Cloud-Based Functional Magnetic Resonance Imaging Neurofeedback to Reduce the Negative Attentional Bias in Depression: A Proof-of-Concept Study

Abstract

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.

Keywords: Attentional bias; Brain-machine interface; Cloud computing; Cognitive training; Depression; Real-time fMRI.

Figure 1:

Cloud-based closed-loop rt-fMRI attention training technique. (A) Participants perform a go/no-go task on overlaid face/scene stimuli, where they respond based on whether the scene image is indoor or outdoor, and thus have to constantly ignore negative faces. (B) As each new time point is acquired, the data are masked and flattened to a 1D-vector. (C) The data are sent to a cloud server for preprocessing and classification. (D) The result is sent as a text file to the local machine controlling the display. A sigmoidal transfer function converts the relative scene minus face classification evidence difference into opacity proportions, so that the attended category (as measured by the classifier) will become more visually prominent. (E) The opacity value is smoothed and updated for the next time point. As shown, when participants are in a maximally negative state, the negative faces dominate the composite image.

Figure 2:

Analysis and results. (A) Each block of continuous scene minus face classification evidence was converted into discrete attentional states (dashed lines). This resulted in a roughly equal number of observations in each state across participants. (B) Probability of staying in a particular attentional state over time, for Early and Late NF runs. (C) During Early NF, the two groups differed in their probability of staying in the most negative attentional state. This group difference was eliminated by the Late NF runs. (D) Depression severity scores significantly decreased for MDD participants over time. (E) Within the MDD group, this reduction in getting stuck in the most negative attentional state showed a trending positive correlation with the reduction in depression severity. Circles represent individual participants; bars represent group averages. Error bars represent ±1 s.e.m. ** = p < 0.01; * = p < 0.05; + = p < 0.1