小鼠与伙伴：使用跨笼单模型对群体中小鼠行为进行自动分类与建模

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages.

作者信息

Camilleri Michael P J, Bains Rasneer S, Williams Christopher K I

机构信息

School of Informatics, University of Edinburgh, Edinburgh, UK.

Mary Lyon Centre, MRC Harwell, Oxfordshire, UK.

出版信息

Int J Comput Vis. 2024;132(12):5491-5513. doi: 10.1007/s11263-024-02118-3. Epub 2024 Jun 17.

DOI:10.1007/s11263-024-02118-3

PMID:39554493

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11568001/

Abstract

UNLABELLED

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual's behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11263-024-02118-3.

摘要

未标注

行为实验通常在专门的场地进行，但这可能会混淆分析结果。为解决此问题，我们提供了在家笼环境中研究小鼠的工具，使生物学家能够在最少人为干预的情况下捕捉个体行为的时间维度，并对同笼伙伴之间的相互作用和相互依存关系进行建模。我们的主要贡献是新颖的全局行为模型（GBM），该模型使用置换矩阵将每个笼子中小鼠的身份与模型进行匹配，从而总结跨笼子小鼠群体的联合行为。为支持上述内容，我们还（a）开发了活动标记模块（ALM）以从视频中自动分类小鼠行为，以及（b）发布了两个数据集，即用于训练行为分类器的ABODe和用于行为建模的IMADGE。