Multi-Scale Cortical Dynamics and Seizure Prediction in Human Focal Epilepsy

描述(由申请人提供)： 照顾患有癫痫的退伍军人是退伍军人事务部的优先事项，全国各地成立了几个癫痫英才中心(ECoE)就证明了这一点。癫痫持续但似乎不可预测的复发极大地影响了患有癫痫的退伍军人和平民的自主性和生活质量。不幸的是，尽管进行了最大限度的药物治疗，25%-35%的癫痫患者仍有癫痫发作(Anenerers 2001)。区域手术切除，除了有显著的风险外，只有40-70%的病例是治愈的(Engel等人，2003年)。即使在药物或外科方法奏效的情况下，副作用也可能很严重。替代癫痫预防和警报系统将是非常可取的，以恢复退伍军人和患有顽固性癫痫的平民的生活质量和自主权 癫痫。在过去的30年里，基于颅内脑电(IEEG)记录的癫痫预防和预警系统得到了广泛的研究，但可靠的癫痫发作预测和早期检测仍然难以实现。总体而言，由于难以监测人类单个神经元的整体活动，这一进展受到了阻碍。我们将使用一种新技术，皮质内96微电极阵列(MEA)，以比iEEG记录更高的空间分辨率来检查人类癫痫。我们将记录药物难治性局灶性癫痫患者手术前监测的单个神经元(Single Unitment，SU)、多通道多单位(MU)和高密度场电位(High-Density field Potential，LFP)的活动。目的1将研究癫痫发作如何在多个尺度上开始、扩散和终止，从SU、MU和LFP集合的微生理水平到iEEG信号反映的宏观动力学。我们将检验这一假设，即癫痫发作前的神经元集合动力学逐渐改变，并且这些变化可以在癫痫发作前几分钟被检测到。我们进一步假设，这种转变应该表现在SU、MU和LFP之间的时空相关性的变化上。目的2a将开发和测试一个新的框架，用于基于局灶性癫痫患者的多尺度MEA神经信号进行癫痫发作预测和预警。Aim 2b将把这种新的癫痫发作预测框架与完全基于iEEG的最先进的预测方法进行比较。这种比较将揭示包括MEA神经信号的癫痫发作预测方法与不包括MEA神经信号的方法的优缺点。

DESCRIPTION (provided by applicant): The care of Veterans with epilepsy is a priority for the Department of Veterans Affairs as attested by the creation of several Epilepsy Centers of Excellence (ECoE) across the country. The constant but seemingly unpredictable re-occurrence of seizures dramatically impacts the autonomy and quality of life of Veterans and civilians suffering from epilepsy. Unfortunately, 25-35% of patients with epilepsy continue to have seizures despite maximal medical therapy (Annegers 2001). Regional surgical resection, besides carrying significant risk, is curative in only ~ 40-70% of cases (Engel et al., 2003). Even when pharmacological or surgical methods work, side effects can be severe. Alternative seizure prevention and warning systems would be highly desirable to restore quality of life and autonomy of Veterans and civilians with intractable epilepsy. Seizure prevention and warning systems based on intracranial EEG (iEEG) recordings have been intensively examined in the past 30 years, yet reliable seizure prediction and early detection remain elusive. Overall, progress has been hampered by the difficulty of monitoring the activity of ensembles of single neurons in humans. We will use a novel technology, intracortical 96-microelectrode arrays (MEAs), to examine human epilepsy at a much higher spatial resolution than in iEEG recordings. We will record the activity of ensembles of single neurons (single units, SUs), and multichannel multi-unit (MU) and high-density field potentials (LFPs) in patients with pharmacologically intractable focal epilepsy undergoing pre-resection surgery monitoring. AIM 1 will study how seizures start, spread and terminate at multiple scales, from the microphysiological level of ensembles of SUs, MUs and LFPs to the macroscopic dynamics reflected in iEEGs signals. We will test the hypothesis that neuronal ensemble dynamics preceding seizure onset change gradually and that these changes can be detected several minutes before the seizure onset. We further hypothesize that this transition should manifest in changes in spatiotemporal correlations among SUs, MUs and LFPs. AIM 2a will develop and test a new framework for seizure prediction and early warning based on multi-scale MEA neural signals in people with focal epilepsy. AIM 2b will compare this new framework for seizure prediction with state of the art prediction approaches based exclusively on iEEG. This comparison will reveal the advantages and disadvantages of seizure prediction approaches that include MEA neural signals versus approaches that do not.