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Remote synchronization and symmetry in interactions for the analysis of functional connectivity of distant cortical regions

Abstract:

Functional MRI (fMRI) of the ongoing brain activity at rest, i.e., without any overt-directed behavior, has revealed patterns of coherent activity, so called resting-state functional networks. Dynamical organization of nodes into these functional networks is closely related to the underlying structural connections. However, functional correlations have also been observed between cortical regions without apparent neural links, and mechanisms generating functional connectivity between distant cortical regions are largely unknown. It has been suggested that indirect connections and collective effects governed by network properties of the cortex play a significant role.

In our presentation, we use a modeling approach in conjunction with experimental data to study dynamical aspects of functional connectivity in the human brain. Based on numerical simulations, we investigate the underlying mechanisms with reference to remote synchronization and network symmetry. At first, we demonstrate how the coupling topology is extracted. Our procedure is based on connectivity maps derived from fMRI and diffusion tensor imaging (DTI) experiments. Thus, it combines both functional and structural connectivity. Consequently, our network model includes the important information whether direct or indirect neural connections exist between functionally associated regions.

Neural activity and inferred hemodynamic response of the network nodes are modeled as sets of self-sustained oscillators, which are embedded in topologies of complex functional brain interactions. In the simulated functional networks, we find that remote synchrony between pairs of nodes arises from symmetry in the interactions, which are quantified by the number of shared neighbors. An increasing size of a joint neighborhood positively correlates with a higher level of synchrony. Therefore, our results indicate that a large overlapping neighborhood in complex networks of brain interactions gives rise to functional similarity between distant cortical regions.

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