Research and New Therapies, Epilepsy Foundation of America, Landover, MD 20784.
Department of Neurology, New York University, New York, NY 10003.
eNeuro. 2017 Dec 27;4(6). doi: 10.1523/ENEURO.0349-17.2017. eCollection 2017 Nov-Dec.
The Epilepsy Innovation Institute (Ei) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms.
癫痫创新研究所(Ei)是癫痫基金会的一个新研究项目,旨在成为癫痫的创新孵化器。Ei 的研究领域是根据社区调查选定的,这些调查询问受癫痫影响的人希望研究人员关注哪些方面。在 2016 年的调查中,无论发作频率或严重程度如何,不可预测性都被选为首要问题。针对这一需求,Ei 发起了“My Seizure Gauge 挑战赛”,最终目标是创建一个个性化的癫痫预警系统设备。在向前推进之前,Ei 召集了来自受癫痫影响的人和临床医生、设备开发商和数据科学家、基础科学研究人员和监管机构的多元化利益相关者,就癫痫预测的科学现状进行评估。从讨论中可以清楚地看出,我们正处于一个令人兴奋的十字路口。随着生物工程的进步,我们可以利用数字标记、可穿戴设备和生物传感器作为癫痫预测算法的参数。还有一千多人被植入了可移动的颅内 EEG 记录设备。将外周测量与大脑状态配对可以识别新的关系和见解。另一个关键组成部分是关系的异质性,这表明在不同组别中汇总发现的效果不佳,并且需要在更长的时间范围内进行数据收集,以便对潜在的癫痫预测算法进行个体化处理。