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Integrating operant behavior and fiber photometry with the open-source python library Pyfiber

Despite the popularity of fiber photometry (FP), its integration with operant behavior paradigms is progressing slowly. This can be attributed to the complex protocols in operant behavior – resulting in a combination of diverse non-predictable behavioral responses and scheduled events, thereby compl...

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Autores principales: Conlisk, Dana, Ceau, Matias, Fiancette, Jean-François, Winke, Nanci, Darmagnac, Elise, Herry, Cyril, Deroche-Gamonet, Véronique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545777/
https://www.ncbi.nlm.nih.gov/pubmed/37783729
http://dx.doi.org/10.1038/s41598-023-43565-1
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author Conlisk, Dana
Ceau, Matias
Fiancette, Jean-François
Winke, Nanci
Darmagnac, Elise
Herry, Cyril
Deroche-Gamonet, Véronique
author_facet Conlisk, Dana
Ceau, Matias
Fiancette, Jean-François
Winke, Nanci
Darmagnac, Elise
Herry, Cyril
Deroche-Gamonet, Véronique
author_sort Conlisk, Dana
collection PubMed
description Despite the popularity of fiber photometry (FP), its integration with operant behavior paradigms is progressing slowly. This can be attributed to the complex protocols in operant behavior – resulting in a combination of diverse non-predictable behavioral responses and scheduled events, thereby complicating data analysis. To overcome this, we developed Pyfiber, an open-source python library which facilitates the merge of FP with operant behavior by relating changes in fluorescent signals within a neuronal population to behavioral responses and events. Pyfiber helps to 1. Extract events and responses that occur in operant behavior, 2. Extract and process the FP signals, 3. Select events of interest and align them to the corresponding FP signals, 4. Apply appropriate signal normalization and analysis according to the type of events, 5. Run analysis on multiple individuals and sessions, 6. Collect results in an easily readable format. Pyfiber is suitable for use with many different fluorescent sensors and operant behavior protocols. It was developed using Doric lenses FP systems and Imetronic behavioral systems, but it possesses the capability to process data from alternative systems. This work sets a solid foundation for analyzing the relationship between different dimensions of complex behavioral paradigms with fluorescent signals from brain regions of interest.
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spelling pubmed-105457772023-10-04 Integrating operant behavior and fiber photometry with the open-source python library Pyfiber Conlisk, Dana Ceau, Matias Fiancette, Jean-François Winke, Nanci Darmagnac, Elise Herry, Cyril Deroche-Gamonet, Véronique Sci Rep Article Despite the popularity of fiber photometry (FP), its integration with operant behavior paradigms is progressing slowly. This can be attributed to the complex protocols in operant behavior – resulting in a combination of diverse non-predictable behavioral responses and scheduled events, thereby complicating data analysis. To overcome this, we developed Pyfiber, an open-source python library which facilitates the merge of FP with operant behavior by relating changes in fluorescent signals within a neuronal population to behavioral responses and events. Pyfiber helps to 1. Extract events and responses that occur in operant behavior, 2. Extract and process the FP signals, 3. Select events of interest and align them to the corresponding FP signals, 4. Apply appropriate signal normalization and analysis according to the type of events, 5. Run analysis on multiple individuals and sessions, 6. Collect results in an easily readable format. Pyfiber is suitable for use with many different fluorescent sensors and operant behavior protocols. It was developed using Doric lenses FP systems and Imetronic behavioral systems, but it possesses the capability to process data from alternative systems. This work sets a solid foundation for analyzing the relationship between different dimensions of complex behavioral paradigms with fluorescent signals from brain regions of interest. Nature Publishing Group UK 2023-10-02 /pmc/articles/PMC10545777/ /pubmed/37783729 http://dx.doi.org/10.1038/s41598-023-43565-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Conlisk, Dana
Ceau, Matias
Fiancette, Jean-François
Winke, Nanci
Darmagnac, Elise
Herry, Cyril
Deroche-Gamonet, Véronique
Integrating operant behavior and fiber photometry with the open-source python library Pyfiber
title Integrating operant behavior and fiber photometry with the open-source python library Pyfiber
title_full Integrating operant behavior and fiber photometry with the open-source python library Pyfiber
title_fullStr Integrating operant behavior and fiber photometry with the open-source python library Pyfiber
title_full_unstemmed Integrating operant behavior and fiber photometry with the open-source python library Pyfiber
title_short Integrating operant behavior and fiber photometry with the open-source python library Pyfiber
title_sort integrating operant behavior and fiber photometry with the open-source python library pyfiber
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545777/
https://www.ncbi.nlm.nih.gov/pubmed/37783729
http://dx.doi.org/10.1038/s41598-023-43565-1
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