Advancing understanding of neural representations of threat perception through a novel predictive coding framework

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PROJECT SUMMARY Anxiety disorders affect an estimated 6.7% to 7.3% of people globally each year and incur a large burden on people’s lives. To address this urgent problem, it is necessary to develop a better understanding of the neural bases of subjective experiences in anxiety, including threat perception. Translational neuroscience has focused on animal models of defensive behavior involving a core set of regions. Although these animal models and the human subjects research they inspired have led to advances in treatment of anxiety, the mapping between neural mechanisms and subjective experience remains poorly understood. The set of regions found to support defensive behavior in animal models does not appear to be involved in all instances of fear or anxiety. The current project overcomes this barrier by integrating current models of anxiety with predictive coding models of the mind and brain. Incorporating predictive coding into models of anxiety will offer a better understanding of how neural activity relates to subjective experiences important to anxiety (e.g., threat perception). The project tests two parallel hypotheses about neural representation of threat perception suggested by predictive coding models: that neural representations of threat perception are content-specific (Aim 1) and that neural representations of threat perception depend on expectations (Aim 2). Using a single design, we manipulate content-specificity and expectations to test these two hypotheses in parallel. We use fMRI to measure brain activity and use self-report and peripheral psychophysiology to measure subjective experiences of threat perception. To the extent that participants find stimuli threatening (as indexed by self-report and psychophysiology), we hypothesize that we will observe relatively content-specific neural representations of threat. We also hypothesize that neural representations of threat will differ under conditions of expectation vs. expectancy violation. The effect of expectation may impact content-specific neural activity or activity in a core set of regions. The knowledge gained from the proposed project has the potential improve understanding of the mapping between neural activity and subjective experiences. Relevant for translational neuroscience, a better understanding of the psychological and neurobiological mechanisms of anxiety will be critical to closing the gap between laboratory research and more effective treatments for anxiety. More broadly, predictive coding models are models of basic brain function. Thus, the predictive coding model we propose offers a new theoretical framework that is generalizable to psychiatric illnesses involving disordered threat perception (e.g., schizophrenia) and other affective disorders (e.g., depression, bipolar disorder).