Strohl Joshua J, Gallagher Joseph T, Gómez Pedro N, Glynn Joshua M, Huerta Patricio T
Laboratory of Immune & Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA, 350 Community Drive, Manhasset, NY, 11030, USA.
Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA.
Bioelectron Med. 2021 Nov 23;7(1):17. doi: 10.1186/s42234-021-00079-3.
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.
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.
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.
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.
细胞外记录是神经科学中一项关键的电生理技术，用于研究单个神经元和神经元群体的活动。电极捕获电压轨迹，借助分析工具可揭示动作电位（“尖峰”）以及局部场电位。尖峰分类过程用于提取单个神经元产生的动作电位。直到最近，尖峰分类都是通过手工技术进行的，由于操作人员固有的偏差，这种方法既费力又不可靠。随着神经科学家在其探针上增加多个电极，高密度设备可以同时记录数百到数千个神经元，使得手工尖峰分类过程变得越来越困难。自动化尖峰分类软件的出现为这个问题提供了一个有吸引力的解决方案，在本研究中，我们提出了一个易于执行的框架来运行自动化尖峰分类器。
在自由活动的小鼠在直线轨道上导航时，从海马体的CA1区域获取四极管记录。在小鼠在T迷宫中进行测试时，也从前边缘皮层（内侧前额叶皮层的一个区域）获取四极管记录。所有动物都植入了定制设计的3D打印微驱动器，这些微驱动器带有16个电极，以4 - 四极管几何形状捆绑在一起。
我们概述了一个用于分析单单元数据的框架，在这个框架中，我们将采集系统（Cheetah，Neuralynx）与分析软件（MATLAB）以及自动化尖峰分类管道（MountainSort）连接起来。我们给出了关于如何实施框架不同步骤的精确说明，以及我们设计逻辑的解释。我们通过比较自由活动小鼠海马体和前边缘皮层的神经记录中手动分类的尖峰与自动分类的尖峰，来验证这个框架。
我们已经有效地将MountainSort尖峰分类器与Neuralynx获取的神经记录集成在一起。我们的框架易于实施，并提供了一个高通量的解决方案。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纳入其装备库的团队在扩大其分析范围时可能会发现我们的框架非常有价值。
