Unbiased analysis of mouse social behaviour using unsupervised machine learning
| Autor: | Oscar Bauer, Anne-Marie Le Sourd, Fabrice de Chaumont, Giacomo Nardi, Elodie Ey, Thomas Bourgeron, Jean-Christophe Olivo-Marin |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science business.industry Feature extraction Social behaviour medicine.disease Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology medicine Autism Unsupervised learning Artificial intelligence business Classifier (UML) computer |
| Zdroj: | ISBI |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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