Emma Pendleton Bradley Hospital

Brain-behavior Vulnerability to Sleep Loss in Children: a Dimensional Study of Attention and Impulsivity - 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.