Zhu Yulin, Wang Jiang, Li Huiyan, Liu Chen, Grill Warren M
Department of Biomedical Engineering, Duke University, Durham, NC, United States.
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Front Neurosci. 2021 Sep 16;15:750806. doi: 10.3389/fnins.2021.750806. eCollection 2021.
Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson's disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson's disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson's disease.
临床上用于治疗帕金森病的深部脑刺激（DBS）以固定刺激参数的开环方式运行，这可能导致高能耗和治疗效果欠佳。本论文的目的是通过在计算模型中进行模拟，建立一种闭环控制系统，该系统能够自动调整刺激参数，以使模型神经元恢复正常活动。过度的β波段活动被认为是帕金森病的一个标志，苍白球内侧部（GPi）模型神经元中的β波段活动被用作控制GPi的DBS的反馈信号。传统的比例控制器和比例积分控制器在消除帕金森病状态下β功率目标水平与β功率之间的误差方面效果不佳。为了克服调整控制器参数的困难并在对象发生变化时提高跟踪性能，通过引入径向基函数（RBF）网络来构建对象的逆模型，实施了一种监督控制算法。仿真结果表明，在帕金森病状态发生变化以及β功率目标水平动态变化的情况下，能够成功跟踪目标β功率。我们的计算研究表明，RBF网络驱动的监督控制算法用于实时调节治疗帕金森病的DBS参数具有可行性。
