Development of tools and standards for dissecting the temporal properties of EEG events during sleep

睡眠对大脑的功能是必不可少的。学习和记忆、认知表现、情绪稳定性、反应时间和判断力都会受到睡眠不良和睡眠剥夺的影响。在睡眠期间，大脑执行独特的、重复的活动模式，这可以通过脑电(EEG)来测量。其中许多不同的模式，如“睡眠纺锤波”，可以被描述为每个人独特的脑电指纹。近年来，对睡眠纺锤体的研究引起了极大的兴趣。它们在学习和记忆中扮演着重要的角色，并会因衰老、精神和神经疾病而改变。然而，目前研究睡眠纺锤波的工具的局限性阻碍了这一领域的进展。我们的研究计划的目标是开发研究睡眠纺锤波的工具和标准，并向研究社区提供这些开源资源。在我们以前工作的基础上，我们现在开发了一个众包平台，从人类专家纺锤评分员那里收集一个大型的纺锤数据库。我们使用这个数据库来开发和测试计算机算法，这些算法可以分析EEG信号并检测睡眠纺锤波。我们还建议开发新的分析工具，不仅测量纺锤体的数量，而且表征重要的纺锤体特征，如纺锤体随时间的模式，以及纺锤体与脑电指纹中看到的其他波形模式的关系。新出现的数据表明，纺锤波和慢波之间的关系可能在睡眠期间巩固学习和记忆方面发挥重要作用。利用蒙特利尔睡眠研究档案馆(MASS)等现有的数据集，我们将测试纺锤波的特征和时间是否会影响认知表现、学习和记忆。了解脑电指纹中的这些重要事件，将有助于我们了解神秘的睡眠过程，以及支持大脑功能的具体机制。

Sleep is essential for the function of the brain. Learning and memory, cognitive performance, emotional stability, reaction time and judgement are all compromised by poor sleep and sleep deprivation. During sleep the brain performs unique and repetitive patterns of activity that can be measured using electroencephalography (EEG). Many of these distinct patterns, such as 'sleep-spindles' can be described as an EEG fingerprint that is unique to each individual. The study of sleep spindles has gained tremendous interest in recent years. They play an important role in learning and memory, and are altered by aging, psychiatric and neurological disorders. However, limitations in the current tools to study sleep spindles are inhibiting progress in this field. The goal of our research program is to develop tools and standards for the study of sleep spindles, and to provide these open-source resources to the research community. Expanding on our previous work, we have now developed a crowdsourcing platform to gather a large database of spindles from human expert spindle scorers. We use this database to develop and test computer algorithms that analyze the EEG signal and detect sleep spindles. We also propose to develop new analytic tools that measure not just the number of spindles, but characterize important spindle characteristics such as pattern of spindles over time, and the relationship of spindles to other wave patterns seen in the EEG fingerprint. Emerging data suggests that the relationship between spindles and slow waves may play an important role in the consolidation of learning and memory during sleep. Using existing datasets such as the Montreal Archive of Sleep Studies (MASS), we will test whether spindle characteristics and timing influence cognitive performance, learning and memory. Understanding these important events in the EEG fingerprint will help us understand the mysterious process of sleep, and the specific mechanisms that support the function of the brain.