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

项目摘要 儿童和青少年越来越多地经历睡眠不足或时间不佳，因为上学时间早 与睡眠调节的成熟变化相冲突。然而，我们对这两个问题都缺乏深刻的认识。 这种睡眠不足的大脑行为后果以及区分哪些儿童可能是 这些国家日益脆弱，对气候变化的影响具有相对的复原力。这一点对于 注意力和冲动性的大脑行为机制：两个急性认知领域 受睡眠不足的影响，对学业成功至关重要，是神经发育的核心方面。 注意力缺陷/多动障碍（ADHD）。解决这些问题的尝试是 受到有限的机制研究、过度依赖成人数据和使用神经典型样本的阻碍 只.因此，这个R 01项目通过受试者内的家庭睡眠限制实验来应对这些挑战 结合功能磁共振成像、神经认知测试、脑电图和昼夜生理学的方法， 重要的问题。我们的目标是招募（超过五年）150名10-13岁的儿童，跨越注意力不集中的水平 和冲动我们将比较睡眠优化基线和5个晚上的睡眠限制。每个孩子都是 其特征在于几个注意力和冲动性指标，以编制每个领域的基线风险评分。 静息神经唤醒（清醒额叶EEG θ功率）和昼夜节律定时（褪黑激素起始相位角） 索引睡眠相关的生理学作为候选机制改变儿童的弹性睡眠损失。重复 功能磁共振成像使用基于任务和休息范例，与复杂的建模， 行为变异性表征了睡眠不足对注意力和冲动的影响。性能电池 将与学业成绩相关的神经认知障碍指数化。最后，机器学习用于 在我们的样本中，确定检查基线风险因素和睡眠不足脆弱性的不同表型。在 总之，这个项目将促进NICHD和NIMH理解复杂行为机制的目标; 在这里，儿童睡眠不足的后果和介质。

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.