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

运动障碍深部脑刺激的自适应闭环控制

• 批准号: 1134296
• 负责人: Daniela Tuninetti
• 金额: $ 33万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1134296&HistoricalAwards=false
• 关键词: Adaptive; Closed; loop; Control; Deep

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.

1134296Tuninetti 深部脑刺激 (DBS) 为目前尚无治愈方法的帕金森病和特发性震颤等耐药退行性神经系统疾病提供了显着的治疗效果。 DBS 使用通过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。这种刺激会阻断导致震颤等疾病症状的异常神经信号，但其潜在机制尚不清楚。今天的 DBS 系统是开环运行的，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。持续提供刺激，其参数随着时间的推移保持恒定，直到下次就诊。这项跨学科研究将电气工程、数学和神经科学原理的前沿整合到大脑区域反应的模型和方法的开发中控制向 DBS 的移动。它提出了下一代 DBS 系统的具体设计，通过以开关方式进行自适应和预测闭环控制，其中刺激的开关时间根据患者的状况实时确定/调整。在任何给定时间根据每位患者的情况调整刺激参数将：a) 减少大脑过度刺激，从而减少对健康神经元的损害并延缓 DBS 可能不耐受的发展，b) 降低功耗，从而延长 DBS c) 减少 DBS 对其他认知功能（例如言语）的副作用，从而除了更好的运动功能控制之外，进一步提高患者的生活质量。这将以降低风险和成本的方式改善和个性化医疗保健。这项研究有三个主要目标：1）通过使用从患者大脑测量的信号对大脑中控制运动的区域的动力学进行建模，以预测效果DBS 刺激参数； 2) 设计闭环 DBS 控制，将大脑信号与来自患者震颤受影响肢体的信号（例如通过无创表面肌电图 (sEMG) 测量的信号）相结合，从而获得患者病理的更完整图像状态。持续监测 sEMG 信号参数，以预测 DBS 停止后震颤的重新出现，并与神经元活动一起作为控制器的输入； 3) 通过实施低复杂度和高能效的 DBS 实时预测闭环控制算法，对第二代 DBS 系统进行软件原型设计。虽然这项研究的重点是退行性运动障碍，但这些发现对 DBS 具有深远的影响。治疗许多神经系统疾病，如严重抑郁症、癫痫、强迫症和慢性疼痛，最近被考虑用于 DBS 类型的治疗。这项研究的变革性方法基于对大脑活动的实时监测，使 DBS 刺激能够适应那些不呈现连续和/或可见症状（例如震颤）的疾病；对于当前的任何开环技术来说，这种适应都是不可能的。