Regulation Of Basal Ganglia Network By Improved Deep Brain Stimulation

The generation of Parkinson’s disease(PD)is mainly related to the degeneration and death of dopaminergic neurons in the substantia nigra,which is mainly located in the basal ganglia(BG).Deep brain stimulation(DBS)alleviates the symptoms of tremor,rigidity,and akinesia of the Parkinson’s disease(PD).Over decades of the clinical experience,subthalamic nucleus(STN),globus pallidus external(GPe)and globus pallidus internal(GPi)have been chosen as the common DBS target sites.There is evidence that chronic high-frequency stimulation may cause long-term tissue damage and other side effects.Therefore,how to design the DBS waveform is still a challenging problem.The Hodgkin Huxley(H-H)model is selected to model the neurons of basal ganglia and its surrounding structures closely related to Parkinson’s disease.Considering the stimulation mode,stimulation parameters,DBS waveform and stimulation time,this paper aims to design an improved DBS scheme and analyze the effect of constructing DBS from the perspective of computational neuroscience.The main contents and conclusions of the paper are as follows:The first chapter introduces the research background and significance,as well as the research status of DBS at home and abroad,and summarizes the research ideas and arrangement of the whole paper.The second chapter introduces the basic knowledge related to the research content.Based on the physiological structure of cortical-basal ganglia-thalamus neural circuit,starting from a single neuron,H-H model is selected to establish the dynamic model of basal ganglia network.In order to verify the therapeutic effect after adding DBS,three measurement indexes are introduced to display intuitively through numerical analysis.In Chapter 3,a form of DBS with delayed rectangular waveform,denoted as pulse-delay-pulse(PDP)type DBS is designed based on a computational model of the basal ganglia-thalamus(BG-TH)network.We mainly investigate the effect of the stimulation frequency on relay reliability of the thalamus neurons,beta oscillation of GPi nucleus and firing rate of the BG network.The results show that the PDP-type DBS at STN-GPe site results in better performance at lower frequencies,while the DBS at GPi-GPe site causes the number of spikes of STN to decline and deviate from the healthy status.Fairly good therapeutic effects can only be achieved by PDP-type DBS at STN-GPi site at higher frequencies.Thus,it is concluded that the application of multiple-site stimulation with PDP-type DBS at STN-GPe is of great significance in treating symptoms.In Chapter 4,the effect of closed-loop DBS on Parkinson’s disease is studied.Low-frequency beta oscillation characteristics is the main manifestation in PD.There is evidence that the closed-loop deep brain stimulation(DBS)paradigms that specifically target these pathological bursts of activity hold promises to help trim,and thus normalize their abnormal behavior in real-time.Here,we developed a classification algorithm that predicts pathological beta bursts based on ongoing changes in GPi power spectrum.By making use of this oscillation characteristic of the beta band,the eigenvectors in two states are obtained and classified.The next step is to classify the data after dimensionality reduction.Linear discriminant analysis(LDA)is used as the classification method.The state of each sample is decoded and its performance is tested by means of 5-fold cross-validation.It is showed that the classification accuracy of the model is 98%,the sensitivity is 99.2%,and the specificity is 96.8%.The prediction-based DBS is selectively tailored to the electrophysiological profile of each patient.The overall stimulation time required is also diminished.It also supports longer battery life.Thus,the application of prediction-based DBS is of great clinical importance in treating symptoms of PD.