在神经生理学实验中利用神经元刺激活性触发闭环刺激
November 12th, 2019
该协议演示如何使用电生理系统进行由神经元活动模式触发的闭环刺激。还提供可轻松针对不同刺激设备修改的示例 Matlab 代码。
这个标准的神经生理记录系统可用于进行闭环刺激，这对于研究大脑中的因果相互作用非常有用。该协议允许在计算机科学领域专业知识有限的神经科学家以很少的成本快速实施各种闭环实验。闭环实验对基础科学和研究越来越重要。
例如，多发癫痫研究表明，在癫痫发作时神经元刺激触发是降低癫痫发作严重程度的有效方法。为了便于在神经科学研究中采用闭环实验，该协议提供了MATLAB代码，用于将闭环电生理记录系统转换为闭环系统。这种闭环刺激的实现在清醒的老鼠中表现出来。
要安装猎豹 6.4 数据采集系统，请打开包含与猎豹采集系统交互的库的软件网页，然后安装 SpikeSort-3D。安装网通开发包。登录到 MATLAB 帐户以选择正确的许可证、版本和操作系统。
然后，下载联机事件触发的库，然后将文件提取到文档 MATLAB 文件夹。对于数据采集，请打开 Cheetah 软件并记录尖峰数据以填充模板波形。几分钟后，停止数据采集，对记录的数据执行 SpikeSort。
接下来，打开 SpikeSort3-D 并单击文件菜单并加载峰值文件从包含记录数据的文件夹中选择峰值文件。然后，单击群集菜单，使用 KlustaKwik 自动群集，然后运行。要执行闭环实验，请恢复猎豹中的数据采集，然后打开 MATLAB。
打开闭合循环。M 并单击"运行"。或者，在命令窗口中执行闭环。
浏览到录制文件夹并选择其中一个尖峰数据文件，以加载初始录制中定义的尖峰信息。单击绘制的波形下相应的复选框，选择一个或多个将触发刺激的神经元。定义通过输入 min 匹配文本框中的整数来触发刺激的最小神经元数，并定义通过在窗口文本框中输入数字来将匹配不同波形的峰值视为共动的时间窗口。
然后，单击"发送"以根据所选神经元的尖峰活动开始事件的在线触发。在这个具有代表性的实验中，老鼠被放在一个花盆上，花盆在行为实验中休息时是一个熟悉的休息场所。在这里，可以观察到记录软件和MATLAB程序的代表性同时运行，该程序显示实时获取的峰值波形。
当检测到预定义触发群集的峰值时，在 MATLAB 图形窗口中用粗体虚线显示波形，从而触发音调并提供闭环系统。在图中，显示了一个代表性直方图，说明生成人工尖峰的时间与触发触发信号的时间之间的延迟。在准备动物并安装软件后，闭环实验包括两个主要阶段:初始数据采集和闭环刺激，在此期间尖峰触发外部刺激。
该协议还允许基于多个神经元的活动来治疗刺激，这对于解决大脑中的信息处理问题非常重要。此 MATLAB 解决方案的优点是，它具有低成本和灵活性，可以定义触发刺激的活动模式，从而有助于研究各种神经科学问题。
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概述
该协议展示了基于神经元活动模式的闭环刺激的电生理系统的使用。它使神经科学家即使在计算机科学专业知识有限的情况下，也能高效且经济地进行闭环实验。该协议包括用于实现的示例MATLAB代码，并以清醒大鼠的记录为例，展示了神经元脉冲和刺激之间的实时交互。
关键研究组成部分
科学领域
- 神经科学
- 电生理学
- 闭环系统
背景
- 闭环刺激对于研究大脑中的因果关系至关重要。
- 这种方法在癫痫研究中特别有用，可用于减轻发作严重程度。
- 该协议解决了神经科学中对可访问闭环系统的需求。
研究目的
- 提供一种以最小成本和专业知识实现闭环刺激的方法。
- 通过实时刺激促进神经元活动和反应的研究。
- 使研究人员能够更好地理解大脑中的信息处理。
所用方法
- 该研究采用标准的神经生理记录系统进行闭环实验。
- 使用清醒大鼠的记录作为生物模型。
- 提供MATLAB代码用于开环系统的定制和转换。
- 关键步骤包括数据采集、脉冲数据分类和定义刺激参数。
- 闭环实验涉及基于神经活动实时触发刺激。
主要结果
- 该协议允许基于多个神经元活动的刺激，增强了对神经交互的理解。
- 脉冲数据用于触发外部刺激，展示了有效的闭环反馈。
- 通过MATLAB接口实现了神经活动的实时可视化。
- 关键发现突出了MATLAB基础解决方案的灵活性和成本效益。
结论
- 这项研究使研究人员能够轻松实现和采用闭环刺激技术。
- 研究结果对于推进对神经元机制和大脑功能的理解具有重要意义。
- 灵活的刺激模式有助于解决各种神经科学问题。
常见问题
Executive Industry Relevance
Closed-loop neurophysiological systems enable causal interrogation of brain circuits by linking neuronal activity patterns to real-time stimulation, supporting target validation and mechanistic de-risking in preclinical neuroscience. This MATLAB-based approach lowers technical barriers for implementing closed-loop designs, facilitating scalable and reproducible studies of neural information processing. The protocol supports early discovery workflows by providing a flexible, cost-effective tool for hypothesis-driven stimulation experiments in disease-relevant models.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by triggering stimuli based on defined spiking activity of single or multiple neurons.
- Operational Value: Supports functional target validation through real-time causal manipulation of neuronal circuits in awake animal models.
- Predictive Value: Enhances confidence in target engagement by demonstrating stimulus-evoked neural responses under controlled, closed-loop conditions.
Screening & Assay Development
- Scientific Value: Prepares validated neuronal recording systems for downstream assay standardization by enabling precise, activity-dependent stimulation.
- Operational Value: Improves assay reproducibility through user-defined spike detection thresholds and coactivity windows for consistent stimulus triggering.
- Scalability: Facilitates platform reuse across experiments via modifiable MATLAB code adaptable to various stimulation devices and recording setups.
Translational & Preclinical Research
- Translational Continuity: Demonstrated in awake rats, supporting relevance to disease models and preclinical validation of neuromodulation strategies.
- Mechanistic De-risking: Allows study of information processing in the brain by treating stimuli based on multi-neuron activity patterns, reducing ambiguity in causal interpretations.
- Risk-Adjusted Advancement: Supports go/no-go decisions by providing quantitative, real-time feedback on neural responses to stimulation.
Pipeline & Workflow Integration
The method integrates into early discovery workflows by enabling hypothesis testing through real-time stimulation triggered by neuronal spiking activity, bridging electrophysiology and intervention studies.
- Discovery Biology: Supports pathway clarification and biological de-risking by allowing researchers to test causal relationships between defined neural activity patterns and downstream responses.
- Screening: Enhances assay readiness through reproducible spike-sorting and online event triggering based on user-defined neuronal clusters.
- Analytics: Provides quantitative dependent variable measurements such as spike-triggered stimulus timing and histogrammed response delays for comparative condition analysis.
- Translational Research: Connects to preclinical continuity via demonstration in awake rodents, supporting extrapolation to disease-relevant systems.
- Enterprise Reuse: Framed as a reusable capability due to open-source MATLAB code that can be adapted across labs and stimulation modalities without major reengineering.
Operational & Enterprise Impact
- Scientific Value: Predictive confidence in target modulation, reduction of mechanistic ambiguity in neural circuit studies.
- Operational Value: Standardization, reproducibility, and low-cost implementation using widely available electrophysiology and MATLAB tools.
- Strategic Value: Better go/no-go decisions in target validation, capital efficiency, and reduced late-stage biological risk in neuromodulation programs.
- Portfolio Impact: Risk-adjusted prioritization of targets based on causal evidence from closed-loop stimulation experiments.
Implementation Considerations
- Requires expertise in electrophysiology, spike sorting, and basic MATLAB scripting for parameter configuration.
- Depends on instrumentation such as Neuralynx Cheetah system and compatible PC for real-time data acquisition and processing.
- Necessitates cross-team standardization of spike detection thresholds, coactivity windows, and min-match parameters for reproducible triggering.
- Involves adaptation considerations when applying to different model systems or stimulation modalities beyond tone or serial port outputs.
- Practical limitations include dependency on stable spike sorting and potential latency in real-time processing affecting temporal precision of stimulation.
