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Published August 1, 2022 | Published + Supplemental Material
Journal Article Open

Resting-state functional connectivity of social brain regions predicts motivated dishonesty


Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.

Additional Information

© 2022 The Author(s). Published by Elsevier Under a Creative Commons license. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Received 7 September 2021, Revised 11 April 2022, Accepted 16 April 2022, Available online 28 April 2022, Version of Record 5 May 2022. This work is funded by the National Natural Science Foundation of China (U1736125), University of Macau (CRG2020-00001-ICI, SRG202000027-ICI), Natural Science Foundation of Guangdong Province (2021A1515012509, 2019A1515111038), the Science and Technology Development Fund (FDCT) of Macau (0127/2020/A3), and Shenzhen-Hong Kong-Macao Science and Technology Innovation Project (Category C) (SGDX2020110309280100). The authors would like to thank Mr Hao Yu who provided general support in participant recruiting. We would also like to thank Ms Xinyi Xu for the external validation dataset. Data and code availability statement The data used in this manuscript is not available due to privacy issues. The code used in this manuscript is available at https://github.com/andlab-um/restDishonesty. CRediT authorship contribution statement. Luoyao Pang: Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Huidi Li: Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Quanying Liu: Writing – original draft. Yue-Jia Luo: Supervision. Dean Mobbs: Project administration. Haiyan Wu: Investigation, Funding acquisition, Writing – original draft, Writing – review & editing, Supervision. All authors declare no competing interests.

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Published - 1-s2.0-S1053811922003482-main.pdf

Supplemental Material - 1-s2.0-S1053811922003482-mmc1.docx


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August 22, 2023
October 24, 2023