Neuro-Feedback (NF) is a particular form of bio-feedback, which feeds back brain activity to the individual in real-time, to allow for training of controlled regulation of the brain in order to improve performance. Functional magnetic resonance imaging (fMRI) spatial localization allows for a high-quality real-time targeting of sub-cortical brain regions. Yet, until now, real-time-fMRI-NF treatments were limited to training brain activity localized within a region of interest. Conversely, since most mental functions are associated with functional integration of networks, limiting the treatment to one region may be a serious hurdle for an effective treatment. Thus, broader network perspective features can be of great value. In this study we developed a novel network-based rt-fMRI-NF procedure that obtains feedback derived from networks’ influence features as constructed by a graph theory method entitled the dependency network analysis (DEP NA), thus training the subject to control an explicit brain region’s influence on the network. In a proof-of-concept pilot study conducted on ten healthy subjects we demonstrated the feasibility of such a network based probe to be volitionally up-regulated. We further propose that this approach will ultimately provide a clinical therapeutic tool for an individually-tailored intervention protocol aimed at improving different mental processes and cognitive abilities.