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Quantifying evoked responses through information-theoretical measures

Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data struc...

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Autores principales: Fuhrer, Julian, Glette, Kyrre, Llorens, Anaïs, Endestad, Tor, Solbakk, Anne-Kristin, Blenkmann, Alejandro Omar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10242156/
https://www.ncbi.nlm.nih.gov/pubmed/37287586
http://dx.doi.org/10.3389/fninf.2023.1128866
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author Fuhrer, Julian
Glette, Kyrre
Llorens, Anaïs
Endestad, Tor
Solbakk, Anne-Kristin
Blenkmann, Alejandro Omar
author_facet Fuhrer, Julian
Glette, Kyrre
Llorens, Anaïs
Endestad, Tor
Solbakk, Anne-Kristin
Blenkmann, Alejandro Omar
author_sort Fuhrer, Julian
collection PubMed
description Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.
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spelling pubmed-102421562023-06-07 Quantifying evoked responses through information-theoretical measures Fuhrer, Julian Glette, Kyrre Llorens, Anaïs Endestad, Tor Solbakk, Anne-Kristin Blenkmann, Alejandro Omar Front Neuroinform Neuroscience Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present. Frontiers Media S.A. 2023-05-23 /pmc/articles/PMC10242156/ /pubmed/37287586 http://dx.doi.org/10.3389/fninf.2023.1128866 Text en Copyright © 2023 Fuhrer, Glette, Llorens, Endestad, Solbakk and Blenkmann. https://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
Fuhrer, Julian
Glette, Kyrre
Llorens, Anaïs
Endestad, Tor
Solbakk, Anne-Kristin
Blenkmann, Alejandro Omar
Quantifying evoked responses through information-theoretical measures
title Quantifying evoked responses through information-theoretical measures
title_full Quantifying evoked responses through information-theoretical measures
title_fullStr Quantifying evoked responses through information-theoretical measures
title_full_unstemmed Quantifying evoked responses through information-theoretical measures
title_short Quantifying evoked responses through information-theoretical measures
title_sort quantifying evoked responses through information-theoretical measures
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10242156/
https://www.ncbi.nlm.nih.gov/pubmed/37287586
http://dx.doi.org/10.3389/fninf.2023.1128866
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