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Wagner F, Rogenz J, Opitz L, Maas J, Schmidt A, Brodoehl S, Ullsperger M, Klingner CM. Reward network dysfunction is associated with cognitive impairment after stroke. Neuroimage Clin 2023; 39:103446. [PMID: 37307650 PMCID: PMC10276182 DOI: 10.1016/j.nicl.2023.103446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/14/2023]
Abstract
Stroke survivors not only suffer from severe motor, speech and neurocognitive deficits, but in many cases also from a "lack of pleasure" and a reduced motivational level. Especially apathy and anhedonic symptoms can be linked to a dysfunction of the reward system. Rewards are considered as important co-factor for learning, so the question arises as to why and how this affects the rehabilitation of stroke patients. We investigated reward behaviour, learning ability and brain network connectivity in acute (3-7d) mild to moderate stroke patients (n = 28) and age-matched healthy controls (n = 26). Reward system activity was assessed using the Monetary Incentive Delay task (MID) during magnetoencephalography (MEG). Coherence analyses were used to demonstrate reward effects on brain functional network connectivity. The MID-task showed that stroke survivors had lower reward sensitivity and required greater monetary incentives to improve performance and showed deficits in learning improvement. MEG-analyses showed a reduced network connectivity in frontal and temporoparietal regions. All three effects (reduced reward sensitivity, reduced learning ability and altered cerebral connectivity) were found to be closely related and differed strongly from the healthy group. Our results reinforce the notion that acute stroke induces reward network dysfunction, leading to functional impairment of behavioural systems. These findings are representative of a general pattern in mild strokes and are independent of the specific lesion localisation. For stroke rehabilitation, these results represent an important point to identify the reduced learning capacity after stroke and to implement individualised recovery exercises accordingly.
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Affiliation(s)
- Franziska Wagner
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany.
| | - Jenny Rogenz
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Laura Opitz
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Johanna Maas
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Alexander Schmidt
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Stefan Brodoehl
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Markus Ullsperger
- Faculty of Natural Sciences, Institute of Psychology, 39106 Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Otto-von-Guericke University Magdeburg, Germany
| | - Carsten M Klingner
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
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Belleau EL. Parsing Heterogeneity in Mood Disorders: The Challenges of Modeling Stable Mood Disorder-Related Functional Connectomes. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:7-9. [PMID: 34998484 PMCID: PMC8809886 DOI: 10.1016/j.bpsc.2021.10.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 06/14/2023]
Affiliation(s)
- Emily L Belleau
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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Langenecker SA, Westlund Schreiner M, Thomas LR, Bessette KL, DelDonno SR, Jenkins LM, Easter RE, Stange JP, Pocius SL, Dillahunt A, Love TM, Phan KL, Koppelmans V, Paulus M, Lindquist MA, Caffo B, Mickey BJ, Welsh RC. Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:76-84. [PMID: 34271215 PMCID: PMC8748287 DOI: 10.1016/j.bpsc.2021.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/14/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease. METHODS We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions. RESULTS There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network). CONCLUSIONS In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.
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Affiliation(s)
| | | | - Leah R Thomas
- Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychology, University of Utah, Salt Lake City, Utah
| | - Katie L Bessette
- Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Sophia R DelDonno
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Lisanne M Jenkins
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, Illinois
| | - Rebecca E Easter
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Jonathan P Stange
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois; Department of Psychology, University of Southern California, Los Angeles, California
| | | | - Alina Dillahunt
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - Tiffany M Love
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | | | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | | | - Brian Caffo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Brian J Mickey
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - Robert C Welsh
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
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Lange I, Papalini S, Vervliet B. Experimental models in psychopathology research: The relation between Research Domain Criteria and Experimental Psychopathology. Curr Opin Psychol 2021; 41:118-123. [PMID: 34418641 DOI: 10.1016/j.copsyc.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 06/07/2021] [Accepted: 07/12/2021] [Indexed: 11/18/2022]
Abstract
Experimental Psychopathology (EPP) and the Research Domain Criteria (RDoC) are research approaches that have developed in parallel, providing inter-related yet different scientific frameworks to investigate psychopathology at the intersection of fundamental and applied research. Here we address the overlap and differences between RDoC and EPP, and the challenges that both approaches face. Although overlap between EPP and RDoC can be clearly observed, each approach has its own unique strengths and weaknesses. These aspects will be illustrated by examples with respect to fear conditioning, an experimental procedure that has played a central role in both EPP and RDoC. We see much potential in boosting psychopathology research by combining the strengths of these two approaches.
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Affiliation(s)
- Iris Lange
- Laboratory of Biological Psychology, Department of Brain and Cognition, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium.
| | - Silvia Papalini
- Laboratory of Biological Psychology, Department of Brain and Cognition, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium
| | - Bram Vervliet
- Laboratory of Biological Psychology, Department of Brain and Cognition, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium
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Nielson DM, Keren H, O'Callaghan G, Jackson SM, Douka I, Vidal-Ribas P, Pornpattananangkul N, Camp CC, Gorham LS, Wei C, Kirwan S, Zheng CY, Stringaris A. Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression. Biol Psychiatry 2021; 89:134-143. [PMID: 32797941 PMCID: PMC10726343 DOI: 10.1016/j.biopsych.2020.06.012] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/20/2020] [Accepted: 06/10/2020] [Indexed: 10/24/2022]
Abstract
Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.
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Affiliation(s)
- Dylan M Nielson
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Hanna Keren
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Georgia O'Callaghan
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Sarah M Jackson
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Ioanna Douka
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Pablo Vidal-Ribas
- Social and Behavioral Science Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
| | | | - Christopher C Camp
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Lisa S Gorham
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Christine Wei
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Stuart Kirwan
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Charles Y Zheng
- Machine Learning Team, Functional Magnetic Resonance Imaging Facility, National Institutes of Health, Bethesda, Maryland
| | - Argyris Stringaris
- Section on Clinical and Computational Psychiatry (CompΨ), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.
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Halahakoon DC, Kieslich K, O’Driscoll C, Nair A, Lewis G, Roiser JP. Reward-Processing Behavior in Depressed Participants Relative to Healthy Volunteers: A Systematic Review and Meta-analysis. JAMA Psychiatry 2020; 77:1286-1295. [PMID: 32725180 PMCID: PMC7391183 DOI: 10.1001/jamapsychiatry.2020.2139] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
IMPORTANCE Dysfunctional reward processing is a leading candidate mechanism for the development of certain depressive symptoms, such as anhedonia. However, to our knowledge, there has not yet been a systematic assessment of whether and to what extent depression is associated with impairments on behavioral reward-processing tasks. OBJECTIVE To determine whether depression is associated with impairments in reward-processing behavior. DATA SOURCES The MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies that investigated reward processing using performance on behavioral tasks by individuals with depression and nondepressed control groups, published between January 1, 1946, and August 16, 2019. STUDY SELECTION Studies that contained data regarding performance by depressed and healthy control groups on reward-processing tasks were included in the systematic review and meta-analysis. DATA EXTRACTION AND SYNTHESIS Summary statistics comparing performance between depressed and healthy groups on reward-processing tasks were converted to standardized mean difference (SMD) scores, from which summary effect sizes for overall impairment in reward processing and 4 subcomponent categories were calculated. Study quality, heterogeneity, replicability-index, and publication bias were also assessed. MAIN OUTCOME AND MEASURES Performance on reward-processing tasks. RESULTS The final data set comprised 48 case-control studies (1387 healthy control individuals and 1767 individuals with major depressive disorder). The mean age was 37.85 years and 58% of the participants were women. These studies used tasks assessing option valuation (n = 9), reward bias (n = 6), reward response vigor (n = 12), reinforcement learning (n = 20), and grip force (n = 1). Across all tasks, depression was associated with small to medium impairments in reward-processing behavior (SMD = 0.345; 95% CI, 0.209-0.480). When examining reward-processing subcomponent categories, impairment was associated with tasks assessing option valuation (SMD = 0.309; 95% CI, 0.147-0.471), reward bias (SMD = 0.644; 95% CI, 0.270-1.017), and reinforcement learning (SMD = 0.352; 95% CI, 0.115-0.588) but not reward response vigor (SMD = 0.083; 95% CI, -0.144 to 0.309). The medication status of the major depressive disorder sample did not explain any of the variance in the overall effect size. There was significant between-study heterogeneity overall and in all subcomponent categories other than option valuation. Significant publication bias was identified overall and in the reinforcement learning category. CONCLUSIONS AND RELEVANCE Relative to healthy control individuals, individuals with depression exhibit reward-processing impairments, particularly for tests of reward bias, option valuation, and reinforcement learning. Understanding the neural mechanisms driving these associations may assist in designing novel interventions.
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Affiliation(s)
- D. Chamith Halahakoon
- Institute of Cognitive Neuroscience, University College London, London, England,Department of Psychiatry, University of Oxford, Oxford, England
| | - Karel Kieslich
- Institute of Cognitive Neuroscience, University College London, London, England
| | | | - Akshay Nair
- Max Planck Centre for Computational Psychiatry and Aging Research, University College London, England,Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, England
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, England
| | - Jonathan P. Roiser
- Institute of Cognitive Neuroscience, University College London, London, England
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