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  Volatility estimates increase choice switching and relate to prefrontal activity in schizophrenia

Deserno, L., Boehme, R., Mathys, C., Katthagen, T., Kaminski, J., Stephan, K. E., et al. (2020). Volatility estimates increase choice switching and relate to prefrontal activity in schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(2), 173-183. doi:10.1016/j.bpsc.2019.10.007.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-8526-A Version Permalink: http://hdl.handle.net/21.11116/0000-0005-E626-D
Genre: Journal Article

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 Creators:
Deserno, Lorenz1, 2, 3, 4, Author              
Boehme, Rebecca1, 5, Author
Mathys, Christoph3, 4, 6, 7, Author
Katthagen, Teresa1, Author
Kaminski, Jakob1, Author
Stephan, Klaas Enno4, 7, 8, Author
Heinz, Andreas1, 9, 10, Author
Schlagenhauf, Florian1, 2, 10, Author              
Affiliations:
1Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Germany, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom, ou_persistent22              
4Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom, ou_persistent22              
5Center for Social and Affective Neuroscience, Linköping University, Sweden, ou_persistent22              
6International School for Advanced Studies, Trieste, Italy, ou_persistent22              
7Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich, Switzerland, ou_persistent22              
8Max Planck Institute for Metabolism Research, Cologne, Germany, ou_persistent22              
9NeuroCure Cluster of Excellence, Charité University Medicine Berlin, Germany, ou_persistent22              
10Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              

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Free keywords: Bayesian learning; Computational psychiatry; Neuroimaging; Psychosis; Reinforcement learning; Schizophrenia
 Abstract: 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.

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Language(s): eng - English
 Dates: 2019-09-112019-07-242019-10-062019-11-052020-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.bpsc.2019.10.007
Other: Epub ahead of print
PMID: 31937449
 Degree: -

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Funding organization : Max Planck Society
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Funding organization : Foundation CELLEX
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Grant ID : SCHL1969/1-2 ; SCHL 1969/3-1 ; SCHL1969/4-1
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Funding organization : German Research Foundation (DFG)
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Funding program : Charite Clinician-Scientist Program
Funding organization : Berlin Institute of Health
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Funding organization : Rene and Susanne Braginsky Foundation
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Funding program : -
Funding organization : University of Zurich

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Title: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 5 (2) Sequence Number: - Start / End Page: 173 - 183 Identifier: ISSN: 2451-9022
CoNE: https://pure.mpg.de/cone/journals/resource/2451-9022