ArticleFrontiers in computational neuroscience2018
On the Influence of Structural Connectivity on the Correlation Patterns and Network Synchronization.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Cell-Type-Specific Effects of Somatostatin on Synaptic Transmission in the Anterior Cingulate Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models.PloS one · 2024Article
- Synchronised neural signature of creative mental imagery in reality and augmented reality.Heliyon · 2022Article
- Phase synchronization and measure of criticality in a network of neural mass models.Scientific reports · 2022Article
- Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses.Frontiers in systems neuroscience · 2020Article
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2 authors.
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Abstract
Since brain structural connectivity is the foundation of its functionality, in order to understand brain abilities, studying the relation between structural and functional connectivity is essential. Several approaches have been applied to measure the role of the structural connectivity in the emergent correlation/synchronization patterns. In this study, we investigates the cross-correlation and synchronization sensitivity to coupling strength between neural regions for different topological networks. We model the neural populations by a neural mass model that express an oscillatory dynamic. The results highlight that coupling between neural ensembles leads to various cross-correlation patterns and local synchrony even on an ordered network. Moreover, as the network departs from an ordered organization to a small-world architecture, correlation patterns, and synchronization dynamics change. Interestingly, at a certain range of the synaptic strength, by fixing the structural conditions, different organized patterns are seen at the different input signals. This variety switches to a bifurcation region by increasing the synaptic strength. We show that topological variations is a major factor of synchronization behavior and lead to alterations in correlated local clusters. We found the coupling strength (between cortical areas) to be especially important at conversions of correlation and synchronization states. Since correlation patterns generate functional connections and transitions of functional connectivity have been related to cognitive operations, these diverse correlation patterns may be considered as different dynamical states corresponding to various cognitive tasks.
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