Paving the way for precision treatment of psychiatric symptoms with functional connectivity neurofeedback

Abstract Introduction Major depressive disorder (MDD) remains challenging to treat, with many patients failing to respond adequately to existing therapies. Patients with MDD have heterogeneous subsets of symptoms with differing underlying neural aberrations. Treatment response may improve if treatments become more individualised. We recently showed preliminary evidence that normalisation of a neural network and a corresponding reduction in related symptoms can be achieved using a Brain Machine Interface (BMI) called real-time fMRI functional connectivity neurofeedback (FCNef). However, the robustness of this effect, and the best FCNef parameters for optimising therapeutic outcomes remained unknown. Methods We ran additional participants, with a final dataset of N = 68, in our FCNef protocol. Functional connectivity between the dorsolateral prefrontal cortex (DLPFC) and posterior cingulate cortex/precuneus (PCC) was targeted with the goal of reducing brooding rumination symptoms. Core FCNef parameters (experimental schedule and reward) were manipulated between participants. Results We replicated findings that normalisation of DLPFC-PCC connectivity with FCNef correlates significantly with reductions in brooding rumination, but not with changes in anxiety, which is associated with different neural circuits. The difference between these correlations was significant, highlighting the precision of this effect. Finally, we found that successful DLPFC-PCC normalisation and corresponding changes in brooding rumination depended on specific FCNef parameters. The most effective protocol involved consecutive training days with greater external reward. Conclusions These results highlight the potential of FCNef for precision medicine in psychiatry and underscore the importance of optimising parameters to enhance feasibility of BMI-based clinical interventions.