The electrophysiology and structural difference between humans with distinct risky preference: a study based on EEG and MRI

Author:

Si Yajing1234,Jiang Lin234,Yi Chanlin234,Zhang Tao5ORCID,Feng Yu34,Li Peiyang6,Wan Feng7,Li Ping8,Yao Dezhong234,Li Fali234ORCID,Xu Peng234

Affiliation:

1. School of Psychology, Xinxiang Medical University , Xinxiang 453003 , China

2. The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China , Chengdu 611731 , China

3. School of Life Science and Technology , Center for Information in Medicine, , Chengdu 611731 , China

4. University of Electronic Science and Technology of China , Center for Information in Medicine, , Chengdu 611731 , China

5. Mental Health Education Center, Xihua University , Chengdu 611731 , China

6. School of Bioinformatics, Chongqing University of Posts and Telecommunications , Chongqing 404100 , China

7. Faculty of Science and Technology, University of Macau , Macau 999077 , China

8. School of Management and Economics, University of Electronic Science and Technology of China , Chengdu 610054 , China

Abstract

Abstract Risky decision-making is affected by past feedback, especially after encountering the beneficial loss in the past decision-making round, yet little is known about the mechanism accounting for the distinctive decision-making that different individuals may make under the past loss context. We extracted decision functional medial frontal negative (MFN) and the cortical thickness (CT) from multi-modality electroencephalography (EEG) and T1-weighted structural MRI (sMRI) datasets to assess the individual risky decision under the past loss context. First, concerning the MFN, the low-risk group (LRG) exhibits larger MFN amplitude and longer reaction time than the high-risk group (HRG) when making risky decisions under the loss context. Subsequently, the sMRI analysis reveals a greater CT in the left anterior insula (AI) for HRG compared with LRG, and a greater CT in AI is associated with a high level of impulsivity, driving individuals to make risky choices under the past loss context. Furthermore, for all participants, the corresponding risky decision behavior could be exactly predicted as a correlation coefficient of 0.523 was acquired, and the classification by combing the MFN amplitude and the CT of the left AI also achieves an accuracy of 90.48% to differentiate the two groups. This study may offer new insight into understanding the mechanism that accounts for the inter-individual variability of risky decisions under the loss context and denotes new indices for the prediction of the risky participants.

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Project of Science Department of Henan Province

Publisher

Oxford University Press (OUP)

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience

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