Real-time neurofeedback enables human subjects to learn to regulate their brain activity, effecting behavioral changes and improvements of psychiatric symptomatology. Neurofeedback up-regulation and downregulation have been assumed to share common neural correlates. Neuropsychiatric pathology and aging incur suboptimal functioning of the default mode network. Despite the exponential increase in real-time neuroimaging studies, the effects of aging, pathology and the direction of regulation on neurofeedback performance remain largelyunknown. Using open-access analyses and real-time fMRI data shared through the Rockland Sample Real-Time Neurofeedback project (N=136), we first modeled neurofeedback performance and learning in a group of subjects with psychiatric history (n a =74) and a healthy control group (n b =62). Subsequently, we examined the relationship between up-regulation and down-regulation learning, the relationship between age and neurofeedback performance in each group and differences in neurofeedback performance between the two groups. Results show that in an initial session of default mode network neurofeedback with real-time fMRI, up-regulation and down-regulation learning scores are negatively correlated. Moreover, age correlates negatively with default mode network neurofeedback performance, only in absence of psychiatric history. Finally, adults with psychiatric history outperform healthy controls in default mode network up-regulation. Interestingly, the performance difference is related to no upregulation learning in controls. attention) is unknown. Third, key performance constraints, such as the effect of age, in relation to self-regulation of brain function and pathology, have not been investigated.Here we address these three open issues regarding self-regulation of brain function, using the largest publicly available rt-fMRI NF repository, comprising data from healthy participants and psychiatric patients, during bidirectional self-regulation of the default mode network (DMN). The DMN is a large-scale cerebral network (Raichle et al., 2001) associated with a variety of brain functions, including perception (Kelly et al., 2008), attention (Weissman et al., 2006 and working memory (Mayer et al. 2010), which has become highly relevant for clinical applications (Zhang and Raichle, 2010;Brakowski et al., 2017;Mulders et al., 2015;Hamilton et al., 2015).Similarly to psychiatric pathology, aging also incurs suboptimal functioning of the human brain's DMN (Whitfield-Gabrieli and Ford, 2012;Damoiseaux et al., 2008). Recent studies have demonstrated that DMN activity can be