Evolution of seizure precursors in refactory epilepsy

描述（由申请人提供）：本提案的目标是开发和验证一种预测人类颞叶和颞外癫痫发作的实用方法。我们实验室的最新数据表明，人类部分性癫痫发作与颅内EEG（IEEG）发作前数分钟至数小时的电活动积累有关。这种发作前积聚的三种最有希望的措施是积累能量、亚临床癫痫样爆发（啁啾）和高频癫痫样振荡。除了癫痫发作前增加，这些参数在其他时间的蜡和减弱，表明大脑兴奋性的反复变化反复发生，只有在关键时刻进行癫痫发作。通过在连续、长期、多通道颅内EEG（IEEG）数据中跟踪上述三个指标，我们计划开发一个癫痫发作如何在癫痫网络中产生的实用模型，并将前瞻性地验证该模型识别癫痫发作概率增加时期的能力（我们对“癫痫发作预测”的定义）。我们实验室基于上述定量特征开发的算法目前正在第一代癫痫响应脑刺激设备中运行，该设备正在进行的临床试验中植入约200名患者，结果令人鼓舞。这些装置刺激大脑，以响应上述定量测量在单通道中的积累。这些设备的最佳性能将需要了解这些措施如何在整个癫痫网络中发展和传播，以及这一过程的机制。基于上述发展，本提案的具体目标是：（1）精心收集、标记和存档一个IEEG研究的数字数据库，该数据库来自具有医学耐药性颞叶和颞外癫痫的成人和儿童的代表性人群，（二）研究所有颅内电极接触中上述3个定量指标的发生率和持续时间，并前瞻性确定它们与在连续的、未处理的患者数据集中的电描记癫痫发作;（3）基于这些发现开发癫痫发作产生的实用模型，并前瞻性地验证其预测癫痫发作的能力。实现这些目标将对癫痫发作的潜在机制产生重要的见解，并对改善第一代反应性癫痫器械的性能至关重要。我们的实验室将领导一个成熟的团队合作者在这个项目中的数据收集，处理和解释，在电气工程，神经科学，临床癫痫，神经病理学和统计学的专业知识在宾夕法尼亚大学，马约诊所和格鲁吉亚理工学院。

DESCRIPTION (provided by applicant): The goal of this proposal is to develop and validate a practical method to predict epileptic seizures in human temporal and extratemporal epilepsy. Recent data from our laboratory suggest that human partial seizures are associated with a build-up of electrical activity minutes to hours prior to their onset on intracranial EEG (IEEG). The three most promising measures of this pre-ictal build-up are accumulating energy, subclinical seizure-like bursts (chirps), and high frequency epileptiform oscillations. In addition to increasing before seizures, these parameters wax and wane at other times, suggesting that recurrent changes in brain excitability occur repetitively and only proceed to seizures at critical times. By tracking the above three measures in continuous, long-term, multi-channel intracranial EEG (IEEG) data we plan to develop a practical model of how seizures are generated in the epileptic network and will prospectively validate the model's ability to identify periods of increased probability of seizure onset (our definition of "seizure prediction"). Algorithms developed in our laboratory based upon the quantitative features above are currently operating in first generation responsive brain stimulation devices for epilepsy being implanted in about 200 patients in an ongoing clinical trial, with encouraging results. These devices stimulate the brain in response to build-ups of the above quantitative measures in single channels. Optimal performance of these devices will require understanding how these measures develop and spread in the entire epileptic network, and the mechanisms underlying this process. Motivated by the above developments, the specific aims of this proposal are: (1) To meticulously collect, mark and archive a digital database of IEEG studies from a representative population of adults and children with medically resistant temporal and extra-temporal epilepsy, (2) to study the occurrence and duration of the above 3 quantitative measures in all intracranial electrode contacts and prospectively determine their relationship to electrographic seizure onset in continuous, undipped patient data sets; (3) to develop a practical model of seizure generation based upon these findings and prospectively validate its ability to predict seizures. Accomplishing these aims will yield important insight into the mechanisms underlying seizure generation and will be critical to improving the performance of 1st generation reactive epilepsy devices. Our lab will lead an established team of collaborators in this project for data collection, processing and interpretation, with expertise in electrical engineering, neuroscience, clinical epilepsy, neuropathology and statistics at The University of Pennsylvania, The Mayo Clinic and The Georgia Institute of Technology.