Exploring real-time functional magnetic resonance imaging neurofeedback in adolescents with disruptive behavior disorder and callous unemotional traits

Abstract

Introduction: Adolescents with increased callous unemotional traits (CU traits) in the context of disruptive behavior disorder (DBD) show a persistent pattern of antisocial behavior with shallow affect and a lack of empathy or remorse. The amygdala and insula as regions commonly associated with emotion processing, empathy and arousal are implicated in DBD with high CU traits. While behavioral therapies for DBD provide significant but small effects, individualized treatments targeting the implicated brain regions are missing. Methods: In this explorative randomized controlled trial we randomly assigned twenty-seven adolescents with DBD to individualized real-time functional magnetic resonance neurofeedback (rtfMRI-NF) or behavioral treatment as usual (TAU). Visual feedback of either amygdala or insula activity was provided during rtfMRI-NF by gauges and included a simple and concurrent video run plus a transfer run. A linear mixed model (LMM) was applied to determine improvement of self-regulation. Specificity was assessed by correlating individual self-regulation improvement with clinical outcomes. Results: The rtfMRI-NF (n = 11) and TAU (n = 10) completers showed comparable and significant clinical improvement indicating neither superiority nor inferiority of rtfMRI-NF. The exploratory LMM revealed successful learning of self-regulation along the course of training for participants who received feedback from the amygdala. A significant exploratory correlation between individual target region activity in the simple run and clinical improvement was found for one dimension of DBD. Conclusions: This exploratory study demonstrated feasibility and suggests clinical efficacy of individualized rtfMRI-NF comparable to active TAU for adolescents with DBD and increased CU traits. Further studies are needed to confirm efficacy, specificity and to clarify underlying learning mechanisms.