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Wise T, Robinson OJ, Gillan CM. Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling. Biol Psychiatry 2023; 93:690-703. [PMID: 36725393 PMCID: PMC10017264 DOI: 10.1016/j.biopsych.2022.09.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/09/2022] [Accepted: 09/27/2022] [Indexed: 02/03/2023]
Abstract
Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.
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Affiliation(s)
- Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Oliver J Robinson
- Neuroscience and Mental Health Group, Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Research Department of Clinical Education and Health Psychology, University College London, London, United Kingdom
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
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Feher da Silva C, Lombardi G, Edelson M, Hare TA. Rethinking model-based and model-free influences on mental effort and striatal prediction errors. Nat Hum Behav 2023:10.1038/s41562-023-01573-1. [PMID: 37012365 DOI: 10.1038/s41562-023-01573-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 02/27/2023] [Indexed: 04/05/2023]
Abstract
A standard assumption in neuroscience is that low-effort model-free learning is automatic and continuously used, whereas more complex model-based strategies are only used when the rewards they generate are worth the additional effort. We present evidence refuting this assumption. First, we demonstrate flaws in previous reports of combined model-free and model-based reward prediction errors in the ventral striatum that probably led to spurious results. More appropriate analyses yield no evidence of model-free prediction errors in this region. Second, we find that task instructions generating more correct model-based behaviour reduce rather than increase mental effort. This is inconsistent with cost-benefit arbitration between model-based and model-free strategies. Together, our data indicate that model-free learning may not be automatic. Instead, humans can reduce mental effort by using a model-based strategy alone rather than arbitrating between multiple strategies. Our results call for re-evaluation of the assumptions in influential theories of learning and decision-making.
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Affiliation(s)
| | - Gaia Lombardi
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Micah Edelson
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Todd A Hare
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland.
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Brandl F, Knolle F, Avram M, Leucht C, Yakushev I, Priller J, Leucht S, Ziegler S, Wunderlich K, Sorg C. Negative symptoms, striatal dopamine and model-free reward decision-making in schizophrenia. Brain 2023; 146:767-777. [PMID: 35875972 DOI: 10.1093/brain/awac268] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/13/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Negative symptoms, such as lack of motivation or social withdrawal, are highly prevalent and debilitating in patients with schizophrenia. Underlying mechanisms of negative symptoms are incompletely understood, thereby preventing the development of targeted treatments. We hypothesized that in patients with schizophrenia during psychotic remission, impaired influences of both model-based and model-free reward predictions on decision-making ('reward prediction influence', RPI) underlie negative symptoms. We focused on psychotic remission, because psychotic symptoms might confound reward-based decision-making. Moreover, we hypothesized that impaired model-based/model-free RPIs depend on alterations of both associative striatum dopamine synthesis and storage (DSS) and executive functioning. Both factors influence RPI in healthy subjects and are typically impaired in schizophrenia. Twenty-five patients with schizophrenia with pronounced negative symptoms during psychotic remission and 24 healthy controls were included in the study. Negative symptom severity was measured by the Positive and Negative Syndrome Scale negative subscale, model-based/model-free RPI by the two-stage decision task, associative striatum DSS by 18F-DOPA positron emission tomography and executive functioning by the symbol coding task. Model-free RPI was selectively reduced in patients and associated with negative symptom severity as well as with reduced associative striatum DSS (in patients only) and executive functions (both in patients and controls). In contrast, model-based RPI was not altered in patients. Results provide evidence for impaired model-free reward prediction influence as a mechanism for negative symptoms in schizophrenia as well as for reduced associative striatum dopamine and executive dysfunction as relevant factors. Data suggest potential treatment targets for patients with schizophrenia and pronounced negative symptoms.
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Affiliation(s)
- Felix Brandl
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, 81675, Germany
| | - Franziska Knolle
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, UK
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, 23538, Germany
| | - Claudia Leucht
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, 81675, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, 81675, Germany
| | - Josef Priller
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,Neuropsychiatry, Charité-Universitätsmedizin Berlin, and DZNE, Berlin, 10117, Germany.,UK DRI at University of Edinburgh, Edinburgh EH16 4SB, UK.,IoPPN, King's College London, London SE5 8AF, UK
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,Department of Psychosis studies, King's College London, London, UK
| | - Sibylle Ziegler
- Department of Nuclear Medicine, Ludwig-Maximilians University Munich, Munich, 81377, Germany
| | - Klaus Wunderlich
- Department of Psychology, Ludwig-Maximilians University Munich, Munich, 81377, Germany
| | - Christian Sorg
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, 81675, Germany
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Sebold M, Chen H, Önal A, Kuitunen-Paul S, Mojtahedzadeh N, Garbusow M, Nebe S, Wittchen HU, Huys QJM, Schlagenhauf F, Rapp MA, Smolka MN, Heinz A. Stronger Prejudices Are Associated With Decreased Model-Based Control. Front Psychol 2022; 12:767022. [PMID: 35069341 PMCID: PMC8767058 DOI: 10.3389/fpsyg.2021.767022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 11/29/2021] [Indexed: 12/01/2022] Open
Abstract
Background: Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or "automatic" reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making. Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale. Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices. Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.
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Affiliation(s)
- Miriam Sebold
- 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
- Department for Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - Hao Chen
- Department of Psychiatry, Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Aleyna Önal
- 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
| | - Sören Kuitunen-Paul
- Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Negin Mojtahedzadeh
- Department of Psychiatry, Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Maria Garbusow
- 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
| | - Stephan Nebe
- Department of Economics, Zurich Center for Neuroeconomics, University of Zurich, Zurich, Switzerland
| | - Hans-Ulrich Wittchen
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Quentin J M Huys
- Division of Psychiatry, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Florian Schlagenhauf
- 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
| | - Michael A Rapp
- Department for Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - Michael N Smolka
- Department of Psychiatry, Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Andreas Heinz
- 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
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van Swieten MMH, Bogacz R, Manohar SG. Hunger improves reinforcement-driven but not planned action. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:1196-1206. [PMID: 34652602 PMCID: PMC8563670 DOI: 10.3758/s13415-021-00921-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/17/2021] [Indexed: 11/08/2022]
Abstract
Human decisions can be reflexive or planned, being governed respectively by model-free and model-based learning systems. These two systems might differ in their responsiveness to our needs. Hunger drives us to specifically seek food rewards, but here we ask whether it might have more general effects on these two decision systems. On one hand, the model-based system is often considered flexible and context-sensitive, and might therefore be modulated by metabolic needs. On the other hand, the model-free system's primitive reinforcement mechanisms may have closer ties to biological drives. Here, we tested participants on a well-established two-stage sequential decision-making task that dissociates the contribution of model-based and model-free control. Hunger enhanced overall performance by increasing model-free control, without affecting model-based control. These results demonstrate a generalized effect of hunger on decision-making that enhances reliance on primitive reinforcement learning, which in some situations translates into adaptive benefits.
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Affiliation(s)
| | - Rafal Bogacz
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Sanjay G Manohar
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.
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Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine. CURRENT ADDICTION REPORTS 2021. [DOI: 10.1007/s40429-021-00399-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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