PROJECT SUMMARY Disrupted attention is among the earliest-emerging, lifelong features of autism spectrum disorder (ASD) and research suggests a critical point in the neonatal and early infant period at which disrupted attention may be detectable. The timing of this disruption aligns with key maturational shifts in neurodevelopment, but the neurobiological mechanisms associated with disrupted attention in ASD remain elusive. One neurobiological system that regulates attention from early infancy is the autonomic nervous system (ANS). Broad ANS dysfunction is observed in older children already diagnosed with ASD, but whether and when ANS dysfunction contributes to attention abnormalities in ASD remains unknown. The overall goal of this study is to examine how atypical autonomic regulation of attention may be associated with the emergence of ASD symptoms. A key marker of autonomic regulation of attention is heart defined attention. Accordingly, maturation of heart defined attention in the early infant period and the developmental consequences therein for the emergence of interactive behaviors and ASD symptoms will be examined. A critical innovation of this study is leveraging a three-group design in which infants who experience elevated ASD symptoms (infant siblings of children with ASD; ASIBs) will be compared to typically developing (TD) and preterm (PT) control groups. Infants born preterm experience broad ANS dysfunction from birth and comparing ASIBs to PTs will help delineate how attention-specific ANS dysfunction predicts the emergence of ASD-specific symptoms. Aim 1 will compare the emergence of autonomic regulation of attention during a very critical period of neurodevelopment (1-3 months) across ASIBs, PT infants, and TD infants. Aim 2 will quantify the association between heart defined attention and interactive behavior on a moment-to-moment basis at 6, 9, and 12 months, and compare across the three groups. Aim 3 will utilize machine learning techniques to predict ASD symptoms at age 3 years from early autonomic and attentional features (from Aims 1-2). Determining the specificity of autonomic regulation of attention as a key, early emerging feature associated with ASD, has significant translational potential for a cost-effective biomarker. This work will inform developmental models of ASD wherein disrupted autonomic regulation of attention has proximal effects on real-world interactions that may interfere with learning and have cascading effects on long-term outcomes.