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Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
Published on: June 13, 2025
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Different Decision-Making Responses Occupy Different Brain Networks for Information Processing: A Study Based on EEG
Yajing Si1,2, Xi Wu3, Fali Li1,2
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
Cerebral Cortex (New York, N.Y. : 1991)
|December 12, 2018
Summary
Brain network patterns differ for accepting or rejecting unfair offers. Modulating frontal cortex activity with transcranial magnetic stimulation (TMS) altered decision-making, suggesting a potential intervention model.
Area of Science:
- Neuroscience
- Cognitive Science
- Decision-Making Research
Background:
- Understanding the neural mechanisms underlying decision-making is crucial for various fields.
- Previous research has explored brain activity during decision tasks, but dynamic network patterns remain less understood.
Purpose of the Study:
- To investigate distinct brain network structures associated with accepting versus rejecting unfair offers.
- To validate a network model of decision-making through further experiments and targeted brain stimulation.
Main Methods:
- Employed large-scale time-varying network analysis to map brain activity during decision stages.
- Conducted validation studies using network hub node analysis and transcranial magnetic stimulation (TMS) targeting the frontal cortex.
Main Results:
- Accepting unfair offers involves a bottom-up information flow from visual to frontal areas.
- Rejecting unfair offers shows a top-down flow from frontal to parietal and occipital cortices.
- Frontal cortex modulation via TMS significantly altered acceptance rates, confirming the network model's validity.
Conclusions:
- Decision-making processes for acceptance and rejection are characterized by distinct large-scale brain network dynamics.
- The identified network model provides insights into neural decision-making and suggests potential for targeted interventions.
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