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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...

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Autores principales: Hua, Jingyu, Wolff, Annemarie, Zhang, Jianfeng, Yao, Lin, Zang, Yufeng, Luo, Jing, Ge, Xianliang, Liu, Chang, Northoff, Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
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.
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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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