Project Summary Mindfulness has been defined as accepting, open-minded attention to the present moment. Mindfulness has been conceptualized as a trait (e.g., mindfulness disposition in everyday life) and a state (e.g., mindfulness in the moment). Greater trait mindfulness, as measured by self-report questionnaires like the mindful attention awareness scale (MAAS) and the five-factor mindfulness questionnaire (FFMQ), is associated with improved well-being and decreased psychopathology. The underlying brain bases, however, of trait mindfulness or the state of mindfulness are as yet poorly understood. A promising research approach is to correlate mindfulness trait measures with task-independent brain functions as measured by resting-state functional magnetic resonance imaging (fMRI) and to use cutting-edge fMRI methods to identify state mindfulness. In our first aim, we aim to identify the most reliable brain network connections that predict trait mindfulness, using three resting state datasets totaling more than 350 adults. We use connectome predictive modelling, a data-driven machine learning approach that identifies whole-brain features that correlate with the behavior of interest. In our second aim, we propose to examine state mindfulness independently in a sample of 100 high-rumination adolescents. We will look at fluctuations in brain networks over time using dynamic functional connectivity methods. We will ask whether changes in behavior on a mindfulness-related task, as well as responses to self-report experience prompts over three days, correlate with these dynamic functional connectivity measures. Our hypothesis is that a state of attentional network anticorrelations (e.g. between the default mode network and frontoparietal network) is associated with mindful attention. Lastly, we will assess the relationship of the dynamic brain changes to rumination and other clinical symptoms in the adolescents. This will shed light on brain-mechanistic links between mindfulness and decreased psychopathology. These conceptually related but independent aims may contribute to a shared understanding of mindfulness, i.e., specific brain networks involved in predicting trait mindfulness may also be implicated in state mindfulness. This research promises to deepen our understanding of mindfulness, paving the way for brain- based insights into how it supports well-being.