Striatal dysfunction during reversal learning in unmedicated schizophrenia patients
Open accessAbstract
Subjects with schizophrenia are impaired at reinforcement-driven reversal learning from as early as their first episode. The neurobiological basis of this deficit is unknown. We obtained behavioral and fMRI data in 24 unmedicated, primarily first episode, schizophrenia patients and 24 age-, IQ- and gender-matched healthy controls during a reversal learning task. We supplemented our fMRI analysis, focusing on learning from prediction errors, with detailed computational modeling to probe task solving strategy including an ability to deploy an internal goal directed model of the task. Patients displayed reduced functional activation in the ventral striatum (VS) elicited by prediction errors. However, modeling task performance revealed that a subgroup did not adjust their behavior according to an accurate internal model of the task structure, and these were also the more severely psychotic patients. In patients who could adapt their behavior, as well as in controls, task solving was best described by cognitive strategies according to a Hidden Markov Model. When we compared patients and controls who acted according to this strategy, patients still displayed a significant reduction in VS activation elicited by informative errors that precede salient changes of behavior (reversals). Thus, our study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies. This result highlights VS dysfunction is tightly linked to a reward-related reversal learning deficit in early, unmedicated schizophrenia patients.
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Bayesian model comparison can clarify whether individual differences in task performance are due to deployment of different cognitive strategies or to differences in implementing these strategies.
“Importantly, this can clarify whether any individual differences in task performance are due to the deployment of different cognitive strategies, or due to differences in implementing these strategies (141).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [141]
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References 64 · Cited by 253
- Authors
- Schlagenhauf, F., Huys, Q. J. M., Deserno, L., Rapp, M. A., Beck, A., Heinze, H.-J., Dolan, R., & Heinz, A.
- Affiliations
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Wellcome Centre for Human Neuroimaging (GB)
- Stiftung Charité (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Corresponding author
- Schlagenhauf, Florian
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-04
- Volume
- 89
- Pages
- 171-180
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3991847
- OpenAlex
- W2162801180
- Cited by
- 253
- References
- 64
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 15 · 2023: 20 · 2022: 22 · 2021: 16 · 2020: 24 · 2019: 16 · 2018: 25 · 2017: 25 · 2016: 35 · 2015: 23 · 2014: 8 · 2013: 1
- FWCI
- 15.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- ETH Zürich Research Collection
- Funders
- Max-Planck-Gesellschaft (STE 1430/2-1, HE 2597/4-3)
- Wellcome Trust (091593/Z/10/Z, 098362/Z/12/Z)
- Deutsche Forschungsgemeinschaft (DFG SCHL 1968/1-1, 14-1, Exc 257, DFG HE2597/4-3, FOR 1617, SCHL 1969/1-1)
- Bundesministerium für Bildung und Forschung (01 GS 08152, 01GQ0411, 01QG87164, 01GS08159)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- schizophrenia, reversal learning, ventral striatum, prediction error, reward learning, first-episode schizophrenia, unmedicated patients, hidden Markov models, computational modeling, psychosis severity
- MeSH terms
- Adult, Basal Ganglia, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Reversal Learning, Reward, Schizophrenia, Young Adult
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