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On the Influence of Structural Connectivity on the Correlation Patterns and Network Synchronization.
Parisa Sadat Nazemi1, Yousef Jamali1
1Department of Mathematics, Tarbiat Modares University, Tehran, Iran.
Frontiers in Computational Neuroscience
|January 24, 2019
Summary
Understanding brain function requires linking structural and functional connectivity. This study shows how network topology and coupling strength influence neural synchronization and correlation patterns, potentially reflecting cognitive states.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Network Science
Background:
- Brain function relies on structural connectivity.
- Understanding the relationship between structural and functional connectivity is key to understanding brain abilities.
- Previous studies explored structural connectivity's role in emergent correlation/synchronization patterns.
Purpose of the Study:
- Investigate cross-correlation and synchronization sensitivity to coupling strength in neural networks with varying topologies.
- Model neural populations using a neural mass model exhibiting oscillatory dynamics.
- Explore how network topology and synaptic strength influence neural dynamics and functional connectivity.
Main Methods:
- Utilized a neural mass model to simulate neural population dynamics.
- Analyzed cross-correlation and synchronization patterns under different coupling strengths.
- Examined network topology variations, from ordered to small-world architectures.
Main Results:
- Neural ensemble coupling generates diverse cross-correlation and local synchrony patterns, even in ordered networks.
- Network topology changes, particularly towards small-world architectures, alter correlation and synchronization dynamics.
- Synaptic strength influences emergent patterns, with variations observed across different input signals and transitions to bifurcation regions at higher strengths.
Conclusions:
- Topological variations significantly impact synchronization behavior and correlated local clusters.
- Coupling strength is crucial for state transitions in correlation and synchronization.
- Diverse correlation patterns may represent distinct dynamical states linked to various cognitive tasks.
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