【作者】 李秀平；

【导师】 王俊松；

【作者基本信息】 天津医科大学 ， 生物医学工程， 2021， 硕士

【摘要】 研究神经振荡能够有效探索神经电活动规律,并深入了解大脑认知、学习和记忆等功能特性。介观尺度的神经群模型(Neural Mass Models,NMMs)具有典型而丰富的动力学行为,被广泛用于研究神经振荡的调控规律。自反馈是神经回路和神经网络的一种典型连接结构,计算研究发现其对大脑节律具有重要的调控作用。因此,本研究旨在从非线性动力学角度探究自反馈回路对神经群非线性动力学特性以及神经振荡的作用机制,从而深入理解神经振荡特性的调控规律。本文综合应用分岔理论和频谱分析方法,从神经群层面分别阐明Wilson-Cowan模型、自反馈神经群模型以及兴奋性丘脑底核(Subthalamic nucleus,STN)与抑制性外苍白球(Globus pallidus externus,GPe)回路模型中gamma振荡、alpha振荡和异常beta振荡的产生和调控机制。具体研究内容如下:(1)Gamma振荡的自反馈调控机制。研究表明,Wilson-Cowan模型可以模拟与认知功能密切相关的gamma振荡,已有工作主要研究了模型外部输入和耦合连接对gamma振荡的调控。自反馈是Wilson-Cowan模型的重要结构特征,其在Wilson-Cowan模型的gamma振荡产生与调节中的功能角色仍然是未知的。此外,已有的研究主要是通过仿真分析,其具体的动力学调节机制尚不清楚。本文基于分岔理论的研究结果表明,抑制性自反馈不利于gamma振荡的产生,并且随着抑制性自反馈强度的增加,gamma振荡频率升高;兴奋性自反馈促进gamma振荡的产生,并且兴奋性自反馈强度的增加导致gamma振荡频率降低。其次,应用分岔和频谱分析的方法,研究了外部输入和抑制性耦合参数对gamma振荡的调控规律,并通过改变抑制性和兴奋性自反馈强度,探讨了自反馈对部输入和抑制性耦合参数动力学行为的影响。上述结果表明抑制性与兴奋性自反馈在Wilson-Cowan模型gamma振荡产生与振荡频率调节中扮演互补的角色,通过二者的协同调控可以对gamma振荡的产生及其频率进行更加灵活的双向调节。(2)Alpha振荡的自反馈调控机制。在自反馈神经群模型中,研究自反馈回路对神经群alpha振荡的调控规律。对抑制性自反馈和兴奋性自反馈进行余维一分岔,得到了动力学行为区间。通过余维二分岔给出抑制性和兴奋性自反馈分别与外部输入、耦合连接参数、突触时间常数的余维二参数区间。频谱分析揭示了抑制性和兴奋性自反馈调控下alpha振荡的变化规律。此外,研究表明外部输入和耦合参数对神经群alpha振荡的产生和调节也具有重要影响。通过分岔分析和仿真分析发现,外部输入和耦合参数能够使模型产生alpha振荡,并且抑制性和兴奋性自反馈会影响外部输入、耦合参数对模型动力学行为的调控,间接表明了自反馈对神经群模型alpha振荡的调控机制。(3)异常beta振荡的外加反馈调控机制。比例积分(Proportional-Integral,PI)控制是实现帕金森振荡深部脑刺激(Deep Brain Simulation,DBS)闭环调控的有效手段。已有研究较多探索神经元模型和神经网络中的DBS闭环调控,从神经群层面和动力学角度的研究还很欠缺。本文利用STN-GPe神经回路模型模拟异常beta振荡,并在STN回路引入PI控制作为外加反馈回路,定量剖析了PI外加反馈控制回路对抑制帕金森振荡的调控规律。通过分岔分析确定了可以抑制异常beta振荡的PI反馈机制二维参数范围,并分析了STN-GPe神经回路模型参数对上述二维参数范围的影响,仿真分析验证了分岔结果的正确性。本研究结果发现兴奋性与抑制性自反馈对gamma振荡与alpha振荡神经振荡的产生与调控具有重要作用,二者相互协调、互为补充,对神经振荡频率实现双向调节。外加PI反馈控制可以实现对异常beta振荡的调控,从而有效抑制帕金森振荡,为设计帕金森深部脑刺激提供了一种可行的理论参考。本文的研究结论为生理实验研究提供了理论参考。更多还原

【Abstract】 Neural oscillations are useful for estimating neural electrical activities,which are thought to play a crucial role in cognitive and memery competence of the brain.Neural mass models(NMMs)with rich and complex nonlinear dynamics have been widely used to investigate the regulating mechanisms of neural oscillation rhythms.In addition,self-feedback is a typical structural feature of neural circuit and neural network,which has been shown to be important for regulating brain rhythm in several computational studies.This paper aims to understand the regulating mechanism of neural oscillations underlined by self-feedback loop of neural mass models from the viewpoint of nonlinear dynamics.In this paper,by using bifurcation theory and simulation analysis methods,we explore the generation and regulation mechanism of gamma oscillations,alpha oscillations and beta oscillations in the Wilson-Cowan model,neural mass model with self-feedback and STN-GPe neural circuit model at the level of neural mass.This research is organized as follows:(1)The regulation mechanism of gamma oscillation underlined by self-feedback.Studies have shown that Wilson-Cowan model can simulate gamma oscillations closely related to cognitive functions,and existing works mainly focus on the external inputs and coupling parameters.Self-feedback is an important structural feature of the Wilson-Cowan model,but its functional role in the generation and regulation of gamma oscillations is still unknown.In addition,the existing research is mainly through simulation analysis,but the specific dynamic regulation mechanism is still not very clear.The study based on bifurcation theory show that,on one hand,the inhibitory self-feedback is not conducive to the generation of gamma oscillations,and increased inhibitory self-feedback strength facilitates the enhancement of the oscillation frequency.On the other hand,the excitatory self-feedback promotes the generation of gamma oscillations,and increasing excitatory self-feedback strength leads to the decrease of oscillation frequency.In addition,by bifurcation and simulation analysis,it has been found that the external input and coupling parameters could simulate gamma oscillation in Wilson-Cowan model,and the dynamic behavior could be affected with different strength of the inhibitory and excitatory self-feedback,which indirectly revealed the regulation mechanism of the self-feedback on the gamma oscillation in the Wilson-Cowan model.To sum up,inhibitory and excitatory self-feedback play a complementary role in generating and regulating the gamma oscillation in the Wilson-Cowan model,and cooperate to bidirectionally regulate the gamma oscillation frequency in a more flexible manner.(2)The regulation mechanism of alpha oscillation underlined by self-feedback.Next,we study the effect of self-feedback loop on alpha oscillations in the self-feedback neural mass model.The bifurcation parameters regions are determined by codimension one and two analyses with respect to inhibitory and excitatory self-feedback.The frequency distribution diagrams of alpha oscillation are obtained.By bifurcation and simulation analysis,it has been found that the external input and coupling parameters could simulate alpha oscillation in the model,and the dynamic behavior of the external input and coupling parameters could be affected with different strength of the inhibitory and excitatory self-feedback,which indirectly revealed the regulation mechanism of the self-feedback on the alpha oscillation in the neural mass model.(3)The regulation mechanism of abnormal beta oscillation underlined by external feedback loop.Proportion-Integral(PI)control is an effective method for deep brain simulation(DBS)closed-loop control of Parkinson’s oscillation.The existing studies mainly focus on the neuron model and neural networks,but seldom research is from the perspective of nonlinear dynamics and neural mass level.Therefore,by introducing the PI control into the STN circuit as an external feedback loop,we use the STN-GPe neural circuit model to explore the suppressing regulation of generating the abnormal beta oscillation.Besides,the PI parameters regions which can effectively suppress the abnormal beta oscillation were determined by bifurcation analysis.Futhermore,the results suggest that the bifurcation parameters regions are influenced by the other model parameters.Finally,the bifurcation results are verified by the simulation results.To sum up,this study shows that the excitatory and inhibitory self-feedback play an important role in generating and regulating the gamma oscillation and alpha oscillation,and the excitatory and inhibitory self-feedback can cooperate to bidirectionally regulate the neural osciilations.In addition,external PI feedback loop can effectively realize the suppressing regulation of abnormal beta oscillation,which can provide a theoretical reference for deep brain simulation of Parkinson’s oscillation.Finaly,the present study provides a theoretical reference for physiological experimental research.更多还原