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首页> 美国卫生研究院文献>Frontiers in Neuroscience >Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal
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Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal

机译：基于模型的闭环深脑刺激控制器适应参考信号动态变化的评估

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High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in suppressing the motor symptoms of Parkinson's disease (PD). Current clinically-deployed DBS technology operates in an open-loop fashion, i.e., fixed parameter high-frequency stimulation is delivered continuously, invariant to the needs or status of the patient. This poses two major challenges: (1) depletion of the stimulator battery due to the energy demands of continuous high-frequency stimulation, (2) high-frequency stimulation-induced side-effects. Closed-loop deep brain stimulation (CL DBS) may be effective in suppressing parkinsonian symptoms with stimulation parameters that require less energy and evoke fewer side effects than open loop DBS. However, the design of CL DBS comes with several challenges including the selection of an appropriate biomarker reflecting the symptoms of PD, setting a suitable reference signal, and implementing a controller to adapt to dynamic changes in the reference signal. Dynamic changes in beta oscillatory activity occur during the course of voluntary movement, and thus there may be a performance advantage to tracking such dynamic activity. We addressed these challenges by studying the performance of a closed-loop controller using a biophysically-based network model of the basal ganglia. The model-based evaluation consisted of two parts: (1) we implemented a Proportional-Integral (PI) controller to compute optimal DBS frequencies based on the magnitude of a dynamic reference signal, the oscillatory power in the beta band (13–35 Hz) recorded from model globus pallidus internus (GPi) neurons. (2) We coupled a linear auto-regressive model based mapping function with the Routh-Hurwitz stability analysis method to compute the parameters of the PI controller to track dynamic changes in the reference signal. The simulation results demonstrated successful tracking of both constant and dynamic beta oscillatory activity by the PI controller, and the PI controller followed dynamic changes in the reference signal, something that cannot be accomplished by constant open-loop DBS.
机译：丘脑底核（STN）的高频深部脑刺激（DBS）可有效抑制帕金森氏病（PD）的运动症状。当前临床上已部署的DBS技术以开环方式运行，即固定参数高频刺激连续不断地传递，而不会改变患者的需求或状况。这带来了两个主要挑战：（1）由于连续高频刺激的能量需求导致的刺激器电池耗尽；（2）高频刺激引起的副作用。与开环DBS相比，闭环深部脑刺激（CL DBS）可能具有比开环DBS所需的能量更少，副作用更少的刺激参数，从而可以有效地抑制帕金森氏症。但是，CL DBS的设计面临一些挑战，包括选择反映PD症状的合适生物标志物，设置合适的参考信号以及实现控制器以适应参考信号的动态变化。 β振荡活动的动态变化会在自愿运动过程中发生，因此跟踪此类动态活动可能会带来性能优势。我们通过使用基于神经节的基底神经节网络模型研究闭环控制器的性能来解决这些挑战。基于模型的评估包括两个部分：（1）我们实现了一个比例积分（PI）控制器，根据动态参考信号的幅度（β波段的振荡功率（13–35 Hz），计算最佳的DBS频率。 ）记录自模型苍白球内部（GPi）神经元。 （2）我们将基于线性自回归模型的映射函数与Routh-Hurwitz稳定性分析方法相结合，以计算PI控制器的参数以跟踪参考信号中的动态变化。仿真结果表明，PI控制器成功跟踪了恒定和动态β振荡活动，并且PI控制器跟随了参考信号的动态变化，而恒定开环DBS则无法实现这一点。

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