Automated system for training and assessing reaching and grasping behaviors in rodents.
Automated system for training and assessing reaching and grasping behaviors in rodents.
复制标题
用于训练和评估啮齿动物的伸手和抓握行为的自动化系统。
DOI：
10.1016/j.jneumeth.2023.109990
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发表时间：
2024
影响因子：
3
通讯作者：
Cowen,StephenL
中科院分区：
文献类型：
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作者：
Jordan,GiannaA;Vishwanath,Abhilasha;Holguin,Gabriel;Bartlett,MitchellJ;Tapia,AndrewK;Winter,GabrielM;Sexauer,MorganR;Stopera,CarolynJ;Falk,Torsten;Cowen,StephenL
研究跨物种的抓取、抓取和拉扯行为，以研究运动控制和问题解决。拉绳是一种独特的伸手和抓握行为，需要快速学习，需要双手协调，具有行为学基础，并已应用于各种物种和疾病条件。在这里，我们描述了PANDA系统（pull And Neural Data Analysis），这是一个硬件和软件系统，它集成了一个连接到旋转编码器、馈线、微控制器、高速摄像机的连续字符串环路，以及用于评估和训练伸手、抓握和拉动行为以及与神经数据同步的分析软件。我们在大鼠的运动皮层和海马体中植入了电极，并展示了该系统如何用于评估伸手、牵拉和抓握运动与单单元和局部场活动之间的关系。此外，我们发现，与人工训练相比，自动化塑形过程显著提高了大鼠的表现，在15分钟的训练中，大鼠可以拉100米。与现有方法的比较拉绳通常是通过将食物奖励绑在绳子上和视觉评分行为来形成的。这里描述的系统可以自动训练，通过深度学习简化视频评估，并自动将到达动作分割为不同的到达/拉动阶段。据我们所知，没有系统可以自动塑造和评估这种行为。结论该系统对于研究运动控制、动机、感觉运动整合和运动障碍（如帕金森病和中风）的研究人员具有广泛的应用价值。
BackgroundReaching, grasping, and pulling behaviors are studied across species to investigate motor control and problem solving. String pulling is a distinct reaching and grasping behavior that is rapidly learned, requires bimanual coordination, is ethologically grounded, and has been applied across species and disease conditions.New MethodHere we describe the PANDA system (Pulling And Neural Data Analysis), a hardware and software system that integrates a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for the assessment and training of reaching, grasping, and pulling behaviors and synchronization with neural data.ResultsWe demonstrate this system in rats implanted with electrodes in motor cortex and hippocampus and show how it can be used to assess relationships between reaching, pulling, and grasping movements and single-unit and local-field activity. Furthermore, we found that automating the shaping procedure significantly improved performance over manual training, with rats pulling > 100 m during a 15-minute session.Comparison with Existing MethodsString-pulling is typically shaped by tying food reward to the string and visually scoring behavior. The system described here automates training, streamlines video assessment with deep learning, and automatically segments reaching movements into distinct reach/pull phases. No system, to our knowledge, exists for the automated shaping and assessment of this behavior.ConclusionsThis system will be of general use to researchers investigating motor control, motivation, sensorimotor integration, and motor disorders such as Parkinson's disease and stroke.