Using Feedback Control to Suppress Seizure Genesis in Epilepsy

项目摘要 癫痫影响着全世界约7000万人。大约30%的癫痫患者具有耐药性 并且必须考虑侵入性替代方案，例如切除手术和电刺激疗法。手术 候选人必须在雄辩的大脑结构之外的区域有一个很好的局部焦点。虽然手术 可以显著改善患者的生活，它是不可逆的，结果是高度可变的（30 - 70%成功 费率）。另一方面，电刺激是可逆的，具有很大的潜力。慢性开环 刺激显示出一定的功效，但不能解释动态的大脑活动和持续的大脑活动。 改变病人的状态，使其不理想和粗糙。为了使治疗效果最大化， 必须开发用于以患者特定的方式对刺激参数进行精细的动态调整。闭环 治疗提供了一种有吸引力的选择，其通过将治疗的递送限制到 病人需要。 已经做出努力，使用不同的协议开发"闭环"刺激策略，但没有一个 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是 “响应开关”，并没有产生可靠的结果，转化为临床。这些策略等到 （通过检测算法）检测癫痫发作，然后用固定模式刺激以抑制癫痫发作。 相反，我们将实现真实的闭环控制，不断引导神经网络远离 完全使用随EEG测量而变化的自适应刺激模式的癫痫发作发生-避免 癫痫检测和癫痫发作。 为了达到这一目标，我们计划使用体内实验数据来开发一个创新的数学模型 在癫痫发作的发生过程中的基本神经动力学的特点，以及不同的电刺激的影响， 刺激神经活动导致癫痫发作。基于此模型，我们将设计并实现一个 反馈控制器，实时监测神经活动，以防止癫痫发作在网络中发展。在 特别地，控制器将操纵刺激的时间模式，以最小程度地干扰癫痫发作前的活动。 能耗为了实现我们的目标，我们组建了一支高度跨学科的专业团队， 系统识别、控制和实验神经生理学。

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.