Sleep EEG Spectral Measures of Insomnia

描述（由申请人提供）： 持续性原发性失眠（PPI）是一种普遍存在且经常使人衰弱的睡眠困难形式，给患者和整个社会带来了巨大的经济负担。这种情况的核心是对持久和严重睡眠障碍的痛苦抱怨。尽管有这样的抱怨，研究人员通过使用常规评分的多导睡眠图（PSG -睡眠记录）来记录这些睡眠困难的努力通常产生了临床上不令人印象深刻的发现。这些结果催生了一些理论，这些理论不强调PPI相关睡眠困难的临床意义，并将PPI投诉归因于内源性觉醒或亚综合征情绪障碍等因素。然而，以前的研究和我们自己的试点工作表明，PSG措施，包括睡眠时间，唤醒时间，和睡眠阶段分布来自传统的分类手评分方法可能足以传达所有PPI患者的一部分，只有显着的睡眠困难。对于其余的，这些常规的措施传达很少或没有睡眠障碍，并意味着目前的PPI投诉代表主观失眠或睡眠状态错觉。我们的研究结果表明，这种“责怪病人”的解释忽略了睡眠病理，这是更好地传达睡眠脑电图频谱振幅指数比传统的PSG参数。在这个项目中，我们将测试这些EEG频谱指数作为睡眠障碍的重要神经生理标志物，在所谓的主观PPI患者中。为此，我们将使用一个大型数据集，其中包括超过1100个夜间PSG，伴随的主观睡眠估计（睡眠日志），以及从93名PPI患者和102名正常睡眠者中获得的日间嗜睡/反应时间测试结果。为了实现我们的特定目标，我们首先将重新评分，并从我们数据库中的所有睡眠记录中导出睡眠EEG频谱振幅测量。然后，我们将基于文献的标准应用于常规睡眠测量的值，以区分所谓的主观（即，PSG看似正常的患者）。 随后，我们将进行一些多变量统计测试，以确定睡眠EEG频谱测量是否：（1）将主观PPI患者与客观PPI和正常睡眠者组区分开来;（2）作为主观PPI组的主观（睡眠日志）睡眠估计、日间嗜睡和日间反应时间表现的预测因子，其表现优于传统PSG测量。

DESCRIPTION (provided by applicant): Persistent primary insomnia (PPI) is a prevalent and often debilitating form of sleep difficulty that places significant financial burden on both its sufferers and society at large. Central to this condition are bitter complaints of enduring and significant sleep disturbances. Despite such complaints, researchers' efforts to document these sleep difficulties through the use of conventionally scored polysomnograms (PSG - sleep recordings) have generally yielded findings that are clinically unimpressive. Such results have spawned theories that de-emphasize the clinical significance of PPI-associated sleep difficulties and attribute PPI complaints to such factors as endogenous arousal or subsyndromal mood disturbances. However, previous research and our own pilot work suggest that PSG measures including sleep time, wake time, and sleep stage distribution derived from conventional categorical hand-scoring methods may adequately convey the significant sleep difficulties of only a portion of all PPI sufferers. For the remainder, these conventional measures convey little or no sleep disturbance and imply the presenting PPI complaints represent subjective insomnia or sleep state misperception. Our findings suggest this "blame-the-patient" interpretation overlooks sleep pathology that is better conveyed by sleep EEG spectral amplitude indices than by conventional PSG parameters. In the project proposed, we will test these EEG spectral indices as important neurophysiological markers of sleep disturbance among so-called subjective PPI suffers. To do so we will use a large dataset that includes over 1100 nocturnal PSGs, concomitant subjective sleep estimates (sleep logs), and daytime sleepiness/reaction time test results acquired from 93 PPI sufferers and 102 normal sleepers. To address our Specific Aims, we first will re-score and deriving sleep EEG spectral amplitude measures from all sleep recordings in our database. We then will then apply literature-based criteria to values of conventional sleep measures in order discriminate so-called subjective (i.e., those with seemingly normal PSGs) from objective PPI subtypes. Subsequently we will conduct a number of multivariate statistical tests to determine if sleep EEG spectral measures: (1) discriminate the subjective PPI sufferers from both the objective PPI and normal sleeper groups; (2) perform better than conventional PSG measures as predictors of subjective (sleep log) sleep estimates, daytime sleepiness, and diurnal reaction time performances for the subjective PPI group.