用深度学习提升帕金森病脑深部刺激的闭环控制效果
Deep Learning Model Predictive Control for Deep Brain Stimulation in Parkinson's Disease
- 基于输入凸神经网络构建多步预测器，动态建模β波振荡
- 相比现有算法，追踪误差与控制活动降低超20%
- 适用于帕金森病及其他神经调控疾病，具强通用性
我们提出一种非线性数据驱动的模型预测控制（MPC）算法，用于帕金森病（PD）的深部脑刺激（DBS）。尽管传统DBS多为开环模式，闭环DBS（CLDBS）通过特定频段（如13-30 Hz β波）的神经振荡幅度作为反馈信号，可改善疗效、减少副作用并延缓患者对刺激的适应。目前，临床实现的CLDBS仅采用比例、积分或阈值切换等简单算法。本文方法利用输入凸神经网络的差分构建多步预测器，模拟β波未来的演化过程，提升了优化时域内的预测精度，并简化了在线计算。在模拟β波响应模型及帕金森病患者数据上的测试表明，相比现有CLDBS算法，本方法可实现超过20%的追踪误差和控制活动降低。该控制策略为数据驱动的通用范式，可推广至PD及其他需闭环神经调控的疾病，以及多种神经调节技术。
原文摘要 · Abstract (English)
We present a nonlinear data-driven Model Predictive Control (MPC) algorithm for deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD). Although DBS is typically implemented in open-loop, closed-loop DBS (CLDBS) uses the amplitude of neural oscillations in specific frequency bands (e.g. beta 13-30 Hz) as a feedback signal, resulting in improved treatment outcomes with reduced side effects and slower rates of patient habituation to stimulation. To date, CLDBS has only been implemented in vivo with simple algorithms such as proportional, proportional-integral, and thresholded switching control. Our approach employs a multi-step predictor based on differences of input-convex neural networks to model the future evolution of beta oscillations. The use of a multi-step predictor enhances prediction accuracy over the optimization horizon and simplifies online computation. In tests using a simulated model of beta-band activity response and data from PD patients, we achieve reductions of more than 20% in both tracking error and control activity in comparison with existing CLDBS algorithms. The proposed control strategy provides a generalizable data-driven technique that can be applied to the treatment of PD and other diseases targeted by CLDBS, as well as to other neuromodulation techniques.
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