MRC London Institute of Medical Sciences, London, UK.
Institute of Clinical Sciences, Imperial College London, London, UK.
Philos Trans R Soc Lond B Biol Sci. 2018 Sep 10;373(1758):20170375. doi: 10.1098/rstb.2017.0375.
Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling at cellular resolution'.
行为是神经系统功能的敏感和综合表现，因此是评估基因突变或药物处理对动物影响的一种有吸引力的方法。视频数据提供了丰富但高维的行为表示，因此分析的第一步通常是某种形式的跟踪和特征提取，以在保持相关信息的同时降低维数。现代机器学习方法功能强大，但众所周知难以解释，而手工制作的特征可解释性强，但性能不一定好。在这里，我们报告了一组新的手工制作的特征，用于紧凑地量化行为。这些特征旨在具有可解释性，但尽可能多地捕捉蠕虫之间的表型差异。我们表明，完整的特征集在对突变株进行分类方面比以前定义的特征集更强大。然后，我们使用自动和手动特征选择的组合来定义一组可解释的核心特征，这些特征仍然具有足够的能力来检测突变株和野生型之间的行为差异。最后，我们将新特征应用于一系列针对不同神经亚群的光遗传学实验中，以检测时间分辨的行为差异。本文是“连接组到行为：在细胞分辨率下建模”讨论会议的一部分。
