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Alpha and theta peak frequency track on- and off-thoughts
Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thou...
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/PMC8901672/ https://www.ncbi.nlm.nih.gov/pubmed/35256748 http://dx.doi.org/10.1038/s42003-022-03146-w |
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author | Hua, Jingyu Wolff, Annemarie Zhang, Jianfeng Yao, Lin Zang, Yufeng Luo, Jing Ge, Xianliang Liu, Chang Northoff, Georg |
author_facet | Hua, Jingyu Wolff, Annemarie Zhang, Jianfeng Yao, Lin Zang, Yufeng Luo, Jing Ge, Xianliang Liu, Chang Northoff, Georg |
author_sort | Hua, Jingyu |
collection | PubMed |
description | Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics. |
format | Online Article Text |
id | pubmed-8901672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89016722022-03-22 Alpha and theta peak frequency track on- and off-thoughts Hua, Jingyu Wolff, Annemarie Zhang, Jianfeng Yao, Lin Zang, Yufeng Luo, Jing Ge, Xianliang Liu, Chang Northoff, Georg Commun Biol Article Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics. Nature Publishing Group UK 2022-03-07 /pmc/articles/PMC8901672/ /pubmed/35256748 http://dx.doi.org/10.1038/s42003-022-03146-w Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hua, Jingyu Wolff, Annemarie Zhang, Jianfeng Yao, Lin Zang, Yufeng Luo, Jing Ge, Xianliang Liu, Chang Northoff, Georg Alpha and theta peak frequency track on- and off-thoughts |
title | Alpha and theta peak frequency track on- and off-thoughts |
title_full | Alpha and theta peak frequency track on- and off-thoughts |
title_fullStr | Alpha and theta peak frequency track on- and off-thoughts |
title_full_unstemmed | Alpha and theta peak frequency track on- and off-thoughts |
title_short | Alpha and theta peak frequency track on- and off-thoughts |
title_sort | alpha and theta peak frequency track on- and off-thoughts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901672/ https://www.ncbi.nlm.nih.gov/pubmed/35256748 http://dx.doi.org/10.1038/s42003-022-03146-w |
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