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...

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Autores principales:	Joshua J. Strohl, Joseph T. Gallagher, Pedro N. Gómez, Joshua M. Glynn, Patricio T. Huerta
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Automated spike sorting Spike clustering MountainSort Neuralynx Cheetah MATLAB Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/0d32fb4c86a545cd85cc6586ea393e11
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spelling	oai:doaj.org-article:0d32fb4c86a545cd85cc6586ea393e112021-11-28T12:05:27ZFramework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice10.1186/s42234-021-00079-32332-8886https://doaj.org/article/0d32fb4c86a545cd85cc6586ea393e112021-11-01T00:00:00Zhttps://doi.org/10.1186/s42234-021-00079-3https://doaj.org/toc/2332-8886Abstract 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.Joshua J. StrohlJoseph T. GallagherPedro N. GómezJoshua M. GlynnPatricio T. HuertaBMCarticleAutomated spike sortingSpike clusteringMountainSortNeuralynxCheetahMATLABMedical technologyR855-855.5ENBioelectronic Medicine, Vol 7, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Automated spike sorting Spike clustering MountainSort Neuralynx Cheetah MATLAB Medical technology R855-855.5
spellingShingle	Automated spike sorting Spike clustering MountainSort Neuralynx Cheetah MATLAB Medical technology R855-855.5 Joshua J. Strohl Joseph T. Gallagher Pedro N. Gómez Joshua M. Glynn Patricio T. Huerta Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice
description	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.
format	article
author	Joshua J. Strohl Joseph T. Gallagher Pedro N. Gómez Joshua M. Glynn Patricio T. Huerta
author_facet	Joshua J. Strohl Joseph T. Gallagher Pedro N. Gómez Joshua M. Glynn Patricio T. Huerta
author_sort	Joshua J. Strohl
title	Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice
title_short	Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice
title_full	Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice
title_fullStr	Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice
title_full_unstemmed	Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice
title_sort	framework for automated sorting of neural spikes from neuralynx-acquired tetrode recordings in freely-moving mice
publisher	BMC
publishDate	2021
url	https://doaj.org/article/0d32fb4c86a545cd85cc6586ea393e11
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Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice

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