Adaptive Closed-loop Control of Deep Brain Stimulation for Movement Disorders

1134296脑深部电刺激（DBS）为其他耐药的退行性神经系统疾病提供了显着的治疗益处，如帕金森病和原发性震颤，目前还没有治愈方法。DBS使用手术植入的电极向控制运动功能的大脑区域提供高频电刺激。这种刺激阻断了引起疾病症状（如震颤）的异常神经信号，但其潜在机制尚不清楚。当今的DBS系统开环运行，即，医生通过观察患者对刺激的反应来设置DBS参数，并选择最能减轻症状的组合。这种跨学科的研究将电气工程、数学和神经科学原理的前沿整合到模型和方法的开发中，以控制运动的大脑区域对DBS的反应。它提出了一个具体的设计，下一代DBS系统通过自适应和预测的闭环控制在开-关的方式，其中的开和关时间的刺激是确定/适应实时与患者的条件。刺激参数在任何给定时间适应每个患者的状况将：a）减少大脑过度刺激，从而减少对健康神经元的损伤并延迟对DBS的可能不耐受的发展，B）降低功耗，从而延长DBS电池寿命并降低与电池更换手术相关的风险和成本，以及c）减少DBS对其他认知功能的副作用，例如言语，从而除了更好的运动功能控制之外，进一步改善患者的生活质量。这将产生改善和个性化的医疗保健，降低风险和成本。这项研究有三个主要的推力：1）建模的动态区域在大脑中，控制运动，通过使用从病人的大脑测量的信号，以便预测DBS刺激参数的效果; 2）设计一个闭环DBS控制，其中大脑信号与来自病人的信号集成？的震颤影响的肢体，如测量的非侵入性表面肌电图（sEMG），以获得一个更完整的图片的病人？的病理状态。连续监测sEMG信号参数，以预测一旦DBS停止震颤的重新出现，并与神经元活动一起用作控制器的输入; 3）通过实施用于DBS的实时预测闭环控制的低复杂度和节能算法，在软件中原型化第二代DBS系统。虽然这项研究侧重于退行性运动障碍，这些发现对许多神经疾病的治疗具有深远的意义，例如严重抑郁症、癫痫、强迫症和慢性疼痛，这些疾病最近被考虑用于DBS型治疗。基于对大脑活动的实时监测，这项拟议研究的变革性方法使DBS刺激适应那些不存在连续和/或可见症状（如震颤）的疾病;这种适应是目前任何开环技术都不可能实现的。

1134296TuninettiDeep Brain Stimulation (DBS) provides remarkable therapeutic benefits for otherwise drug-resistant degenerative neurological disorders, such as Parkinson's disease and Essential Tremor, for which no cure exists at present. DBS uses surgically implanted electrodes to deliver high frequency electrical stimulation to the area of the brain that controls motor functions. The stimulation blocks the abnormal nerve signals that cause disease symptoms, such as tremor, but its underlying mechanisms are unclear. Today's DBS systems operate open-loop, i.e., the physician sets DBS parameters by looking at the patient's reaction to stimulation and chooses the combination that reduced symptoms the most. Stimulation is provided continuously and its parameters remain constant over time until the next visit to the physician.This interdisciplinary research integrates the forefront of electrical engineering, mathematics, and neuroscience principles into the development of models and methods to the response of the area of the brain that controls movement to DBS. It proposes a concrete design of the next generation of DBS systems via adaptive and predictive closed-loop control in an on-off fashion, where on and off times of stimulation are determined/adapted in real-time with the patient's condition. Adaptation of the stimulation parameters to each patient's condition at any given time will: a) diminish brain over-stimulation, thus reducing the damage to healthy neurons and delaying the development of a possible intolerance to DBS, b) lower power consumption, thus prolonging DBS battery life and reducing the risks and costs related to surgeries for battery replacement, and c) reduce DBS side effects on other cognitive functions, such as speech, thus further improving patients' quality of life besides better motor functions control. This will yield improved and personalized health-care at reduced risks and costs.This research has three main thrusts: 1) Modeling the dynamics of the area in the brain that controls movement by using signals measured from the patient's brain so as to predict the effect of the DBS stimulation parameters; 2) Designing a closed-loop DBS control where brain signals are integrated with signals from the patient?s tremor affected limbs, such as measured by noninvasive Surface ElectroMyoGraphy (sEMG), so as to obtain a more complete picture of the patient?s pathological state. sEMG signal parameters are continuously monitored to predict the re-emergence of the tremor once DBS is stopped and serve as input to the controller, together with the neuronal activity; 3) Prototyping in software the second generation of DBS systems by implementing low-complexity and energy-efficient algorithms for real-time predictive closed-loop control of DBS.Although this research focuses on degenerative movement disorders, the discoveries have far reaching implications on the treatment of a number of neurological conditions, such as severe depression, epilepsy, obsessive compulsive disorder, and chronic pain, which have recently been considered for DBS-type treatments. The transformative approach of this proposed research, based on the real-time monitoring of the brain activity, enables DBS stimuli adaptation for those diseases that do not present continuous and/or visible symptoms such as tremor; such adaptation is impossible with any current open-loop technology.