Atypical processing of uncertainty in individuals at risk for psychosis
•Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility.•Low-level prediction error (PE) signals evoke increased frontal activity in CHR.•Volatility-related PEs are associated with reduced frontal activity in CHR.•Frontal cortical activation to low-level PEs reflects imp...
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| Published in | NeuroImage clinical Vol. 26; p. 102239 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.01.2020
Elsevier |
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| Online Access | Get full text |
| ISSN | 2213-1582 2213-1582 |
| DOI | 10.1016/j.nicl.2020.102239 |
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| Abstract | •Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility.•Low-level prediction error (PE) signals evoke increased frontal activity in CHR.•Volatility-related PEs are associated with reduced frontal activity in CHR.•Frontal cortical activation to low-level PEs reflects impaired clinical functioning.•Atypical PE learning signal representations may promote delusion formation in CHR.
Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals.
Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data.
Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning.
Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation. |
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| AbstractList | Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals.Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data.Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning.Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation. •Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility.•Low-level prediction error (PE) signals evoke increased frontal activity in CHR.•Volatility-related PEs are associated with reduced frontal activity in CHR.•Frontal cortical activation to low-level PEs reflects impaired clinical functioning.•Atypical PE learning signal representations may promote delusion formation in CHR. Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation. • Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility. • Low-level prediction error (PE) signals evoke increased frontal activity in CHR. • Volatility-related PEs are associated with reduced frontal activity in CHR. • Frontal cortical activation to low-level PEs reflects impaired clinical functioning. • Atypical PE learning signal representations may promote delusion formation in CHR. Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals ( n = 13) and control participants ( n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation. Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour - with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental 'volatility' - and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals' behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour - with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental 'volatility' - and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals' behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation. Highlights•Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility. •Low-level prediction error (PE) signals evoke increased frontal activity in CHR. •Volatility-related PEs are associated with reduced frontal activity in CHR. •Frontal cortical activation to low-level PEs reflects impaired clinical functioning. •Atypical PE learning signal representations may promote delusion formation in CHR. |
| ArticleNumber | 102239 |
| Author | Mathys, Christoph D. Tittgemeyer, Marc Brodersen, Kay H. Julkowski, Dominika Cole, David M. Schilbach, Leonhard Diaconescu, Andreea O. Vogeley, Kai Pfeiffer, Ulrich J. Ruhrmann, Stephan Stephan, Klaas E. |
| AuthorAffiliation | b Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Psychiatric Hospital of the University of Zurich, Zurich, Switzerland h Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany f Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy n Cologne Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), Germany d Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada e Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany c Department of Psychiatry (UPK), University of Basel, Basel, Switzerland g Interacting Minds Centre, Aarhus University, Aarhus, Denmark m Max Planck Institute for Metabolism Research, Cologne, Germany i Graduate School for Systemic Neuroscience, Munich, Germany l Kliniken der Heinrich-Heine-Universität/LVR-K |
| AuthorAffiliation_xml | – name: a Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland – name: l Kliniken der Heinrich-Heine-Universität/LVR-Klinik Düsseldorf, Düsseldorf, Germany – name: p Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom – name: b Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Psychiatric Hospital of the University of Zurich, Zurich, Switzerland – name: i Graduate School for Systemic Neuroscience, Munich, Germany – name: d Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada – name: m Max Planck Institute for Metabolism Research, Cologne, Germany – name: n Cologne Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), Germany – name: f Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy – name: j International Max Planck Research School for Translational Psychiatry, Munich, Germany – name: h Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany – name: k Ludwig-Maximilians-Universität München, Munich, Germany – name: o Institute for Neuroscience and Medicine – Cognitive Neuroscience (INM3), Research Center Juelich, Juelich, Germany – name: e Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany – name: g Interacting Minds Centre, Aarhus University, Aarhus, Denmark – name: c Department of Psychiatry (UPK), University of Basel, Basel, Switzerland |
| Author_xml | – sequence: 1 givenname: David M. orcidid: 0000-0001-9260-0430 surname: Cole fullname: Cole, David M. email: dcole.neurosci@gmail.com organization: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland – sequence: 2 givenname: Andreea O. orcidid: 0000-0002-3633-9757 surname: Diaconescu fullname: Diaconescu, Andreea O. organization: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland – sequence: 3 givenname: Ulrich J. surname: Pfeiffer fullname: Pfeiffer, Ulrich J. organization: Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany – sequence: 4 givenname: Kay H. orcidid: 0000-0002-7707-090X surname: Brodersen fullname: Brodersen, Kay H. organization: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland – sequence: 5 givenname: Christoph D. surname: Mathys fullname: Mathys, Christoph D. organization: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland – sequence: 6 givenname: Dominika surname: Julkowski fullname: Julkowski, Dominika organization: Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany – sequence: 7 givenname: Stephan surname: Ruhrmann fullname: Ruhrmann, Stephan organization: Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany – sequence: 8 givenname: Leonhard surname: Schilbach fullname: Schilbach, Leonhard organization: Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany – sequence: 9 givenname: Marc orcidid: 0000-0001-5072-2149 surname: Tittgemeyer fullname: Tittgemeyer, Marc organization: Max Planck Institute for Metabolism Research, Cologne, Germany – sequence: 10 givenname: Kai orcidid: 0000-0002-5891-5831 surname: Vogeley fullname: Vogeley, Kai organization: Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany – sequence: 11 givenname: Klaas E. orcidid: 0000-0002-8594-9092 surname: Stephan fullname: Stephan, Klaas E. organization: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32182575$$D View this record in MEDLINE/PubMed |
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| Keywords | Decision-making Hierarchical Bayesian learning Computational psychiatry At-risk mental state Volatility Prodromal |
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| PublicationYear | 2020 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
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| Snippet | •Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility.•Low-level prediction error (PE) signals evoke increased frontal activity... Highlights•Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility. •Low-level prediction error (PE) signals evoke increased... Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional... • Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility. • Low-level prediction error (PE) signals evoke increased frontal... |
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| SubjectTerms | At-risk mental state Brain - physiopathology Computational psychiatry Decision-making Female Hierarchical Bayesian learning Humans Learning - physiology Magnetic Resonance Imaging Male Prodromal Psychotic Disorders - physiopathology Radiology Regular Uncertainty Volatility Young Adult |
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| Title | Atypical processing of uncertainty in individuals at risk for psychosis |
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