Lorenz Deserno1, Rebecca Boehme2, Christoph Mathys3, Teresa Katthagen4, Jakob Kaminski4, Klaas Enno Stephan5, Andreas Heinz6, Florian Schlagenhauf7. 1. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom. Electronic address: l.deserno@ucl.ac.uk. 2. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Center for Social and Affective Neuroscience, Linköping University, Linköping, Sweden. 3. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom; Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland. 4. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 5. Max Planck Institute for Metabolism Research, Cologne, Germany; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland. 6. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Cluster of Excellence NeuroCure, Charité Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany. 7. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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.
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.
Authors: Lilian A Weber; Andreea O Diaconescu; Christoph Mathys; André Schmidt; Michael Kometer; Franz Vollenweider; Klaas E Stephan Journal: J Neurosci Date: 2020-06-19 Impact factor: 6.167
Authors: Martin Panitz; Lorenz Deserno; Elisabeth Kaminski; Arno Villringer; Bernhard Sehm; Florian Schlagenhauf Journal: Cereb Cortex Commun Date: 2022-01-27
Authors: Eren Kafadar; Vijay A Mittal; Gregory P Strauss; Hannah C Chapman; Lauren M Ellman; Sonia Bansal; James M Gold; Ben Alderson-Day; Samuel Evans; Jamie Moffatt; Steven M Silverstein; Elaine F Walker; Scott W Woods; Philip R Corlett; Albert R Powers Journal: Schizophr Res Date: 2020-06-24 Impact factor: 4.939