Primary Motor Cortex Granger Causality Networks Analysis Between Neurons

In the field of neuroscience, it is becoming increasingly common to record the activity of hundreds of neurons simultaneously by using electrode arrays. The key to understanding how a specific brain functions work based on analyzing the connection between multiple neurons. In this paper, the data is obtained from simultaneously recording spike train data of Primary Motor Cortex, when the monkey was trained to complete three-dimensional reach-to-grasp tasks, and we construct the Granger causality network between neurons through the copula-based Granger causality. The contributions of this dissertation are following:(1) We analyzed Granger causality network between neurons at the different Trial times for the same target task, we find the Granger causality network between neurons is not invariant but changing dynamically. The results support the hypothesis that monkeys may have some differences in processing the same information. Through Granger causality connection network properties between neurons conduct significance test under the different target task, we found that Granger causality connection network topology between neurons has significant differences under different target task. And it indicates that collaborative work mechanism between neurons will change under different target task. And the change of network topology has a certain guiding significance for the identification of the motion task based on the brain computer interface. Statistical analysis revealed that the level and complexity of Granger causality in the motor preparation stage is weaker than that of the motor execution stage, this may be related to the closed-loop motion control based on sensor feedback in the motion execution stage.(2) In order to verify the impact of interference on the Granger causality network, by comparing the additional interference and the interference free Granger causal network, we find that the involvement of information between the interfering neurons will change. And by comparing Granger causality network properties under repeated perturbation and random perturbation, we find that for different types of interference, the monkey will take different motion control strategies, which will lead to changes in the mechanism of neuronal cooperative work. Compared with the random disturbance, the online feedback control, which is more concentrated and efficient for the Grainger causal network of the neuron under the control of the repetitive disturbance. By comparing the repetitive disturbances, early and late neuronal Granger causality network adaptation found in repeated interference prior adaptation, monkeys in order to cope with the disturbance, an increase of the complexity of the task execution, which will make the network structure changes; and in the latter part of the repetitive disturbance, with adaptive learning, monkeys of the repetitive disturbance has been able to accurately predict and neurons Granger causality network topology toward no interference change.