Framework for acquisition and automated sorting of neural spikes from multielectrode recordings in freely-moving mice

Abstract BackgroundExtracellular 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 are able to 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. The purpose of this study is to provide a simple-to-execute framework for using MountainSort, an automated spike sorting pipeline, in conjunction with MATLAB and the acquisition system (Cheetah, Neuralynx). We validate this automated framework with neural recordings from the hippocampus and prelimbic cortex. MethodsMultielectrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Multielectrode 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. ResultsWe 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. ConclusionsAutomated spike sorting is a necessity for medium and large-scale extracellular neural recordings. Here, we have smoothly integrated MountainSort-based spike sorting into a framework for acquisition and analysis of multielectrode brain recordings in mice.

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Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice

Bioelectronic Medicine ◽

10.1186/s42234-021-00079-3 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Joshua J. Strohl ◽

Joseph T. Gallagher ◽

Pedro N. Gómez ◽

Joshua M. Glynn ◽

Patricio T. Huerta

Keyword(s):

Action Potentials ◽

Extracellular Recording ◽

High Density ◽

Spike Sorting ◽

Prelimbic Cortex ◽

Neural Recordings ◽

Ca1 Region ◽

Freely Moving ◽

Electrophysiological Technique ◽

Tetrode Recordings

Abstract 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.

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Extracellular detection of neuronal coupling

Scientific Reports ◽

10.1038/s41598-021-94282-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Elmer Guzman ◽

Zhuowei Cheng ◽

Paul K. Hansma ◽

Kenneth R. Tovar ◽

Linda R. Petzold ◽

...

Keyword(s):

Action Potentials ◽

Microelectrode Arrays ◽

Short Latency ◽

Spike Sorting ◽

Neuronal Connectivity ◽

Direct Stimulation ◽

Multiple Electrodes

AbstractWe developed a method to non-invasively detect synaptic relationships among neurons from in vitro networks. Our method uses microelectrode arrays on which neurons are cultured and from which propagation of extracellular action potentials (eAPs) in single axons are recorded at multiple electrodes. Detecting eAP propagation bypasses ambiguity introduced by spike sorting. Our methods identify short latency spiking relationships between neurons with properties expected of synaptically coupled neurons, namely they were recapitulated by direct stimulation and were sensitive to changing the number of active synaptic sites. Our methods enabled us to assemble a functional subset of neuronal connectivity in our cultures.

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Building population models for large-scale neural recordings: Opportunities and pitfalls

Current Opinion in Neurobiology ◽

10.1016/j.conb.2021.07.003 ◽

2021 ◽

Vol 70 ◽

pp. 64-73

Author(s):

Cole Hurwitz ◽

Nina Kudryashova ◽

Arno Onken ◽

Matthias H. Hennig

Keyword(s):

Large Scale ◽

Population Models ◽

Neural Recordings

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T-SNE visualization of large-scale neural recordings

10.1101/087395 ◽

2016 ◽

Cited By ~ 5

Author(s):

George Dimitriadis ◽

Joana Neto ◽

Adam R. Kampff

Keyword(s):

Large Scale ◽

New Technologies ◽

Dimensional Space ◽

Clustering Algorithms ◽

Brain Regions ◽

Data Sets ◽

Neural Recordings ◽

Sorting Problem ◽

Feature Spaces ◽

High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

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An approach for long-term, multi-probe Neuropixels recordings in unrestrained rats

eLife ◽

10.7554/elife.59716 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 1

Author(s):

Thomas Zhihao Luo ◽

Adrian Gopnik Bondy ◽

Diksha Gupta ◽

Verity Alexander Elliott ◽

Charles D Kopec ◽

...

Keyword(s):

Cost Effective ◽

New Approach ◽

Neural Recordings ◽

Chronic Recording ◽

Long Term Stability ◽

Multiple Cycles ◽

Single Unit Recordings ◽

Freely Moving ◽

High Yields

The use of Neuropixels probes for chronic neural recordings is in its infancy and initial studies leave questions about long-term stability and probe reusability unaddressed. Here, we demonstrate a new approach for chronic Neuropixels recordings over a period of months in freely moving rats. Our approach allows multiple probes per rat and multiple cycles of probe reuse. We found that hundreds of units could be recorded for multiple months, but that yields depended systematically on anatomical position. Explanted probes displayed a small increase in noise compared to unimplanted probes, but this was insufficient to impair future single-unit recordings. We conclude that cost-effective, multi-region, and multi-probe Neuropixels recordings can be carried out with high yields over multiple months in rats or other similarly sized animals. Our methods and observations may facilitate the standardization of chronic recording from Neuropixels probes in freely moving animals.

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Using the Grid to Test the ATLAS Trigger and Data Acquisition System at Large Scale

2007 15th IEEE-NPSS Real-Time Conference ◽

10.1109/rtc.2007.4382817 ◽

2007 ◽

Author(s):

Alessandra Forti ◽

Hegoi Garitaonandia ◽

Jiri Masik ◽

Sarah Wheeler ◽

Thorsten Wengler

Keyword(s):

Data Acquisition ◽

Large Scale ◽

Data Acquisition System ◽

Acquisition System

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YASS: Yet Another Spike Sorter

10.1101/151928 ◽

2017 ◽

Cited By ~ 14

Author(s):

JinHyung Lee ◽

David Carlson ◽

Hooshmand Shokri ◽

Weichi Yao ◽

Georges Goetz ◽

...

Keyword(s):

Dirichlet Process ◽

Large Scale ◽

Matching Pursuit ◽

Spike Sorting ◽

Dirichlet Process Mixture ◽

Electrode Arrays ◽

Neural Signals ◽

Model Framework ◽

Multi Stage ◽

Computational Bottleneck

AbstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a “coreset” approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The “triaged” waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a ≃ 500-electrode dataset, using primarily a single CPU core.

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General Relativity and Cosmology

Principles of Physical Cosmology ◽

10.23943/princeton/9780691209814.003.0002 ◽

2020 ◽

pp. 227-360

Author(s):

P. J. E. Peebles

Keyword(s):

Constant Velocity ◽

Large Scale ◽

Reference Frames ◽

Physical Sciences ◽

Inertial Frames ◽

Moving Body ◽

History Of ◽

The Subject ◽

Freely Moving ◽

The Universe

This chapter discusses the development of physical sciences in seemingly chaotic ways, by paths that are at best dimly seen at the time. It refers to the history of ideas as an important part of any science, and particularly worth examining in cosmology, where the subject has evolved over several generations. It also examines the puzzle of inertia, which traces the connection to Albert Einstein's bold idea that the universe is homogeneous in the large-scale average called “cosmological principle.” The chapter cites Newtonian mechanics that defines a set of preferred motions in space, the inertial reference frames, by the condition that a freely moving body has a constant velocity. It talks about Ernst Mach, who argued that inertial frames are determined relative to the motion of the rest of the matter in the universe.

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