The developmental period from adolescence to young adulthood is associated with vulnerabilities that undermine survival (e.g., risk-taking behaviors) and increase the risk for the emergence of psychopathology (e.g., substance abuse, mood disorders, and schizophrenia). These vulnerabilities may be specifically linked with striatal and dopamine (DA) function, which support motivational systems and influence behavior. During adolescence, DA metabolism and striatal neurophysiology change significantly, and the striatum takes on greater functional significance in behavior. To date, research on the maturation of striatal motivational systems has been restricted to animal models, post mortem studies, and indirect neuroimaging evidence, limiting our ability to understand the neurobiological mechanisms of striatal development in humans. The parent grant identified increases in striatal function during reward processing in the adolescent period that were associated with indices of sensation seeking, as well as changes in brain networks suggesting a unique specialization in adolescence. We now propose to probe the neurobiological mechanisms underlying striatal changes in adolescence and how these affect brain systems and behavior. We will study 140 12- to 30-year-old healthy subjects in an accelerated longitudinal design using a molecular magnetic resonance (mMR) scanner that provides simultaneous magnetic resonance imaging (MRI) and positron emission tomography (PET) data. PET methods will quantify DA availability and release from young to middle adulthood, whereas complementary MRI measures of striatal neurophysiology will provide indices of reward-related neural activation in the striatum and indirect measures of DA processing via quantification of brain function and tissue iron (Aim 1). The effects of developmental changes in the striatum on brain systems will be characterized by linking striatal neurophysiology measures with functional and structural brain network connectivity (Aim 2). Neurobiological changes will be linked with behavioral measures of motivation, including a computational model of dopaminergic effects on reinforcement learning (Aim 3). This work will inform a model of the neurobiological processes underlying the transition from adolescence to adulthood that can clarify the development of psychopathology and increased risk-taking during this time.