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Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as...

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Autores principales: Truong, Nghi Cong Dung, Wang, Xinlong, Wanniarachchi, Hashini, Lang, Yan, Nerur, Sridhar, Chen, Kay-Yut, Liu, Hanli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376113/
https://www.ncbi.nlm.nih.gov/pubmed/35963934
http://dx.doi.org/10.1038/s41598-022-17970-x
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author Truong, Nghi Cong Dung
Wang, Xinlong
Wanniarachchi, Hashini
Lang, Yan
Nerur, Sridhar
Chen, Kay-Yut
Liu, Hanli
author_facet Truong, Nghi Cong Dung
Wang, Xinlong
Wanniarachchi, Hashini
Lang, Yan
Nerur, Sridhar
Chen, Kay-Yut
Liu, Hanli
author_sort Truong, Nghi Cong Dung
collection PubMed
description Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text] ) or trials ([Formula: see text] ). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects’ EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.
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spelling pubmed-93761132022-08-15 Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem Truong, Nghi Cong Dung Wang, Xinlong Wanniarachchi, Hashini Lang, Yan Nerur, Sridhar Chen, Kay-Yut Liu, Hanli Sci Rep Article Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text] ) or trials ([Formula: see text] ). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects’ EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex. Nature Publishing Group UK 2022-08-13 /pmc/articles/PMC9376113/ /pubmed/35963934 http://dx.doi.org/10.1038/s41598-022-17970-x Text en © The Author(s) 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
Truong, Nghi Cong Dung
Wang, Xinlong
Wanniarachchi, Hashini
Lang, Yan
Nerur, Sridhar
Chen, Kay-Yut
Liu, Hanli
Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem
title Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem
title_full Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem
title_fullStr Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem
title_full_unstemmed Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem
title_short Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem
title_sort mapping and understanding of correlated electroencephalogram (eeg) responses to the newsvendor problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376113/
https://www.ncbi.nlm.nih.gov/pubmed/35963934
http://dx.doi.org/10.1038/s41598-022-17970-x
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