This application responds to the NIMH PAR-16-136, ?Using the NIMH Research Domain Criteria (RDoC) Approach to Understand Psychosis.? Psychotic symptoms, such as delusions and hallucinations, are treatment-resistant in many patients and are associated with high levels of distress and impairment. Treatment advances have been slowed by the lack of a model of how these symptoms arise and persist. Adopting the RDoC approach, we suggest that these symptoms may result from abnormalities in the neural and cognitive processes that underlie perception, action, and belief formation. Hierarchical predictive coding represents an explanatory framework that unites function and dysfunction in perception action and belief formation. We perceive, act, and believe based on our prior experiences, and we update those priors in light of new data and the prediction errors they elicit. We suggest that hallucinations and delusions form, and are maintained, via aberrant predictive coding mechanisms that vitiate perception, action and belief. We will test these hypotheses with a suite of predictive coding measures in a large sample, capturing variability in symptom severity and duration. We will use functional magnetic resonance imaging (fMRI) during tasks of perception, action, and belief, electroencephalography to measure mismatch negativity (MMN) to unexpected perceptual stimuli, and magnetic resonance spectroscopy (MRS) to measure glutamate concentrations, which may underlie the perturbed MMN and fMRI signals in people with psychosis. We will bring together behavioral and brain data with formal computational modeling that will allow us to estimate, from each individual subject's data, the strength of their priors and prediction errors across a hierarchy of representational richness from simple stimuli through more complex percepts, action choices, and beliefs. We propose four specific aims: (1) testing whether inappropriately strong top-down perceptual priors cause hallucinations; (2) testing if delusions are caused by aberrant prediction error signaling; (3) examining whether psychotic symptoms result from a failure to attribute outcomes to one's own actions appropriately; (4) and assessing whether glutamate levels are related to predictive coding phenomena assayed in Aims 1-3. In a fifth exploratory aim, we will examine whether predictive coding abnormalities change over course of illness. Our overall goal is to provide a computationally rigorous test of the predictive coding account of delusions and hallucinations. Depending on the outcome, we will either discard the theory, or use it to design and test treatment approaches more tailored to the specific, and this far unmet, needs of individuals with psychosis.

Public Health Relevance

Delusions and hallucinations are a serious public health problem for which better treatments are needed. Advances in our understanding of how the brain forms percepts and beliefs are revolutionizing our approach to these distressing symptoms. The current application proposes to extend that understanding, so that more effective and personalized treatment interventions for these devastating symptoms might be developed.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Morris, Sarah E
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University of Maryland Baltimore
Schools of Medicine
United States
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Powers 3rd, Albert R; Bien, Claire; Corlett, Philip R (2018) Aligning Computational Psychiatry With the Hearing Voices Movement: Hearing Their Voices. JAMA Psychiatry 75:640-641
Sterzer, Philipp; Adams, Rick A; Fletcher, Paul et al. (2018) The Predictive Coding Account of Psychosis. Biol Psychiatry 84:634-643