Author Summary Neurofeedback using real-time fMRI (rt-fMRI NF) is a potential neurorehabilitation tool for several neurodevelopmental, psychiatric and neurological disorders. However, not all subjects are capable to learn how to self-regulate their brain, that is, to be successful in rt-fMRI NF. Our goal, in this study, is to find connectivity metrics that predict this success in a working memory paradigm targeting dorsolateral prefrontal cortex (DLPFC), so that we can avoid futile implementation of the technique in the future by previously selecting responders. We found that success is probably dependent on the dynamic switching between DMN, salience network, and central executive network, influenced by both functional and structural connectivity. Abstract Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n -back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.