Mayo Clinic College of Medicine, Mayo Clinic Rochester, MN, USA.
Department of Neurologic Surgery, Mayo Clinic Rochester, MN, USA.
Front Neurosci. 2014 Jun 25;8:169. doi: 10.3389/fnins.2014.00169. eCollection 2014.
Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a "smart" neuroprosthetic system for treatment of neurologic and psychiatric disorders.
目前，优化脑深部刺激（DBS）疗法的策略涉及多次术后就诊。在每次就诊中，都会调整刺激参数，直到达到理想的治疗效果并最小化不良反应。然而，由于疾病进展、宿主环境与电极之间的相互作用以及导联迁移等原因，这些治疗参数的疗效可能会随时间下降。因此，开发能够响应不断变化的神经化学环境的闭环控制系统，针对个体患者定制 DBS 治疗，对于提高 DBS 的治疗效果至关重要。在动物和人类中使用电生理学和影像学技术获得的证据表明，DBS 通过调节神经网络活动起作用。最近，动物研究表明，刺激诱发的神经递质释放变化与正常生理相匹配，与 DBS 的治疗益处相关。因此，要全面了解 DBS 的神经生理学并优化其疗效，可能有必要超越传统的电生理分析，描述治疗和非治疗刺激的神经化学效应。通过结合电化学监测和数学建模技术，我们可以潜在地用帮助达到最佳和稳定的神经化学特征的确定性方法来替代临床编程中使用的反复试验过程。在本文中，我们总结了控制神经调节疗法的电生理和电化学处理的当前理解。此外，我们描述了一个原理验证闭环控制器，该控制器可根据啮齿动物模型中 DBS 诱发的多巴胺变化来调整刺激参数。本文所描述的工作代表了朝着实现用于治疗神经和精神疾病的“智能”神经假体系统迈出的最初步骤。
