AbstractAnxiety disorders affect millions of people worldwide and impair health, happiness, and productivity on a massive scale. Developmental research points to a connection between early-life behavioral inhibition and the eventual development of these disorders. Our group has previously shown that measures of behavioral inhibition in young rhesus monkeys (Macaca mulatta) predict anxiety-like behavior later in life. In recent years, clinical and basic researchers have implicated the central extended amygdala (EAc)—a neuroanatomical concept that includes the central nucleus of the amygdala (Ce) and the bed nucleus of the stria terminalis (BST)—as a key neural substrate for the expression of anxious and inhibited behavior. An improved understanding of how early-life behavioral inhibition relates to an increased lifetime risk of anxiety disorders—and how this relationship is mediated by alterations in the EAc—could lead to improved treatments and preventive strategies. In this study, we explored the relationships between infant behavioral inhibition and peri-adolescent defensive behavior and brain metabolism in 18 female rhesus monkeys. We coupled a mildly threatening behavioral assay with concurrent multimodal neuroimaging, and related those findings to various measures of infant temperament. To score the behavioral assay, we developed and validated UC-Freeze, a semi-automated machine-learning (ML) tool that uses unsupervised clustering to quantify freezing. Consistent with previous work, we found that heightened Ce metabolism predicted elevated defensive behavior (i.e., more freezing) in the presence of an unfamiliar human intruder. Although we found no link between infant-inhibited temperament and peri-adolescent EAc metabolism or defensive behavior, we did identify infant nervous temperament as a significant predictor of peri-adolescent defensive behavior. Our findings suggest a connection between infant nervous temperament and the eventual development of anxiety and depressive disorders. Moreover, our approach highlights the potential for ML tools to augment existing behavioral neuroscience methods.