Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.闭环神经调控（closed-loop neuromodulation）领域的进展，亟需能够应对两大核心挑战的分析与建模方法：一是神经元对刺激的复杂响应特性，二是神经记录中存在的强内源噪声与测量噪声。本文针对晚期帕金森病（Parkinson’s disease）与治疗难治性强迫症（obsessive-compulsive disorder）的抗噪声闭环丘脑底核深部脑刺激（subthalamic nucleus deep brain stimulation）系统，详细阐述其算法层面的设计细节，确保刺激效率、选择性与计算速度均达到优异水平。首先，本文提出一种源自动力学系统理论（dynamical systems theory）的高效方法，可可靠评估β频段与高频丘脑底核神经元活动间的显著非线性耦合，并将其作为反馈控制的生物标志物（biomarker）。此外，本文提出一种基于模型的策略，可用于识别实现神经元活动去同步化控制且能耗最低的最优刺激参数。该策略融合了随机建模（stochastic modeling）与基于二次建模（quadratic modeling）的神经动力学无导数优化（derivative-free optimization）方法。基于数值模拟（numerical simulations）的实验结果表明，相较于术后确定的刺激参数设置，本文提出的建模方法可在计算成本相对较低的前提下，识别出效率与选择性均显著更高的刺激参数配置，展现出良好的应用潜力。本研究的数据验证了这一假说：基于模型的控制策略是临床应用场景下新型刺激方案设计的关键要素。