Brain-behavior vulnerability to sleep loss in children: a dimensional study of attention and impulsivity

项目摘要 随着开学时间的延长，儿童和青少年越来越多地经历睡眠不足或睡眠不及时的情况。 与睡眠调节的成熟变化相冲突。然而，我们对这两个问题缺乏深入的了解 这种睡眠缺失的大脑行为后果以及区分哪些儿童可能是 这些国家越来越脆弱，对其影响的抵抗力相对较强。这一点对于 注意力和冲动的大脑行为机制：两个认知领域 受睡眠不足的影响，对学业成功至关重要，也是神经发育的核心方面 注意力缺陷/多动障碍(ADHD)等障碍。解决这些问题的尝试是 受制于有限的机械论研究，过度依赖成人数据，以及使用神经典型样本 只有这样。因此，这个R01项目通过受试者在家中限制睡眠的实验来解决这些挑战 结合功能磁共振成像、神经认知测试、脑电和昼夜生理学的方法对此进行研究 很重要的问题。我们的目标是(在五年内)招募150名10-13岁的不同注意力不集中的儿童 和冲动。我们会将睡眠优化基线与5个晚上的睡眠限制进行比较。每个孩子都是 以几个注意力和冲动指标为特征，为每个领域汇编基线风险分数。 静息的神经唤醒(清醒的额叶脑电角)和昼夜节律(褪黑激素的起始相位角) 将与睡眠相关的生理学作为改变儿童对睡眠不足的恢复力的候选机制。重复 使用基于任务和休息的范例的功能磁共振成像，具有复杂的建模 行为的可变性是睡眠不足对注意力和冲动的影响的特征。一种高性能电池 索引与学业成绩相关的神经认知障碍。最后，将机器学习应用于 在我们的样本中，确定检查基线风险因素和失眠脆弱性的不同表型。在……里面 总而言之，这个项目将推动NICHD和NIMH理解复杂行为机制的目标； 以下是儿童失眠的后果和调节因素。

Project Summary Children and adolescents increasingly experience insufficient or poorly timed sleep as early school start times collide with maturational changes in sleep regulation. However, we lack a deep understanding of both the brain-behavior consequences of this sleep loss and the factors that distinguish which children may be increasingly vulnerable and which are comparatively resilient to its effects. This is particularly salient for the brain-behavior mechanisms underlying attention and impulsivity: two cognitive domains which are acutely impacted by sleep loss, are critical for academic success, and are core aspects of neurodevelopmental disorders such as attention-deficit/hyperactivity-disorder (ADHD). Attempts to address these issues are hindered by limited mechanistic studies, overreliance on data from adults, and the use of neurotypical samples only. Thus, this R01 project tackles these challenges with a within-subject at-home sleep restriction experiment with combined methods of fMRI, neurocognitive testing, EEG, and circadian physiology to bear on this important problem. We aim to enroll (over five years) 150, 10-13-year-old children across levels of inattention and impulsivity. We will compare a sleep optimized baseline to 5 nights of sleep restriction. Each child is characterized on several attention and impulsivity metrics to compile baseline risk scores for each domain. Resting neural arousal (waking frontal EEG theta power) and circadian timing (melatonin onset phase angle) index sleep-relevant physiology as candidate mechanisms altering a child’s resilience to sleep loss. Repeated functional magnetic resonance imaging using task-based and resting paradigms, with complex modeling of behavior variability characterizes sleep loss’s impact on attention and impulsivity. A performance battery indexes neurocognitive impairments relevant for academic achievement. Finally, machine learning is used to determine examine distinct phenotypes of baseline risk factors and sleep-loss vulnerability in our sample. In sum, this project will advance both NICHD and NIMH’s goal to understand mechanisms of complex behavior; here, consequences and mediators of sleep loss for children.