Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1785054153042558976 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10242156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fuhrerjulian quantifyingevokedresponsesthroughinformationtheoreticalmeasures AT glettekyrre quantifyingevokedresponsesthroughinformationtheoreticalmeasures AT llorensanais quantifyingevokedresponsesthroughinformationtheoreticalmeasures AT endestadtor quantifyingevokedresponsesthroughinformationtheoreticalmeasures AT solbakkannekristin quantifyingevokedresponsesthroughinformationtheoreticalmeasures AT blenkmannalejandroomar quantifyingevokedresponsesthroughinformationtheoreticalmeasures |