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

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

10.21203/rs.3.rs-842123/v1 ◽

2021 ◽

Author(s):

Joshua J. Strohl ◽

Joseph T. Gallagher ◽

Pedro N. Gomez ◽

Joshua M. Glynn ◽

Patricio T. Huerta

Keyword(s):

Action Potentials ◽

Large Scale ◽

Acquisition System ◽

Spike Sorting ◽

Prelimbic Cortex ◽

Neural Recordings ◽

Ca1 Region ◽

Freely Moving ◽

Multielectrode Recordings ◽

Electrophysiological Technique

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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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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Synchronized Intracellular and Extracellular Recording of Action Potentials by Three-Dimensional Nanoroded Electroporation

Biosensors and Bioelectronics ◽

10.1016/j.bios.2021.113501 ◽

2021 ◽

pp. 113501

Author(s):

Dongxin Xu ◽

Jiaru Fang ◽

Mingyue Zhang ◽

Hao Wang ◽

Tao Zhang ◽

...

Keyword(s):

Action Potentials ◽

Three Dimensional ◽

Extracellular Recording

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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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Signal-to-peak-interference ratio maximization with automatic interference weighting for threshold-based spike sorting of high-density neural probe data

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) ◽

10.1109/ner.2019.8716953 ◽

2019 ◽

Author(s):

Jasper Wouters ◽

Fabian Kloosterman ◽

Alexander Bertrand

Keyword(s):

High Density ◽

Spike Sorting ◽

Neural Probe

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Motor unit filter prelearning strategies for decomposition of compound muscle action potentials in high-density surface electromyograms*

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ◽

10.1109/embc44109.2020.9175479 ◽

2020 ◽

Cited By ~ 1

Author(s):

A. Holobar ◽

A. Francic

Keyword(s):

Motor Unit ◽

Action Potentials ◽

High Density ◽

Muscle Action ◽

Compound Muscle Action Potentials ◽

Density Surface ◽

Surface Electromyograms

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A low-cost, multiplexedμECoG system for high-density recordings in freely moving rodents

Journal of Neural Engineering ◽

10.1088/1741-2560/13/2/026030 ◽

2016 ◽

Vol 13 (2) ◽

pp. 026030 ◽

Cited By ~ 18

Author(s):

Michele Insanally ◽

Michael Trumpis ◽

Charles Wang ◽

Chia-Han Chiang ◽

Virginia Woods ◽

...

Keyword(s):

Low Cost ◽

High Density ◽

Freely Moving

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Automatic spike sorting for high-density microelectrode arrays

Journal of Neurophysiology ◽

10.1152/jn.00803.2017 ◽

2018 ◽

Vol 120 (6) ◽

pp. 3155-3171 ◽

Cited By ~ 13

Author(s):

Roland Diggelmann ◽

Michele Fiscella ◽

Andreas Hierlemann ◽

Felix Franke

Keyword(s):

Principal Component Analysis ◽

Clustering Algorithm ◽

State Of The Art ◽

Microelectrode Arrays ◽

Principal Component ◽

Component Analysis ◽

High Density ◽

Sorting Algorithm ◽

Spike Sorting ◽

Recording Electrodes

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the “curse of dimensionality” and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

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