Understanding the neural basis of cognitive bias modification as a clinical treatment for depression - Eguchi, A; Walters, D; Peerenboom, N; Dury, H; Fox, E; Stringer, S (American Psychological Asssociation, 2016)
Objective Cognitive Bias Modification (CBM) eliminates cognitive biases towards negative information and is efficacious in reducing depression recurrence, but the mechanisms behind the bias elimination are not fully understood. The present study investigated, through computer simulation of neural network models, the neural dynamics underlying the use of CBM in eliminating the negative biases in the way that depressed patients evaluate facial expressions. Methods We investigated two new CBM methodologies using biologically plausible synaptic learning mechanisms, continuous transformation learning and trace learning, which guide learning by exploiting either the spatial or temporal continuity between visual stimuli presented during training. We first describe simulations with a simplified one-layer neural network, and then we describe simulations in a biologically detailed multi-layer neural network model of the ventral visual pathway. Results After training with either the continuous transformation learning rule or the trace learning rule, the one-layer neural network eliminated biases in interpreting neutral stimuli as sad. The multi-layer neural network trained with realistic face stimuli was also shown to be able to use continuous transformation learning or trace learning in order to reduce biases in the interpretation of neutral stimuli. Conclusions The 19 simulation results suggest two biologically plausible synaptic learning mechanisms, continuous transformation learning and trace learning, that may subserve CBM. The results are highly informative for the development of experimental protocols to produce optimal CBM training methodologies with human participants.