Cloud-based fMRI neurofeedback to reduce the negative attentional bias in depression: a proof-of-concept study

Abstract Depressed individuals show an attentional bias toward negatively valenced stimuli and thoughts. In this proof-of-concept study, we present a novel closed-loop neurofeedback procedure intended to remediate this bias. Internal attentional states were detected in real-time by applying machine learning techniques to fMRI data on a cloud server; these attentional states were externalized using a visual stimulus that the participant could learn to control. We trained 15 depressed (MDD) and 12 healthy control (HC) participants over three fMRI sessions. Exploratory analysis showed that MDD participants were initially more likely than HC participants to get “stuck” in negative attentional states, but this diminished with neurofeedback training relative to controls. Depression severity also decreased from pre- to post-training. These results demonstrate that our method is sensitive to the negative attentional bias in MDD and showcase the potential of this novel technique as a treatment that can be evaluated in future clinical trials.