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Tytuł pozycji:

Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification.

Tytuł:
Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification.
Autorzy:
Geana A; Department of Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island. Electronic address: andra_.
Barch DM; Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
Gold JM; Department of Psychiatry, Maryland Psychiatric Research Center, Baltimore, Maryland.
Carter CS; Department of Psychiatry, University of California, Davis, California.
MacDonald AW 3rd; Department of Psychology, University of Minnesota, Minneapolis, Minnesota.
Ragland JD; Department of Psychiatry, University of California, Davis, California.
Silverstein SM; Department of Psychiatry, University of Rochester, Rochester, New York.
Frank MJ; Department of Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island.
Źródło:
Biological psychiatry. Cognitive neuroscience and neuroimaging [Biol Psychiatry Cogn Neurosci Neuroimaging] 2022 Oct; Vol. 7 (10), pp. 1035-1046. Date of Electronic Publication: 2021 Apr 18.
Typ publikacji:
Journal Article; Research Support, N.I.H., Extramural
Język:
English
Imprint Name(s):
Original Publication: New York, NY : Elsevier, Inc., [2016]-
MeSH Terms:
Antipsychotic Agents*/therapeutic use
Psychotic Disorders*/drug therapy
Schizophrenia*/drug therapy
Computer Simulation ; Humans ; Reinforcement, Psychology
References:
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Grant Information:
R01 MH084826 United States MH NIMH NIH HHS; R01 MH084821 United States MH NIMH NIH HHS; R01 MH084861 United States MH NIMH NIH HHS; T32 MH126388 United States MH NIMH NIH HHS; R01 MH084828 United States MH NIMH NIH HHS; R01 MH084840 United States MH NIMH NIH HHS
Contributed Indexing:
Keywords: Classification; Computational psychiatry; Modeling; Reinforcement learning; Schizophrenia
Substance Nomenclature:
0 (Antipsychotic Agents)
Entry Date(s):
Date Created: 20210420 Date Completed: 20221011 Latest Revision: 20240206
Update Code:
20240206
PubMed Central ID:
PMC9272137
DOI:
10.1016/j.bpsc.2021.03.017
PMID:
33878489
Czasopismo naukowe
Background: Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error-driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex.
Methods: We used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72).
Results: Using accuracy, there was a main effect of group (F 3,279  = 7.87, p < .001), such that all patient groups were less accurate than control subjects. Using computationally derived parameters, both medicated and unmediated individuals with schizophrenia, but not patients with bipolar disorder, demonstrated a reduced mixing parameter (F 3,295  = 13.91, p < .001), indicating less dependence on learning explicit value representations as well as greater learning decay between training and test (F 1,289  = 12.81, p < .001). Unmedicated patients with schizophrenia also showed greater decision noise (F 3,295  = 2.67, p = .04).
Conclusions: Both medicated and unmedicated patients showed overreliance on prediction error-driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.
(Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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