Studies On The Generation And Regulation Of Beta Oscillations In The Globus Pallidus External Segment Based On Neural Computational Models

Beta oscillations in the brain play an important role in the execution of sensorimotor and cognitive functions,and are closely related to the pathogenesis of Parkinson’s disease(PD).Exploring the mechanisms of beta oscillations not only helps to understand brain functions,but also promotes effective treatments of PD.A large number of animal experiments and clinical studies have emphasized the importance of globus pallidus external segment(GPe)in the generation of beta oscillations,but the specific mechanisms are still unclear.In addition,although the use of neuromodulation to suppress beta oscillations in clinical practice has been suggested to effectively improve the symptoms of PD patients,the selection of modulatory parameters often lacks objective standards and has a large subjective dependence.To address the above problems,we build multiscale neural computational models based on the anatomical structure of GPe to explore the generation mechanisms and modulatory neurodynamic behaviors of beta oscillations in GPe.The main content of this thesis includes the following three parts:(1)At the macro-level,we explore whether and how GPe can generate beta oscillations.We use firing rate model to simulate the firing rate of GPe,and explore the influences of connection strength,transmission delay,and time constant on the activity of GPe under the condition of excluding the interference of external inputs.Model simulations show that transmission delay is the main factor to induce neural oscillations in GPe,and it can lead to beta oscillations under appropriate conditions.However,only changing the connection strength or time constant can not make GPe generate beta oscillations.Note that when the transmission delay is relatively larger than the time constant,GPe is able to generate beta oscillations.Moreover,enhancing the connection strength increases the parameter area of beta oscillations,that is,beta oscillations are more likely to occur.(2)At the micro-level,we explore how chemical and electrical synapses affect the generation of beta oscillations in GPe.According to the macro-research,we build a network model containing 1000 GPe neurons using Izhikevich model.The results show that increasing the value of the transmission delay of chemical synapse or the strength of electrical synapse can cause beta oscillations in GPe.Importantly,the strength of chemical synapse can regulate the generation of beta oscillations.Specifically,the strength of chemical synapse must not be too small,although excessively strong chemical synapse will suppress beta oscillations.(3)We modulate the beta oscillations in open-loop and closed-loop strategies based on the GPe network model.The open-loop strategy adopts the square wave stimulation commonly used in clinical practice,which the parameters include amplitude,frequency,and pulse width.Model simulations show that the square wave stimulation can suppress beta oscillations when its amplitude or pulse width is large enough,and increasing the amplitude or pulse width will reduce each other required to suppress beta oscillations.When the frequency of the square wave is close to the multiples of the intrinsic oscillation frequency of GPe,the resonance phenomenon occurs,thus only high frequency stimulation can completely suppress beta oscillations.The form of closed-loop strategy is feedback stimulation.The stimulation signal is determined by the current state of GPe and its amplitude is adjusted by a scale factor.The results show that when the scale factor is large enough,the beta oscillations can be suppressed and the network synchronization can be effectively weakend.In addition,smaller scale factor is able to suppress beta oscillations when adding delay module to the feedback loop,but large values of delay will increase the synchronization.In this thesis,based on neural computational models,we theoretically demonstrated that GPe itself is able to generate beta oscillations,and analyzed related potential mechanisms.We also systematically explored the parameter ranges of two neuromodulation strategies that effectively suppress beta oscillations.These findings might provide new insights into the research of beta oscillations,as well as provide theoretical guidance for the clinical treatments of PD.