Tian Yupeng, Saradhi Srikar, Bello Edward, Johnson Matthew D, D'Eleuterio Gabriele, Popovic Milos R, Lankarany Milad
Krembil Brain Institute-University Health Network, Toronto, ON, Canada.
Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Front Netw Physiol. 2024 Apr 8;4:1356653. doi: 10.3389/fnetp.2024.1356653. eCollection 2024.
Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.
深部脑刺激（DBS）的闭环控制有利于有效且自动地治疗各种神经疾病，如帕金森病（PD）和特发性震颤（ET）。单纯基于临床观察的手动（开环）DBS编程依赖于神经科医生的专业知识和患者的经验。开环DBS中的持续刺激可能会缩短电池寿命并引起副作用。相反，闭环DBS系统使用反馈生物标志物/信号来跟踪患者症状的恶化（或改善），与开环DBS系统相比具有多个优势。现有的闭环DBS控制系统没有纳入DBS或症状背后的生理机制，例如，DBS如何调节突触可塑性的动力学。在这项工作中，我们提出了一个用于开发基于模型的DBS控制器的计算框架，其中神经模型可以描述DBS与神经活动之间的关系，基于多项式的近似可以估计神经与行为活动之间的关系。在我们的模型中，控制器以准实时方式用于找到能显著减轻症状恶化的DBS模式。通过使用所提出的计算框架，这些DBS模式可以在临床中通过在将DBS施加给患者之前预测其效果来进行测试。我们将此框架应用于仅根据肌电图（EMG）记录来寻找特发性震颤的最佳DBS频率的问题。基于我们最近建立的腹中间核（Vim）的网络模型（震颤的主要手术靶点）对DBS的响应，我们开发了神经模型模拟，其中Vim-DBS背后的生理机制与EMG信号中的症状变化相关联。通过使用比例积分微分（PID）控制器，我们表明闭环系统可以跟踪EMG信号并调整Vim-DBS的刺激频率，以使EMG的功率达到期望的控制目标。我们证明基于模型的DBS频率与临床研究中使用的频率非常吻合。我们基于模型的闭环系统适用于不同的控制目标，并且有可能用于不同的疾病和个性化系统。
