Study Of SSVEP Neural Network Mechanism Based On Rats

Steady state visual evoked potential(SSVEP) has a high signal to noise ratio and the stability of the spectrum characteristics and other advantages, so it has been widely applied to many areas, such as brain computer interface(BCI). The existing research reveals that SSVEP involves multiple brain regions, yet the underlying mechanisms are not well understood which hampers the research in the related area. In this study, in order to well investigate the neural mechanism of SSVEP from the network level, we utilized multi-channel intracranial recordings which had high time and high spatial resolution together to record EEG data of the anesthetized rat. The main works of this thesis are as follows:1. In this paper, we explored the relationship between SSVEP amplitude and the network topological properties in anesthetized rat for different stimulation frequencies by using graph theory of brain network. We also examined the synergetic dynamic changes of the SSVEP amplitude and topological properties in each rat, the network properties of the control state, and the individual difference of SSVEP network attributes existing among rats. Our work revealed for the first time that the generation of SSVEP is closely correlated with the network reconfiguration whose nodes are widely distributed in the brain. In the production of SSVEP, the reorganization of the background network plays an important role, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation. We also found that the 8Hz network topology corresponding to a very strong SSVEP response has much more efficient connection, at the same time the long-distance connections between the frontal area and occipital lobe increased significantly. So we speculated that the information interaction between the occipital lobe and frontal area plays a crucial role in SSVEP production.2. We used the double column neural mass model coupled with two single-column model to simulate the frontal and occipital areas for the first time, aiming to study the neural mechanism of SSVEP for different frequencies stimuli. And we used the partial directed coherence(PDC) analysis method to build the directed network based on EEG data to verify the finding in this model based on anaysis. The particle swarm optimization(PSO) was used to estimate the model parameters accounting for the different frequencies stimuli, and those estimated parameters having the minimum error compared with the actual EEG data were then used to delineate the underlying neural mechanism of SSVEP. The conducted study reveals that no obvious linkage difference within the local brain areas is observed, and the feedback linkage strength from frontal area to occipital area is also of no significant difference, while the main difference is observed for the information transmission strength form occipital lobe to frontal area, i.e., the 8Hz stimulus having strong SSVEP response reveals the relatively stronger information transmission from occipital lobe to frontal area compared to other stimuli. The finding reveals that the information transmission strength form occipital lobe to frontal area plays an important role for SSVEP generation which is consistent with the result of the directed network built by PDC analysis method.