Using Feedback Control to Suppress Seizure Genesis in Epilepsy

使用反馈控制抑制癫痫发作

基本信息

批准号：
9920327
负责人：
Sridevi V. Sarma
金额：
$ 0.83万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2021-03-31
项目状态：
已结题

项目摘要

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.

项目摘要癫痫病影响了全球约7000万人。大约30％的癫痫患者是耐药性并且必须考虑侵入性替代方法，例如改良手术和电刺激疗法。外科候选人必须在雄辩的大脑结构以外的区域中具有良好的重点。虽然手术可以极大地改善患者的生活，这是不可逆转的，结果是高度可变的（30-70％的成功（费率）。另一方面，电气模拟是可逆的，并且具有巨大的潜力。慢性开环刺激显示出一定的效率，但不能解释动态的大脑活动和连续的改变患者状态，使其次优和粗糙。为了最大化热效应，新方法必须开发以特定于患者的方式对模拟参数进行精细动态调整。闭环疗法提供了一种有吸引力的选择，可以将干预措施限制在限制治疗的时代患者需要。已经努力使用不同的协议制定“闭环”模拟策略，但没有提供一个高效和可靠的解决方案。实际上，所有提议的闭环策略都是 “响应式开关”，并且没有产生可靠的结果来转化为诊所。这些策略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。相比之下，我们将实施真正的闭环控制癫痫发作完全使用随着脑电图测量而改变的自适应刺激模式 - 避免癫痫发作和癫痫发作。为了满足这一目标，我们计划使用体内实验数据来开发创新的数学模型这是癫痫发作期间基本神经动力学的特征刺激导致癫痫发作的神经活性。基于此模型，我们将设计和实施反馈控制器实时监视神经活动以防止癫痫发作在网络中发展。在尤其是，控制器将窃取临时刺激模式，以最少破坏塞氏前活动能源消耗。为了实现我们的目标，我们组建了一个具有专业知识的高度跨学科团队在系统识别，控制和实验性神经生理学中。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Publisher Correction: Neural fragility as an EEG marker of the seizure onset zone.

出版商更正：神经脆弱性作为癫痫发作区的脑电图标记。

DOI：
10.1038/s41593-022-01047-z
发表时间：
2022
期刊：
Nature neuroscience
影响因子：
25
作者：
Li,Adam;Huynh,Chester;Fitzgerald,Zachary;Cajigas,Iahn;Brusko,Damian;Jagid,Jonathan;Claudio,AngelO;Kanner,AndresM;Hopp,Jennifer;Chen,Stephanie;Haagensen,Jennifer;Johnson,Emily;Anderson,William;Crone,Nathan;Inati,Sara;Zaghlou
通讯作者：
Zaghlou

Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy.

超宽带神经活动预示着癫痫大鼠模型的癫痫发作。