Characterizing Sleep Signatures and its effects on Cognition in New-Onset Temporal Lobe Epilepsy

项目摘要/摘要 颞叶癫痫(TLE)以神经网络活动紊乱和颞叶癫痫为特征。 在美国，多达300万TLE患者也经历了认知和睡眠问题， 导致儿童学习成绩差，成年后就业不足的风险高，以及 随之而来的是社会经济地位的降低。患有TLE的人经常经历睡眠碎片，这 破坏记忆巩固和持续注意力，这两者在这种疾病中都受到了损害。而这些 合并症可能是反复癫痫发作和药物治疗的长期后果，现在已知它们 通常出现在第一次被确认的癫痫发作之前，并随着时间的推移而恶化，即使癫痫发作成功 治疗。这表明，早期神经网络异常可能是癫痫发生的基础，而 同时损害睡眠和认知发展，甚至在慢性紊乱的额外影响之前。 尽管如此，关于这些睡眠和认知机制的研究一直很有限 TLE中的问题。这代表着一个严重的未得到满足的公共卫生需求，美国国家科学院 医学和NINDS已经将这一显著的差距确定为研究优先事项。我将从以下方面着手解决这一差距 My K23计划通过调查TLE中的异常睡眠结构模式直接导致 使用观察性(目标1)和机械干预(目标2)方法的认知缺陷。在……里面 典型的NREM睡眠，脑电(EEG)慢波振荡是锁相的，并与 睡眠纺锤体振荡(Sw-SSO)，有助于巩固记忆，潜在地改善注意力。 在TLE中，导致发作间期癫痫放电和癫痫发作的网络紊乱也可能有助于 睡眠时的Sw-SSO偶联改变，导致记忆和注意力缺陷。一夜的声学表演 刺激(AS)被证明在增强短波-SSO耦合和改善认知操作方面是有效的 在健康的老年人中使用，但尚未在TLE中进行研究。我的中心假设是无序的网络 新诊断的TLE患者会导致睡眠结构改变，从而扰乱记忆巩固和 注意力能力。我将通过以下几个方面来验证这一假设：(1)使用计算工具描述TLE睡眠结构 脑电-睡眠纺锤体密度、慢波功率、发作间期癫痫样放电和短波-SSO耦合(AIM 1a)，(2)将这些特定的TLE相关睡眠结构模式与认知处理联系起来(目标1b)；(3) 确定AS是否增强患有TLE的年轻人的Sw-SSO偶联(目标2a)和(4)确定是否增强 Sw-SSO耦合改善TLE的记忆力和注意力(目标2b)。这个训练奖将为我提供 有机会将我的研究专长扩展到计算睡眠脑电采集和分析、声学 刺激技术和临床试验设计。我的长期目标是利用睡眠和睡眠之间的联系， 行为和神经网络活动，以开发和实施量身定制的认知和睡眠干预 癫痫患者。

Project Summary/Abstract Temporal Lobe Epilepsy (TLE) is characterized by disordered neural network activity and temporal lobe seizures. As many as 3 million individuals with TLE in the United States also experience cognitive and sleep problems, resulting in poor school performance in childhood, with high risk of underemployment in adulthood, and consequent lower socioeconomic status. Individuals with TLE frequently experience sleep fragmentation, which disrupts memory consolidation and sustained attention, both of which are impaired in this disorder. While these comorbidities can be long-term consequences of repeated seizures and medications, it is now known that they also often present prior to the first recognized seizure and worsen over time even with successful seizure treatment. This suggests that an early neural network abnormality may underlie seizure development while simultaneously impairing sleep and cognitive development, even prior to the added effects of disorder chronicity. In spite of this, there has been limited research addressing mechanisms underlying these sleep and cognitive problems in TLE. This represents a critical unmet public health need and both the National Academy of Medicine and NINDS have identified this notable gap as a research priority. I will begin to address this gap with the my K23 proposal by investigating abnormal sleep architecture patterns in TLE that directly contribute to cognitive deficits using both an observational (Aim 1) and a mechanistic interventional (Aim 2) approach. In typical NREM sleep, electroencephalogram (EEG) slow wave oscillations are phase-locked and coupled with sleep spindle oscillations (SW-SSO), which facilitates memory consolidation and potentially improves attention. In TLE, disordered networks that result in interictal epileptic discharges and seizures may also contribute to altered SW-SSO coupling during sleep, resulting in memory and attention deficits. A single night of acoustic stimulation (AS) has been proven effective in enhancing SW-SSO coupling and improving cognitive performance in healthy older adults but has not been studied in TLE. My central hypothesis is that disordered networks in newly diagnosed TLE patients result in altered sleep architecture, which disrupt memory consolidation and attention capability. I will test this hypothesis by: (1) characterizing TLE sleep architecture using computational EEG – sleep spindle density, slow wave power, interictal epileptiform discharges, and SW-SSO coupling (Aim 1a), (2) linking these specific TLE-related sleep architecture patterns to cognitive processing (Aim 1b); (3) determining if AS enhances SW-SSO coupling in young adults with TLE (Aim 2a) and (4) determining if enhanced SW-SSO coupling improves memory and attention in TLE (Aim 2b). This training award will provide me the opportunity to extend my research expertise into computational sleep EEG acquisition and analysis, acoustic stimulation techniques, and clinical trial design. My long-term goal is to leverage connections between sleep, behavior and neural network activity to develop and implement tailored cognitive and sleep interventions for individuals with epilepsy.