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NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology

Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targe...

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Autores principales: Oltmanns, Sebastian, Abben, Frauke Sophie, Ender, Anatoli, Aimon, Sophie, Kovacs, Richard, Sigrist, Stephan J., Storace, Douglas A., Geiger, Jörg R. P., Raccuglia, Davide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381214/
https://www.ncbi.nlm.nih.gov/pubmed/32765213
http://dx.doi.org/10.3389/fnins.2020.00712
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author Oltmanns, Sebastian
Abben, Frauke Sophie
Ender, Anatoli
Aimon, Sophie
Kovacs, Richard
Sigrist, Stephan J.
Storace, Douglas A.
Geiger, Jörg R. P.
Raccuglia, Davide
author_facet Oltmanns, Sebastian
Abben, Frauke Sophie
Ender, Anatoli
Aimon, Sophie
Kovacs, Richard
Sigrist, Stephan J.
Storace, Douglas A.
Geiger, Jörg R. P.
Raccuglia, Davide
author_sort Oltmanns, Sebastian
collection PubMed
description Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin. In this work, we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. NOSA allows to efficiently identify oscillatory frequencies in electrical patterns, quantify neuronal response parameters and moreover provides an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions.
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spelling pubmed-73812142020-08-05 NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology Oltmanns, Sebastian Abben, Frauke Sophie Ender, Anatoli Aimon, Sophie Kovacs, Richard Sigrist, Stephan J. Storace, Douglas A. Geiger, Jörg R. P. Raccuglia, Davide Front Neurosci Neuroscience Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin. In this work, we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. NOSA allows to efficiently identify oscillatory frequencies in electrical patterns, quantify neuronal response parameters and moreover provides an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions. Frontiers Media S.A. 2020-07-14 /pmc/articles/PMC7381214/ /pubmed/32765213 http://dx.doi.org/10.3389/fnins.2020.00712 Text en Copyright © 2020 Oltmanns, Abben, Ender, Aimon, Kovacs, Sigrist, Storace, Geiger and Raccuglia. 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) and the copyright owner(s) 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
Oltmanns, Sebastian
Abben, Frauke Sophie
Ender, Anatoli
Aimon, Sophie
Kovacs, Richard
Sigrist, Stephan J.
Storace, Douglas A.
Geiger, Jörg R. P.
Raccuglia, Davide
NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology
title NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology
title_full NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology
title_fullStr NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology
title_full_unstemmed NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology
title_short NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology
title_sort nosa, an analytical toolbox for multicellular optical electrophysiology
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381214/
https://www.ncbi.nlm.nih.gov/pubmed/32765213
http://dx.doi.org/10.3389/fnins.2020.00712
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