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Maximum entropy models provide functional connectivity estimates in neural networks
Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons ten...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187636/ https://www.ncbi.nlm.nih.gov/pubmed/35688933 http://dx.doi.org/10.1038/s41598-022-13674-4 |
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author | Lamberti, Martina Hess, Michael Dias, Inês van Putten, Michel le Feber, Joost Marzen, Sarah |
author_facet | Lamberti, Martina Hess, Michael Dias, Inês van Putten, Michel le Feber, Joost Marzen, Sarah |
author_sort | Lamberti, Martina |
collection | PubMed |
description | Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks. |
format | Online Article Text |
id | pubmed-9187636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91876362022-06-12 Maximum entropy models provide functional connectivity estimates in neural networks Lamberti, Martina Hess, Michael Dias, Inês van Putten, Michel le Feber, Joost Marzen, Sarah Sci Rep Article Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks. Nature Publishing Group UK 2022-06-10 /pmc/articles/PMC9187636/ /pubmed/35688933 http://dx.doi.org/10.1038/s41598-022-13674-4 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Lamberti, Martina Hess, Michael Dias, Inês van Putten, Michel le Feber, Joost Marzen, Sarah Maximum entropy models provide functional connectivity estimates in neural networks |
title | Maximum entropy models provide functional connectivity estimates in neural networks |
title_full | Maximum entropy models provide functional connectivity estimates in neural networks |
title_fullStr | Maximum entropy models provide functional connectivity estimates in neural networks |
title_full_unstemmed | Maximum entropy models provide functional connectivity estimates in neural networks |
title_short | Maximum entropy models provide functional connectivity estimates in neural networks |
title_sort | maximum entropy models provide functional connectivity estimates in neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187636/ https://www.ncbi.nlm.nih.gov/pubmed/35688933 http://dx.doi.org/10.1038/s41598-022-13674-4 |
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