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In Biological psychiatry. Cognitive neuroscience and neuroimaging

Individuals with depression 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 functional magnetic resonance imaging data on a cloud server; these attentional states were externalized using a visual stimulus that the participant could learn to control. We trained 15 participants with major depressive disorder and 12 healthy control participants over 3 functional magnetic resonance imaging sessions. Exploratory analysis showed that participants with major depressive disorder were initially more likely than healthy control participants to get stuck in negative attentional states, but this diminished with neurofeedback training relative to controls. Depression severity also decreased from pre- to posttraining. These results demonstrate that our method is sensitive to the negative attentional bias in major depressive disorder and showcase the potential of this novel technique as a treatment that can be evaluated in future clinical trials.

Mennen Anne C, Turk-Browne Nicholas B, Wallace Grant, Seok Darsol, Jaganjac Adna, Stock Janet, deBettencourt Megan T, Cohen Jonathan D, Norman Kenneth A, Sheline Yvette I


Attentional bias, Brain-machine interface, Cloud computing, Cognitive training, Depression, Real-time fMRI