UNBIASED ANALYSIS OF MOUSE SOCIAL BEHAVIOUR USING UNSUPERVISED MACHINE LEARNING

被引：0

作者：

Bauer, Oscar ^{[1
,2
,3
]}

Le Sourd, Anne-Marie ^{[2
]}

Nardi, Giacomo ^{[1
]}

Bourgeron, Thomas ^{[2
]}

Olivo-Marin, Jean-Christophe ^{[1
]}

Ey, Elodie ^{[2
]}

de Chaumont, Fabrice ^{[1
]}

机构：

[1] Inst Pasteur, CNRS, UMR 3691, Unite Anal Images Biol, Paris, France

[2] Univ Paris Diderot, Sorbonne Paris Cite, Inst Pasteur, Genet Humaine & Fonct Cognit,CNRS,UMR 3571, Paris, France

[3] Ecole Doctorale Frontieres Vivant FdV, Programme Bettencourt, Paris, France

来源：

2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017) | 2017年

关键词：

unsupervised automated behavioural analysis; unsupervised classification; animal behaviour; autism spectrum disorder; mouse model; social behaviour; MICE;

D O I：

暂无

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Mouse models are broadly used to study the mechanisms of neuropsychiatric disorders and to test potential treatments. In these models, automation to monitor behavioural differences during social interactions is currently limited. We propose in the present study a new method to conduct automatic behavioural classification, using an original unsupervised machine learning. We applied the proposed method to mice mutated in Shank2, a gene associated with autism spectrum disorders. We validated our results by comparing automatically extracted results to rule-based classifier labelling. We discovered seven behavioural states matching from 80 to 95% previous rule-based classification, and two unsuspected behaviours. Interestingly, we also highlighted genotype-related differences in two behavioural categories, namely locomotion and facing the conspecific.

引用

页码：878 / 881

页数：4

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