期刊
BIOLOGICAL PSYCHIATRY
卷 88, 期 4, 页码 304-314出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.biopsych.2020.03.012
关键词
Computational psychiatry; Imaging; Prodrome; Psychosis; Risk; Schizophrenia
资金
- Medical Research Council [MC-A656-5QD30, MC_PC_17214, MR/S007806/1]
- Maudsley Charity [666]
- Brain and Behavior Research Foundation
- Wellcome Trust [094849/Z/10/Z]
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley
- Connecticut State Department of Mental Health and Addiction Services
- International Mental Health Research Organization/Janssen Rising Star Translational Research Award
- National Institute of Mental Health [R01MH112887]
- Department of Psychiatry of Yale University School of Medicine
- Angelini
- AstraZeneca
- Autifony
- Biogen
- Bristol Myers Squibb
- Eli Lilly and Company
- Heptares
- Janssen
- Lundbeck
- Leyden Delta
- Otsuka
- Servier
- Sunovion
- Rand
- Recordati
- Roche
- EPSRC [EP/N027280/1] Funding Source: UKRI
- MRC [MR/S007806/1, MC_PC_17214] Funding Source: UKRI
The aberrant salience hypothesis proposes that striatal dopamine dysregulation causes misattribution of salience to irrelevant stimuli leading to psychosis. Recently, new lines of preclinical evidence on information coding by subcortical dopamine coupled with computational models of the brain's ability to predict and make inferences about the world (predictive processing) provide a new perspective on this hypothesis. We review these and summarize the evidence for dopamine dysfunction, reward processing, and salience abnormalities in people at clinical high risk of psychosis (CHR) relative to findings in patients with psychosis. This review identifies consistent evidence for dys-regulated subcortical dopamine function in people at CHR, but also indicates a number of areas where neurobio-logical processes are different in CHR subjects relative to patients with psychosis, particularly in reward processing. We then consider how predictive processing models may explain psychotic symptoms in terms of alterations in prediction error and precision signaling using Bayesian approaches. We also review the potential role of environ-mental risk factors, particularly early adverse life experiences, in influencing the prior expectations that individuals have about their world in terms of computational models of the progression from being at CHR to frank psychosis. We identify a number of key outstanding questions, including the relative roles of prediction error or precision signaling in the development of symptoms and the mechanism underlying dopamine dysfunction. Finally, we discuss how the integration of computational psychiatry with biological investigation may inform the treatment for people at CHR of psychosis.
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