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Volatility Estimates Increase Choice Switching and Relate to Prefrontal Activity in Schizophrenia
Charite Univ Med Berlin, Germany; Free Univ Berlin, Germany; Humboldt Univ, Germany; Berlin Inst Hlth, Germany; Max Planck Inst Human Cognit and Brain Sci, Germany; Max Planck UCL Ctr Computat Psychiat and Ageing Res, England; UCL, England.
Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Charite Univ Med Berlin, Germany; Free Univ Berlin, Germany; Humboldt Univ, Germany; Berlin Inst Hlth, Germany.
Max Planck UCL Ctr Computat Psychiat and Ageing Res, England; UCL, England; Scuola Internazl Super Avanzati, Italy; Univ Zurich, Switzerland; Swiss Fed Inst Technol, Switzerland.
Charite Univ Med Berlin, Germany; Free Univ Berlin, Germany; Humboldt Univ, Germany; Berlin Inst Hlth, Germany.
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2020 (English)In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, ISSN 2451-9022, Vol. 5, no 2, p. 173-183Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Reward-based decision making is impaired in patients with schizophrenia (PSZ), as reflected by increased choice switching. The underlying cognitive and motivational processes as well as associated neural signatures remain unknown. Reinforcement learning and hierarchical Bayesian learning account for choice switching in different ways. We hypothesized that enhanced choice switching, as seen in PSZ during reward-based decision making, relates to higher-order beliefs about environmental volatility, and we examined the associated neural activity. METHODS: In total, 46 medicated PSZ and 43 healthy control subjects performed a reward-based decision-making task requiring flexible responses to changing action-outcome contingencies during functional magnetic resonance imaging. Detailed computational modeling of choice data was performed, including reinforcement learning and the hierarchical Gaussian filter. Trajectories of learning from computational modeling informed the analysis of functional magnetic resonance imaging data. RESULTS: A 3-level hierarchical Gaussian filter accounted best for the observed choice data. This model revealed a heightened initial belief about environmental volatility and a stronger influence of volatility on lower-level learning of action-outcome contingencies in PSZ as compared with healthy control subjects. This was replicated in an independent sample of nonmedicated PSZ. Beliefs about environmental volatility were reflected by higher activity in dorsolateral prefrontal cortex of PSZ as compared with healthy control subjects. CONCLUSIONS: Our study suggests that PSZ inferred the environment as overly volatile, which may explain increased choice switching. In PSZ, activity in dorsolateral prefrontal cortex was more strongly related to beliefs about environmental volatility. Our computational phenotyping approach may provide useful information to dissect clinical heterogeneity and could improve prediction of outcome.

Place, publisher, year, edition, pages
ELSEVIER , 2020. Vol. 5, no 2, p. 173-183
Keywords [en]
Bayesian learning; Computational psychiatry; Neuroimaging; Psychosis; Reinforcement learning; Schizophrenia
National Category
Psychology (excluding Applied Psychology)
Identifiers
URN: urn:nbn:se:liu:diva-164040DOI: 10.1016/j.bpsc.2019.10.007ISI: 000512908200007PubMedID: 31937449OAI: oai:DiVA.org:liu-164040DiVA, id: diva2:1411747
Note

Funding Agencies|Max Planck SocietyMax Planck SocietyFoundation CELLEX; German Research FoundationGerman Research Foundation (DFG) [DFG SCHL1969/1-2, DFG SCHL 1969/3-1, DFG SCHL1969/4-1]; Charite Clinician-Scientist Program of the Berlin Institute of Health; Rene and Susanne Braginsky Foundation; University of Zurich

Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-04

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Böhme, Rebecca
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Center for Social and Affective NeuroscienceFaculty of Medicine and Health Sciences
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