Computational Modeling of Prefrontal-Cingulate Coupling: Exploring Its Role in Balancing Emotion and Cognition

Academic Background

In recent years, emotional processing and cognitive control, which are crucial for maintaining normal social behavior and executive function, have attracted widespread attention. This study explores how the balance between these two key brain functions is affected by neural network coupling, and how changes in this coupling relationship can lead to psychological disorders, especially depression, with the hope of advancing the diagnosis and treatment of depression.

Research Source

This article was written by Wei Jinchao, Li Licong, Zhang Jiayi, Shi Ersong, Yang Jianli, and Liu Xiuling, all from the Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, and published in the journal “Neurosci. Bull.”. The article was received on December 21, 2023, and accepted on February 11, 2024.

Research Process

The study constructed a biophysical computational model including the dorsolateral prefrontal cortex (dlPFC), ventromedial prefrontal cortex (vMPFC), and subgenual anterior cingulate cortex (sgACC) to explore how the coupling relationships between these brain regions in the neural network adjust the balance between emotion and cognition.

Main Research Findings

1. Numerical results confirm that coupling weights play a crucial role in maintaining the emotion-cognition network.
2. The model predicts that abnormal activation of sgACC may be one of the pathological mechanisms triggering depression, and network function can be restored through dlPFC intervention.

Conclusions and Value

The study presents the important role of sgACC as a central hub in the emotion-cognition network and points out the role of coupling weights in regulating the balance between emotion and cognition. It provides new perspectives for the etiology and treatment of depression.

Research Highlights

• The study identified key neural network coupling relationships on which the balance between emotion and cognition depends.
• Through computational modeling, it revealed the dynamic balance between emotional and cognitive networks and their potential impact on emotional disorders.

Other Important Information

The article focuses on the application of computational models in mental disorder research, suggesting the unique value of computational modeling methods in explaining human brain computational properties and providing a theoretical framework for exploring the interrelationship between emotion and cognition.

Report Summary

This study successfully used computational models to predict different characteristics of emotion-cognition network coupling in healthy human brains and those of depression patients, targeting specific neural circuit structures. The research not only helps to understand the neurobiological basis and neural circuits of depression but also provides possible theoretical support for future treatment methods of depression.