchhiker's guide to behavioral analysis in laboratory rodents , 2006, Genes, brain, and behavior.

[21]  L. Breiman.  Random Forests , 2001, Machine Learning.

[22]  P. Dudchenko,et al.  How do animals actually solve the T maze? , 2001, Behavioral neuroscience.

[23]  T J Sejnowski,et al.  Running enhances neurogenesis, learning, and long-term potentiation in mice. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  F. Gage,et al.  Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus , 1999, Nature Neuroscience.

[25]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[26]  J. Aggleton,et al.  One-Trial Object Recognition by Rats , 1985 .

[27]  N. McNaughton,et al.  Comparison between the behavioural effects of septal and hippocampal lesions: A review , 1983, Neuroscience & Biobehavioral Reviews.

[28]  R. Morris,et al.  Place navigation impaired in rats with hippocampal lesions , 1982, Nature.

[29]  D. Berlyne.  NOVELTY AND CURIOSITY AS DETERMINANTS OF EXPLORATORY BEHAVIOUR1 , 1950 .

[30]  H. Woodrow,et al.  : A Review of the , 2018 .

[31]  J. Rawlins,et al.  T-maze alternation in the rodent , 2006, Nature Protocols.