Project Summary/Abstract Mindfulness-based smartphone apps have surged in popularity in recent years. Headspace – among the most popular of these platforms – has over 42 million users. Recent surveys indicate that 11% of U.S. adolescents have used mindfulness apps as a means of coping with anxiety or depressive symptoms, which increase substantially during the adolescent years. A growing body of research implicates rumination as being a transdiagnostic risk factor involved in the development of depression and anxiety in youth. Critically, mindfulness meditation has shown significant promise in targeting rumination, and ultimately improving depressive and anxiety symptoms. Mindfulness apps offer a convenient and cost-effective means for accessing mindfulness training, while being interactive and engaging for youth. Despite their growing popularity among teens, strikingly little research has been conducted on these apps. Two critical questions have yet to be addressed, which are strongly aligned with the NCCIH Strategic Plan: (1) what are the underlying neural and cognitive mechanisms that account for the beneficial effects of these apps and (2) for whom is app-based mindfulness well-suited. To address these gaps, adolescents (ages 13-18) will be randomly assigned to an app-delivered mindfulness course vs. an active control condition and will complete pre- and post-intervention resting state functional magnetic resonance imaging (fMRI) scans to probe static and dynamic functional connectivity within – and between – brain networks strongly implicated in mindfulness training and rumination (i.e., Default Mode Network and Salience Network). In addition, cognitive tasks will be administered at pre- and post-intervention to assess attentional control abilities putatively enhanced by mindfulness training. Finally, mindfulness skills and changes in rumination will be assessed via a smartphone-based ecological momentary assessment (EMA) protocol developed in the PI’s lab. First, we will test whether changes in (1) brain functional connectivity, (2) attentional control and (3) acquisition and use of mindfulness skills mediate between-group (i.e., app vs. control) differences in the reduction of rumination. Second, we will test whether a machine learning model incorporating baseline clinical, demographic, and psychosocial characteristics can be used to identify which adolescents are predicted to benefit from app-based mindfulness training. Recent advances in machine learning allow for the development of algorithms predicting outcome at the individual level, as well as the integration of numerous predictors rather than relying on single variables that may, in isolation, have limited clinically-useful predictive value. Ultimately, such an algorithm may inform individual risk-benefit assessments that could be used to objectively communicate the probability of experiencing positive vs. adverse outcomes to users prior to engaging with a mindfulness app. Collectiv...