Research Of Energy Coding And Physiological Mechanism In Brain Hemodynamics

Neural activity alters with the changes in cerebral blood flow(CBF)and blood oxygen saturation.Despite that these changes can be detected with functional magnetic resonance imaging(fMRI),the underlying physiological mechanism remains obscure.Upon activation of the specific brain region,CBF increases substantially,albeit with 5-8 s delay.Neuroscience has no scientific explanation for this experimental discovery yet.Based on the theory of energy coding,this paper attempted to study the intrinsic physiological mechanism of hemodynamics in fMRI from the perspective of energy metabolism by simulating the energy consumption of neural networks and the evolution of glucose supply in blood.Based on the Wang-Zhang model,this paper first constructed a multi-level structure network.Through the computer numerical simulation,the neural activity of the network and the energy supply of glucose in the blood are obtained.The calculation results showed that the time of peak supply of glucose in the blood is delayed about 5.6 seconds compared to the time when the neural activity of the network reaches its peak,which reproduced the hemodynamic phenomenon in fMRI.Then,based on the results of this numerical calculation,this paper proposed a physiological mechanism of hemodynamic phenomenon from the perspective of energy metabolism.Finally,based on the proposed physiological mechanism,this paper selected the visual system as the specific research object,and constructed a large-scale multi-level visual neural network based on the anatomical structure of the visual system.The simulation results showed that negative energy mechanism,which was previously reported by our group using Wang-Zhang neuronal model,played a central role in controlling the hemodynamics of the brain.The research results in this paper provided a new research direction for further exploring the physiological mechanism of hemodynamic phenomena in the future,and gave a new perspective and research method in the modeling and calculation of neural networks.