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Aberrant Salience, Information Processing, and Dopaminergic Signaling in People at Clinical High Risk for Psychosis

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

  1. Medical Research Council [MC-A656-5QD30, MC_PC_17214, MR/S007806/1]
  2. Maudsley Charity [666]
  3. Brain and Behavior Research Foundation
  4. Wellcome Trust [094849/Z/10/Z]
  5. National Institute for Health Research Biomedical Research Centre at South London and Maudsley
  6. Connecticut State Department of Mental Health and Addiction Services
  7. International Mental Health Research Organization/Janssen Rising Star Translational Research Award
  8. National Institute of Mental Health [R01MH112887]
  9. Department of Psychiatry of Yale University School of Medicine
  10. Angelini
  11. AstraZeneca
  12. Autifony
  13. Biogen
  14. Bristol Myers Squibb
  15. Eli Lilly and Company
  16. Heptares
  17. Janssen
  18. Lundbeck
  19. Leyden Delta
  20. Otsuka
  21. Servier
  22. Sunovion
  23. Rand
  24. Recordati
  25. Roche
  26. EPSRC [EP/N027280/1] Funding Source: UKRI
  27. MRC [MR/S007806/1, MC_PC_17214] Funding Source: UKRI

向作者/读者索取更多资源

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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