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

开发用于剖析睡眠期间脑电图事件时间特性的工具和标准

• 批准号: RGPIN-2019-05107
• 负责人: Warby, Simon
• 金额: $ 2.77万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687091
• 关键词: Development; tools; standards; dissecting; temporal

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.

睡眠对大脑的功能至关重要。 学习和记忆，认知能力，情绪稳定性，反应时间和判断力都受到睡眠不足和睡眠剥夺的影响。 在睡眠期间，大脑执行独特和重复的活动模式，可以使用脑电图（EEG）进行测量。 许多这些不同的模式，如“睡眠纺锤波”，可以被描述为一个脑电图指纹，是独特的每个人。 近年来，睡眠纺锤波的研究引起了人们极大的兴趣。 它们在学习和记忆中起着重要作用，并因衰老、精神和神经系统疾病而改变。 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。 我们研究计划的目标是开发睡眠纺锤波研究的工具和标准，并向研究社区提供这些开源资源。 在我们之前工作的基础上，我们现在开发了一个众包平台，从人类专家纺锤体评分员那里收集纺锤体的大型数据库。 我们使用这个数据库来开发和测试分析EEG信号和检测睡眠纺锤波的计算机算法。 我们还建议开发新的分析工具，不仅可以测量纺锤体的数量，还可以表征重要的纺锤体特征，例如纺锤体随时间的变化模式，以及纺锤体与EEG指纹中其他波模式的关系。 新的数据表明，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。使用现有的数据集，如蒙特利尔睡眠研究档案（MASS），我们将测试纺锤波特征和时间是否会影响认知表现，学习和记忆。 了解脑电图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特定机制。