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Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram
Published on: July 7, 2023
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Graph theory analysis reveals an assortative pain network vulnerable to attacks.
Chong Chen1,2,3, Adrien Tassou4,5,6, Valentina Morales4,5,6
1Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. chong_chen@med.unc.edu.
Scientific Reports
|December 11, 2023
Summary
Graph theory reveals the brain
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- The brain's pain network is complex, with its structural connectivity poorly understood.
- Understanding pain network architecture is crucial for deciphering pain perception.
Purpose of the Study:
- To systematically characterize the structural architecture of the human pain network using graph theory.
- To identify subnetworks and assess network properties like robustness and assortativity.
Main Methods:
- Employed graph theory to analyze a structural brain network of 49 pain-related brain regions.
- Calculated network properties including clustering coefficient, community structure, assortativity, and robustness.
Main Results:
- Identified a dense, assortative pain network with significant reciprocal connections (63%).
- Discovered two subnetworks mediating sensory and affective pain components.
- The network shows high error tolerance but vulnerability to targeted attacks on hub nodes.
Conclusions:
- Graph theory analysis reveals an assortative structural pain network processing nociceptive information.
- The network's vulnerability suggests potential therapeutic strategies targeting key brain regions for pain relief.
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