Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
November 12th, 2019
This protocol demonstrates how to use an electrophysiological system for closed-loop stimulation triggered by neuronal activity patterns. Sample Matlab code that can be easily modified for different stimulation devices is also provided.
This standard neurophysiological recording system can be used to perform a closed-loop stimulation which is useful for studying causal interactions in the brain. This protocol allows neuroscientists with only limited expertise in computer science to rapidly implement a variety of closed-loop experiments with little cost. Closed-loop experiments have becaming increasingly important for basic science and research.
For instance, multiple epilepsy studies have shown that neuron stimulation trigger on seizure onset is an an effective approach to reduce the severity of seizures. To facilitate the adoption of closed-loop experiments in neuroscience research, this protocol provides MATLAB code to convert an open-loop electrophysiological recording system into a closed-loop system. This implementation of closed-loop stimulation is demonstrated in an awake rat.
To install the Cheetah 6.4 Data Acquisition System, open the webpage for the software which includes libraries to interact with the Cheetah acquisitions system, and install SpikeSort-3D. Install the NetCom Development Package. Login to a MATLAB account to select the correct license, version, and operating system.
Then, download the library for online event triggering as indicated and extract the files to the documents MATLAB folder. For data acquisition, open the Cheetah software and record spiking data to populate the template waveforms. After a few minutes, stop the data acquisition and perform a SpikeSort on the recorded data.
Next, open SpikeSort3-D and click file menu and load spike file To select a spike file from the folder with the recorded data. Then, click cluster menu, autocluster using KlustaKwik, and run. To perform a closed-loop experiment, resume the data acquisition in Cheetah, and open MATLAB.
Open ClosedLoop. M and click run. Alternatively, execute closed-loop in the command window.
Browse to the recording folder and select one of the spiking data files to load the spike information defined in the initial recording. Click the appropriate check boxes under the plotted waveforms to select one or multiple neurons that will trigger stimulation. Define the minimum number of neurons that will trigger stimulation by entering an integer in the min matches text box and define the time window in which the spikes matching the different waveforms are considered coactive by entering a number into the window text box.
Then, click send to begin the online triggering of events based on the spiking activity of the selected neurons. In this representative experiment, the rat was rested on a flower pot which was a familiar resting place during breaks in the behavioral experiments. Here, a representative simultaneous running of the recording software and the MATLAB program which displays spike waveforms acquired in real time can be observed.
When spikes from predefined triggering clusters are detected, the waveforms are displayed with a bold dash line in the MATLAB figure window triggering a tone and providing a closed-loop system. In this figure, a representative histogram illustrating the delays between the time of generating an artificial spike and the time of inducing a triggered signal are shown. After preparing an animal, and installing the software, the closed-loop experiment consists of two main stages, the initial data acquisition and the closed-loop stimulation during which spikes trigger external stimuli.
This protocol also allows the treating of a stimuli based on the activity of multiple neurons which is important for addressing questions about information processing in the brain. The advantages of this MATLAB solution are its low cost and flexibility in defining the activity patterns to trigger stimuli which can help in investigating a variety of neuroscientific questions.
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Overview
This protocol demonstrates the use of an electrophysiological system for closed-loop stimulation based on neuronal activity patterns. It enables neuroscientists, even with limited computer science expertise, to conduct closed-loop experiments efficiently and cost-effectively. The protocol includes sample MATLAB code for implementation and is exemplified with recordings from an awake rat, showcasing the real-time interaction between neural spikes and stimulation.
Key Study Components
Area of Science
- Neuroscience
- Electrophysiology
- Closed-loop systems
Background
- Closed-loop stimulation is critical for studying causal interactions in the brain.
- This approach is particularly useful in epilepsy research to alleviate seizure severity.
- The protocol addresses the need for accessible closed-loop systems in neuroscience.
Purpose of Study
- To provide a method for implementing closed-loop stimulation with minimal cost and expertise.
- To facilitate the study of neuronal activity and responses through real-time stimulation.
- To enable researchers to understand information processing in the brain better.
Methods Used
- The study employs a standard neurophysiological recording system for closed-loop experiments.
- Recording from awake rats was used as the biological model.
- MATLAB code is provided for customization and conversion of open-loop systems.
- Key steps include data acquisition, sorting of spiking data, and defining stimulation parameters.
- The closed-loop experiment involves real-time triggering of stimuli based on neural activity.
Main Results
- The protocol allows for stimulation based on the activity of multiple neurons, enhancing the understanding of neural interactions.
- Spiking data were used to trigger external stimuli, demonstrating effective closed-loop feedback.
- Real-time visualization of neural activity was achieved through the MATLAB interface.
- Key findings highlight the flexibility and cost-effectiveness of the MATLAB-based solution.
Conclusions
- This study enables researchers to implement and adopt closed-loop stimulation techniques easily.
- The findings have implications for advancing the understanding of neuronal mechanisms and brain functions.
- Flexible stimulation patterns contribute to addressing various neuroscientific questions.
Frequently Asked Questions
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.
