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.
Highlights
Brain, as a combination of neural ensembles, generates oscillatory activities
In-phase synchronization defined as simultaneously firing patterns of neurons, and anti-phase is considered as an increase in the activity of a certain area of the brain, while the activity of others decreases (Li and Zhou, 2011)
When we model a network of neural masses with a coupled oscillatory system, the correlation and synchronization between masses are nearly close concepts
Summary
As a combination of neural ensembles, generates oscillatory activities These oscillations can be recorded simultaneously from the neural masses with electroencephalography (EEG) and magnetoencephalography (MEG). Synchronization has been broadly analyzed at the level of individual neurons (Bazhenov et al, 2008; Bonjean et al, 2011; Feldt Muldoon et al, 2013). This phenomenon is slightly different in mesoscale. In-phase synchronization defined as simultaneously firing patterns of neurons, and anti-phase is considered as an increase in the activity of a certain area of the brain, while the activity of others decreases (Li and Zhou, 2011)
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