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Published November 2022 | Published + Supplemental Material
Journal Article Open

Dynamic neural reconfiguration for distinct strategies during competitive social interactions


Information exchange between brain regions is key to understanding information processing for social decision-making, but most analyses ignore its dynamic nature. New insights on this dynamic might help us to uncover the neural correlates of social cognition in the healthy population and also to understand the malfunctioning neural computations underlying dysfunctional social behavior in patients with mental disorders. In this work, we used a multi-round bargaining game to detect switches between distinct bargaining strategies in a cohort of 76 healthy participants. These switches were uncovered by dynamic behavioral modeling using the hidden Markov model. Proposing a novel model of dynamic effective connectivity to estimate the information flow between key brain regions, we found a stronger interaction between the right temporoparietal junction (rTPJ) and the right dorsolateral prefrontal cortex (rDLPFC) for the strategic deception compared with the social heuristic strategies. The level of deception was associated with the information flow from the Brodmann area 10 to the rTPJ, and this association was modulated by the rTPJ-to-rDLPFC information flow. These findings suggest that dynamic bargaining strategy is supported by dynamic reconfiguration of the rDLPFC-and-rTPJ interaction during competitive social interactions.

Additional Information

© 2022 The Authors. Published by Elsevier. Under a Creative Commons license. Attribution 4.0 International (CC BY 4.0) This study was partially supported by grants from the National Key Research and Development Program of China (No. 2019YFA0709502), the National Natural Science Foundation of China (Nos. 81873909 and 12072236), the Science and Technology Commission of Shanghai Municipality (Nos. 20ZR1404900 and 20DZ2260300), the Shanghai Municipal Science and Technology Major Project (Nos. 2018SHZDZX01 and 2021SHZDZX0103), and the Fundamental Research Funds for the Central Universities. CRediT authorship contribution statement. Ruihan Yang: Methodology, Investigation, Visualization, Writing – original draft. Yina Ma: Methodology, Writing – review & editing. Bao-Bao Pan: Methodology, Investigation, Visualization, Writing – original draft. Meghana A. Bhatt: Methodology, Investigation, Writing – original draft. Terry Lohrenz: Methodology, Writing – review & editing. Hua-Guang Gu: Methodology, Investigation, Writing – review & editing. Jonathan W. Kanen: Investigation, Writing – review & editing. Colin F. Camerer: Conceptualization, Supervision, Writing – review & editing. P. Read Montague: Conceptualization, Supervision, Writing – review & editing. Qiang Luo: Conceptualization, Methodology, Investigation, Visualization, Supervision, Writing – original draft. Data and code availability statement. The datasets and code generated and analysed during the current study are available at https://github.com/rhyang2021/data-code4TVGCSDN. A Matlab toolbox of this algorithm is also available at https://github.com/qluo2018/GCSDN. Dynamic neural reconfiguration for distinct strategies during competitive social interactions (Mendeley Data). Human ethics statements. The work described has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. Authors declare that they have no conflict of interest.

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Published - 1-s2.0-S1053811922007005-main.pdf

Supplemental Material - 1-s2.0-S1053811922007005-mmc1.pdf


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Additional details

August 22, 2023
October 23, 2023