背景:细胞外记录代表神经科学中用于研究单个神经元和神经元群体活动的关键电生理技术。电极捕获电压的痕迹,在分析工具的帮助下,揭示动作电位(“尖峰”)以及局部场电位。尖峰分选的过程用于提取单个神经元产生的动作电位。直到最近,穗分选是用手动技术进行的,由于固有的操作员偏见,这是费力且不可靠的。当神经科学家在他们的探针上添加多个电极时,高密度设备可以同时记录数百到数千个神经元,使得手动道钉分选过程变得越来越困难。自动尖峰排序软件的出现为这个问题提供了一个令人信服的解决方案,在这项研究中,我们提出了一个简单的执行框架来运行一个自动化的尖峰排序器。
方法:自由移动小鼠的Tetrode记录是从海马的CA1区域获得的,因为它们在线性轨迹中导航。Tetrode录音也是从前边缘皮层获得的,内侧前额叶皮层的一个区域,而小鼠在T迷宫中进行测试。所有动物都植入了定制设计,带有16个电极的3D打印微驱动器,它们被捆绑在一个4-tetrode几何形状中。
结果:我们提供了一个用于分析单个单元数据的框架的概述,在该框架中,我们连接了采集系统(猎豹,Neuralynx)带有分析软件(MATLAB)和自动尖峰分选管道(MountainSort)。我们对如何实施框架的不同步骤给出了精确的指示,以及我们的设计逻辑的解释。我们通过比较手动排序的尖峰和自动排序的尖峰来验证这个框架,在自由移动的小鼠中使用海马和前边缘皮层的神经记录。
结论:我们已经有效地将MountainSort尖峰分选仪与神经分支获得的神经记录整合在一起。我们的框架易于实现,并提供高吞吐量解决方案。我们预测,在生物电子医学的广阔领域中,那些将高密度神经记录设备整合到他们的医疗设备中的团队可能会发现我们的框架相当有价值,因为他们扩大了他们的分析足迹。
BACKGROUND: Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials (\'spikes\') as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices can record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue and, in this study, we present a simple-to-execute framework for running an automated spike sorter.
METHODS: Tetrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Tetrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry.
RESULTS: We provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (
Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice.
CONCLUSIONS: We have efficiently integrated the MountainSort spike sorter with Neuralynx-acquired neural recordings. Our framework is easy to implement and provides a high-throughput solution. We predict that within the broad field of bioelectronic medicine, those teams that incorporate high-density neural recording devices to their armamentarium might find our framework quite valuable as they expand their analytical footprint.