ReachingBot: An automated and scalable benchtop device for highly parallel Single Pellet Reach-and-Grasp training and assessment in mice.
ReachingBot: An automated and scalable benchtop device for highly parallel Single Pellet Reach-and-Grasp training and assessment in mice.
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ReachingBot：一种自动化且可扩展的台式设备，用于对小鼠进行高度并行的单颗粒触及和抓握训练和评估。
DOI：
10.1016/j.jneumeth.2023.109908
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发表时间：
2023
影响因子：
3
通讯作者：
Kakanos SG
中科院分区：
文献类型：
--
作者：
Kakanos SG
研究背景单颗粒抓取任务（single pellet reaching and grasp，SPRG）是一种广泛用于研究动物神经系统损伤后运动学习、控制和恢复的行为学方法。SPRG的手动训练和评估是劳动密集型和耗时的，并导致了多种设备的开发，这些设备使SPRG任务自动化。新方法在这里，使用机器人技术，计算机视觉和视频的机器学习分析，我们描述了一种可以无人值守的设备，向小鼠提供颗粒，并使用两种监督学习算法，在不使用图形处理单元（GPU）的情况下，以大于94%的准确度对每个试验的结果进行分类。我们的设备也可以使用我们的跨平台图形用户界面（GUI）操作。ResultsWe表明，这些设备的训练和评估小鼠并行。30只小鼠中的21只在训练期后成功地取回> 40%的颗粒。缺血性卒中后，一些小鼠表现出大的持续性缺陷，而其他小鼠仅表现出短暂的缺陷。与现有方法的比较目前最先进的桌面方法仍然需要监督，试验结果的手动分类，或昂贵的本地安装的硬件，如图形处理单元（GPU）.ConclusionsReachingBots成功地自动化SPRG培训和评估，并揭示了中风后达到结果的异质性。我们推测，达到和把握代表在运动皮层双边，但更大的不对称性，在一些小鼠比其他小鼠。
BackgroundThe single pellet reaching and grasp (SPRG) task is a behavioural assay widely used to study motor learning, control and recovery after nervous system injury in animals. The manual training and assessment of the SPRG is labour intensive and time consuming and has led to the development of multiple devices which automate the SPRG task.New methodHere, using robotics, computer vision, and machine learning analysis of videos, we describe a device that can be left unattended, presents pellets to mice, and, using two supervised learning algorithms, classifies the outcome of each trial with an accuracy of greater than 94% without the use of graphical processing units (GPUs). Our devices can also be operated using our cross-platform Graphical User Interface (GUI).ResultsWe show that these devices train and assess mice in parallel. 21 out of 30 mice retrieved > 40% of pellets successfully following the training period. Following ischaemic stroke; some mice showed large persistent deficits whilst others showed only transient deficits. This highlights the heterogeneity in reaching outcomes following stroke.Comparison with existing method(s)Current state-of-the-art desktop methods either still require supervision, manual classification of trial outcome, or expensive locally-installed hardware such as graphical processing units (GPUs).ConclusionsReachingBots successfully automated SPRG training and assessment and revealed the heterogeneity in reaching outcomes following stroke. We conjecture that reach-and-grasp is represented in motor cortex bilaterally but with greater asymmetry in some mice than in others.