Different Decision-Making Responses Occupy Different Brain Networks for Information Processing: A Study Based on EEG and TMS

Author:

Si Yajing12,Wu Xi3,Li Fali12,Zhang Luyan12,Duan Keyi12,Li Peiyang12,Song Limeng12,Jiang Yuanling12,Zhang Tao124ORCID,Zhang Yangsong125,Chen Jing6,Gao Shan7,Biswal Bharat128ORCID,Yao Dezhong12,Xu Peng12

Affiliation:

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

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

3. Business School, Sichuan Normal University, Chengdu 610101, China

4. Center for Mental Health Development and Research, Xihua University, Chengdu 610039, China

5. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China

6. Research Center of Psychological Development and Application, Sichuan Normal University, Chengdu 610101, China

7. School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu 611731, China

8. Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

Abstract

Abstract This study used large-scale time-varying network analysis to reveal the diverse network patterns during the different decision stages and found that the responses of rejection and acceptance involved different network structures. When participants accept unfair offers, the brain recruits a more bottom-up mechanism with a much stronger information flow from the visual cortex (O2) to the frontal area, but when they reject unfair offers, it displayed a more top-down flow derived from the frontal cortex (Fz) to the parietal and occipital cortices. Furthermore, we performed 2 additional studies to validate the above network models: one was to identify the 2 responses based on the out-degree information of network hub nodes, which results in 70% accuracy, and the other utilized theta burst stimulation (TBS) of transcranial magnetic stimulation (TMS) to modulate the frontal area before the decision-making tasks. We found that the intermittent TBS group demonstrated lower acceptance rates and that the continuous TBS group showed higher acceptance rates compared with the sham group. Similar effects were not observed after TBS of a control site. These results suggest that the revealed decision-making network model can serve as a potential intervention model to alter decision responses.

Funder

National Key Research and Development Plan of China

National Natural Science Foundation of China

Open Foundation of Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology

Longshan academic research supporting program of SWUST

Sichuan Science and Technology Program

Publisher

Oxford University Press (OUP)

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience

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