From Feasibility to Utility: A Meta-Analysis of Amygdala-Neurofeedback

Amygdala dysregulation is core to multiple psychiatric disorders. Real-time fMRI enables Amygdala self-modulation through NeuroFeedback (NF). Despite a surge in Amygdala-NF studies, a systematic quantification of self-modulation is lacking. Amygdala-NF dissemination is further restricted by absence of unifying framework dictating design choices and insufficient understanding of neural changes underlying successful self-modulation. The current meta-analysis of Amygdala-NF literature found that real-time feedback facilitates learned self-modulation more than placebo. Intriguingly, while we found that variability in design choices could be explained by the targeted domain, this was rarely highlighted by authors. Lastly, reanalysis of six fMRI data-sets (n=151), revealed that successful Amygdala down-modulation is coupled with deactivation of posterior insula and Default-Mode-Network major nodes, pointing to regulation related processes. While findings point to Amygdala self-modulation as a learned skill that could modify brain functionality, further placebo-controlled trials are necessary to prove clinical efficacy. We further suggest that studies should explicitly target neuro-behavioral domain, design studies accordingly and include 'target engagement' measures. We exemplify this idea through a 'process-based' NF approach for PTSD.