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Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology

High-density microelectrode arrays (HDMEA) have been recently introduced to study principles of neural function at high spatial resolution. However, the exact nature of the experimentally observed extracellular action potentials (EAPs) is still incompletely understood. The soma, axon and dendrites o...

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Detalles Bibliográficos
Autores principales: Deligkaris, Kosmas, Bullmann, Torsten, Frey, Urs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021702/
https://www.ncbi.nlm.nih.gov/pubmed/27683541
http://dx.doi.org/10.3389/fnins.2016.00421
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author Deligkaris, Kosmas
Bullmann, Torsten
Frey, Urs
author_facet Deligkaris, Kosmas
Bullmann, Torsten
Frey, Urs
author_sort Deligkaris, Kosmas
collection PubMed
description High-density microelectrode arrays (HDMEA) have been recently introduced to study principles of neural function at high spatial resolution. However, the exact nature of the experimentally observed extracellular action potentials (EAPs) is still incompletely understood. The soma, axon and dendrites of a neuron can all exhibit regenerative action potentials that could be sensed with HDMEA electrodes. Here, we investigate the contribution of distinct neuronal sources of activity in HDMEA recordings from low-density neuronal cultures. We recorded EAPs with HDMEAs having 11,011 electrodes and then fixed and immunostained the cultures with β3-tubulin for high-resolution fluorescence imaging. Immunofluorescence images overlaid with the activity maps showed EAPs both at neuronal somata and distal neurites. Neuritic EAPs had mostly narrow triphasic shapes, consisting of a positive, a pronounced negative peak and a second positive peak. EAPs near somata had wide monophasic or biphasic shapes with a main negative peak, and following optional positive peak. We show that about 86% of EAP recordings consist of somatic spikes, while the remaining 14% represent neuritic spikes. Furthermore, the adaptation of the waveform shape during bursts of these neuritic spikes suggested that they originate from axons, rather than from dendrites. Our study improves the understanding of HDMEA signals and can aid in the identification of the source of EAPs.
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spelling pubmed-50217022016-09-28 Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology Deligkaris, Kosmas Bullmann, Torsten Frey, Urs Front Neurosci Neuroscience High-density microelectrode arrays (HDMEA) have been recently introduced to study principles of neural function at high spatial resolution. However, the exact nature of the experimentally observed extracellular action potentials (EAPs) is still incompletely understood. The soma, axon and dendrites of a neuron can all exhibit regenerative action potentials that could be sensed with HDMEA electrodes. Here, we investigate the contribution of distinct neuronal sources of activity in HDMEA recordings from low-density neuronal cultures. We recorded EAPs with HDMEAs having 11,011 electrodes and then fixed and immunostained the cultures with β3-tubulin for high-resolution fluorescence imaging. Immunofluorescence images overlaid with the activity maps showed EAPs both at neuronal somata and distal neurites. Neuritic EAPs had mostly narrow triphasic shapes, consisting of a positive, a pronounced negative peak and a second positive peak. EAPs near somata had wide monophasic or biphasic shapes with a main negative peak, and following optional positive peak. We show that about 86% of EAP recordings consist of somatic spikes, while the remaining 14% represent neuritic spikes. Furthermore, the adaptation of the waveform shape during bursts of these neuritic spikes suggested that they originate from axons, rather than from dendrites. Our study improves the understanding of HDMEA signals and can aid in the identification of the source of EAPs. Frontiers Media S.A. 2016-09-14 /pmc/articles/PMC5021702/ /pubmed/27683541 http://dx.doi.org/10.3389/fnins.2016.00421 Text en Copyright © 2016 Deligkaris, Bullmann and Frey. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Deligkaris, Kosmas
Bullmann, Torsten
Frey, Urs
Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology
title Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology
title_full Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology
title_fullStr Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology
title_full_unstemmed Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology
title_short Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology
title_sort extracellularly recorded somatic and neuritic signal shapes and classification algorithms for high-density microelectrode array electrophysiology
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021702/
https://www.ncbi.nlm.nih.gov/pubmed/27683541
http://dx.doi.org/10.3389/fnins.2016.00421
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AT freyurs extracellularlyrecordedsomaticandneuriticsignalshapesandclassificationalgorithmsforhighdensitymicroelectrodearrayelectrophysiology