Synchronization Analysis And Effective Connectivity Estimation Of Neuronal Population

A large number of neurons interact with others through synaptic connections,which contributes to forming complex structural network of brain.Through interactions and integrations of neuronal activities of distinct neural units,brain functional networks are reconstructed according to different tasks and activities.Synchronization is related to information integration and conduction in neuronal networks.Effective connectivity characterizes directional causal influences of one neuronal unit on another.Estimating effective connectivity is significantly important to the reconstruction of functional brain network and is helpful to reveal mechanisms of information transmission.Therefore,by using Granger causality method,the modulation of synchronization on effective connectivity estimation is studied in acupuncture experiments and modeled neuronal networks.According to the experimental data recorded from manual acupuncture(MA)with different types and frequencies taken at Zusanli points of experimental rats,synchronization and effective connectivity are investigated among sorted neuronal clusters in the activated spinal dorsal root ganglion.The results show that,MA with different manipulations induces different levels of synchronization among three neurons.Synchronization between neurons is enhanced when subjected to acupuncture with a proper frequency.Effective inter-connective states depend on synchronization dynamics,the level of synchronization is relevance to the direction and intensity of effective connectivity.MA with different frequencies and types give rise to distinct effective connectivity networks.Neuronal network motifs with different structural connections constituted by Hodgkin-Huxley model are constructed to further investigate the relationship between synchronization and effective connectivity estimation.The numerical results show that,physically feedback connection fosters synchronization in such motif.Synchronization contributes to form reciprocal effective connections between neurons.Dynamical effective connectivity may not necessarily match direct synaptic links,but rather rest on the synchronization relationship.Networks with different structural topologies(but similar synchrony dynamics)give rise to equivalent functional topologies.The impact of network parameters on effective connectivity estimation is investigated in the motif with feedback connections.Delay-induced transition among in-phase synchrony,antiphase synchrony and out of synchrony corresponds to dynamical transformation of significant effective interplay.In-phase synchronization facilitates the existence of reciprocal effective connections.With the increase of synaptic conductance magnitude,effective connection undergoes the transition to be statistically significant at a critical value.The strength of effective interaction increases with the coupling strength.Multiple functional topologies stem out of a network with a given structural topology(supporting different synchronization dynamics).The obtained results demonstrate the key role of synchronization in estimating effective connectivity,which provides a theoretical basis for the reconstruction of brain functional network.