Using Feedback Control to Suppress Seizure Genesis in Epilepsy

项目总结 全世界约有7000万人患有癫痫。约30%的癫痫患者具有抗药性 而且必须考虑侵入性的替代方案，如切除手术和电刺激疗法。外科手术 候选人必须在雄辩的大脑结构之外的某个区域有一个很好的定位重点。虽然外科手术 可以极大地改善患者的生活，它是不可逆转的，结果非常不稳定(30%-70%的成功率 差饷)。另一方面，电刺激是可逆的，具有巨大的潜力。慢性开环 刺激已显示出一定的疗效，但不能说明大脑活动的动态化和持续性 改变病人的状态，使其处于次优和粗糙状态。为了使治疗效果最大化，新的方法 必须开发用于以特定于患者的方式对刺激参数进行精细动态调整。闭环系统 治疗提供了一种有吸引力的选择，通过将治疗的提供限制在以下时间段来最大限度地减少干预 这位病人需要帮助。 已经努力开发使用不同方案的“闭环式”刺激策略，但还没有 提供高效可靠的解决方案。所有提出和研究的闭环策略实际上都是 “反应开关”，还没有产生可靠的结果，转化为临床。这些策略要等到 检测到癫痫发作(通过检测算法)，然后用固定模式刺激以抑制癫痫发作。 相反，我们将实施真正的闭环控制，不断引导神经网络远离 癫痫的发生完全使用随着脑电测量而改变的适应性刺激模式-避免 癫痫检测和癫痫发作。 为了达到这一目标，我们计划利用活体实验数据来开发一个创新的数学模型 这是癫痫发生过程中的基本神经动力学特征，以及不同电刺激的影响 刺激神经活动导致癫痫的发生。在此模型的基础上，我们将设计并实现一个 反馈控制器，实时监控神经活动，防止癫痫在网络中演变。在……里面 具体地说，控制器将引导刺激的时间模式，以最小限度地中断癫痫发作前的活动 能源消耗。为了实现我们的目标，我们组建了一支拥有专业知识的高度跨学科的团队 在系统识别、控制和实验神经生理学方面。

PROJECT SUMMARY Epilepsy affects approximately 70 million people worldwide. About 30% of epilepsy patients are drug resistant and must consider invasive alternatives such as resective surgery, and electrical stimulation therapy. Surgical candidates must have a well-localized focus in an area outside of eloquent brain structures. Although surgery can dramatically improve the lives of patients, it is irreversible and outcomes are highly variable (30-70% success rates). Electrical stimulation, on the other hand, is reversible and has great potential. Chronic open-loop stimulation has shown some efficacy, but does not account for dynamic brain activity and the continuously changing state of the patient, making it suboptimal and crude. To maximize therapeutic effects, new methods must be developed for fine dynamic tuning of stimulation parameters in a patient-specific manner. Closed-loop therapy provides an attractive option that minimizes intervention by limiting the delivery of therapy to times when the patient is in need. Efforts have been made to develop “closed-loop” stimulation strategies using different protocols, yet none provide a highly effective and reliable solution. All closed-loop strategies proposed and studied are actually “responsive switches” and haven’t produced reliable results that translate to the clinic. These strategies wait until a seizure is detected (via a detection algorithm) and then stimulate with a fixed pattern to suppress the seizure. In contrast, we will implement real closed-loop control that continuously steers the neural network away from seizure genesis entirely using adaptive stimulation patterns that change with EEG measurements - avoiding seizure detection and seizures altogether. To meet this objective, we plan to use in vivo experimental data to develop an innovative mathematical model that characterizes fundamental neural dynamics during seizure genesis, and the effects of different electrical stimuli on neural activity leading to seizure genesis. Based on this model, we will then design and implement a feedback controller that monitors neural activity in real-time to prevent seizures from evolving in the network. In particular, the controller will steer temporal patterns of stimulation to disrupt pre-seizure activity with minimal energy consumption. To accomplish our goals, we have assembled a highly interdisciplinary team with expertise in system identification, control, and experimental neurophysiology.