机构：[1]The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu611731, China,[2]School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China,[3]School of Psychology, Xinxiang Medical University, Xinxiang 453003, China,[4]School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China,[5]Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China,[6]Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China,[7]Department of Neurology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China,四川省人民医院[8]Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China四川省人民医院
Despite node-centric studies revealing an association between resting-state functional connectivity and individual risk propensity, the prediction of future risk decisions remains undetermined. Herein, we applied a recently emerging edge-centric method, the edge community similarity network (ECSN), to alternatively describe the community structure of resting-state brain activity and to probe its contribution to predicting risk propensity during gambling. Results demonstrated that inter-individual variability of risk decisions correlates with the inter-subnetwork couplings spanning the visual network (VN) and default mode network (DMN), cingulo-opercular task control network, and sensory/somatomotor hand network (SSHN). Particularly, participants who have higher community similarity of these subnetworks during the resting state tend to choose riskier and higher yielding bets. And in contrast to low-risk propensity participants, those who behave high-risky show stronger couplings spanning the VN and SSHN/DMN. Eventually, based on the resting-state ECSN properties, the risk rate during the gambling task is effectively predicted by the multivariable linear regression model at the individual level. These findings provide new insights into the neural substrates of the inter-individual variability in risk propensity and new neuroimaging metrics to predict individual risk decisions in advance.
基金：
National Natural Science Foundation of China [62103085, U19A2082]; STI 2030-Major Projects [2022ZD0208500, 2022ZD02114000, 2022ZD0208900]; Key R8zD projects of Science 8z Technology Department of Sichuan Province [23ZDYF0961]; Scientific Research Foundation of Sichuan Provincial People's Hospital [2021LY21]
第一作者机构：[1]The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu611731, China,[2]School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China,
通讯作者：
通讯机构：[1]The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu611731, China,[2]School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China,[5]Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China,[6]Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China,[7]Department of Neurology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China,[8]Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China[*1]No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, Sichuan, China.
推荐引用方式(GB/T 7714)：
Jiang Lin,Yang Qingqing,He Runyang,et al.Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study[J].CEREBRAL CORTEX.2023,33(14):8904-8912.doi:10.1093/cercor/bhad169.
APA：
Jiang, Lin,Yang, Qingqing,He, Runyang,Wang, Guangying,Yi, Chanlin...&Li, Fali.(2023).Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study.CEREBRAL CORTEX,33,(14)
MLA：
Jiang, Lin,et al."Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study".CEREBRAL CORTEX 33..14(2023):8904-8912
