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

Despite the prevalence of Major depressive disorder (MDD), a large proportion of patients do not respond well to its existing treatments. Patients with MDD have heterogeneous transdiagnostic subsets of symptoms with differing underlying neural aberrations. Therefore, better treatment response might be achieved using more customizable treatments. Showing promise for this, brain-machine interfaces (BMIs) can be used to directly target patient-specific underlying neural aberrations. As a major step in this direction, here we reproduce and extend, with a larger sample, our previous findings that a BMI technique called Functional Connectivity Neurofeedback (FCNef) can normalize neural aberrations related to specific MDD symptoms. For the first time, we show that normalization of the target neural activity (here, connectivity between the dorsolateral prefrontal cortex and the precuneus) corresponds meaningfully more to reductions in corresponding than non-corresponding symptoms (here, significantly more to reductions in rumination than anxiety symptoms). Furthermore, we showed for the first time that this depended on the specific parameters that FCNef was run with. Specifically, normalization of the targeted neural activity and a corresponding reduction in related symptoms was greater with more external reward and with consecutive (compared to non-consecutive) days of training, but did not differ depending on whether participants were given shorter or longer time-windows to manipulate their neural activity. Overall, these findings demonstrate the promise of FCNef for precision medicine and highlight the importance of BMI parameter testing for enhancing the feasibility of actual clinical trials. Hereby, we inch closer to a future where signals from our own brains are used to guide our own individual medical interventions.