Gehring Tiago V, Luksys Gediminas, Sandi Carmen, Vasilaki Eleni
Department of Computer Science, University of Sheffield, Sheffield, UK.
Division of Cognitive Neuroscience, University of Basel, Basel, Switzerland.
Sci Rep. 2015 Oct 1;5:14562. doi: 10.1038/srep14562.
The Morris Water Maze is a widely used task in studies of spatial learning with rodents. Classical performance measures of animals in the Morris Water Maze include the escape latency, and the cumulative distance to the platform. Other methods focus on classifying trajectory patterns to stereotypical classes representing different animal strategies. However, these approaches typically consider trajectories as a whole, and as a consequence they assign one full trajectory to one class, whereas animals often switch between these strategies, and their corresponding classes, within a single trial. To this end, we take a different approach: we look for segments of diverse animal behaviour within one trial and employ a semi-automated classification method for identifying the various strategies exhibited by the animals within a trial. Our method allows us to reveal significant and systematic differences in the exploration strategies of two animal groups (stressed, non-stressed), that would be unobserved by earlier methods.
莫里斯水迷宫是啮齿动物空间学习研究中广泛使用的一项任务。动物在莫里斯水迷宫中的经典行为指标包括逃避潜伏期以及到达平台的累计距离。其他方法则侧重于将轨迹模式分类为代表不同动物策略的刻板类别。然而,这些方法通常将轨迹作为一个整体来考虑,因此会将一条完整的轨迹归为一个类别,而动物在一次试验中往往会在这些策略及其相应类别之间切换。为此,我们采用了一种不同的方法:我们在一次试验中寻找不同动物行为的片段,并采用半自动分类方法来识别动物在一次试验中展现出的各种策略。我们的方法使我们能够揭示两组动物(应激组、非应激组)在探索策略上的显著且系统的差异,而早期方法是无法观察到这些差异的。