Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-15T07:26:35.121Z Has data issue: false hasContentIssue false
  • English
  • Français

Abnormal reward valuation and event-related connectivity in unmedicated major depressive disorder

Published online by Cambridge University Press:  07 January 2020

S. Rupprechter ,
A. Stankevicius ,
Q. J. M. Huys ,
P. Series  and
J. D. Steele Open the ORCID record for J. D. Steele [Opens in a new window]
S. Rupprechter
Affiliation:
Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
A. Stankevicius
Affiliation:
Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
Q. J. M. Huys
Affiliation:
Max Planck Centre for Computational Psychiatry and Ageing Research, UCL, London, UK Camden and Islington NHS Foundation Trust, London, UK
P. Series
Affiliation:
Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
J. D. Steele*
Affiliation:
Division of Imaging Science and Technology, Medical School, University of Dundee, Dundee, UK Department of Neurology, Ninewells Hospital, NHS Tayside, Dundee, UK
*
Author for correspondence: J. D. Steele, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.

Methods

Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.

Results

Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.

Conclusions

Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.

Keywords

Decision-makingevent-related connectivitymajor depressive disorderneural valuationunmedicated
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 5 , April 2021 , pp. 795 - 803
Check for updates
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage, 76, 412–427. doi: 10.1016/j.neuroimage.2013.02.063.CrossRefGoogle ScholarPubMed
Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. (1996). Comparison of Beck Depression Inventories-IA and -II in psychiatric outpatients. Journal of Personality Assessment, 67(3), 588–597. doi: 10.1207/s15327752jpa6703_13.CrossRefGoogle ScholarPubMed
Bishop, S. J., & Gagne, C. (2018). Anxiety, depression, and decision making: A computational perspective. Annual Review of Neuroscience, 41, 371–388. doi: 10.1146/annurev-neuro-080317-062007.CrossRefGoogle ScholarPubMed
Chang, C. K., Hayes, R. D., Perera, G., Broadbent, M. T., Fernandes, A. C., Lee, W. E., … Stewart, R.(2011). Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PLoS One, 6(5), e19590. doi: 10.1371/journal.pone.0019590.CrossRefGoogle Scholar
Chase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015). Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis. Cognitive. Affective and Behavioral Neuroscience, 15(2), 435–459. doi: 10.3758/s13415-015-0338-7.CrossRefGoogle ScholarPubMed
Chase, H. W., Nusslock, R., Almeida, J. R., Forbes, E. E., LaBarbara, E. J., & Phillips, M. L. (2013). Dissociable patterns of abnormal frontal cortical activation during anticipation of an uncertain reward or loss in bipolar versus major depression. Bipolar Disorders, 15(8), 839–854. doi: 10.1111/bdi.12132.CrossRefGoogle ScholarPubMed
Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine, 11, 126. doi: 10.1186/1741-7015-11-126.CrossRefGoogle ScholarPubMed
Dolan, R. J. (2002). Emotion, cognition, and behavior. Science, 298(5596), 1191–1194. doi: 10.1126/science.1076358.CrossRefGoogle Scholar
Dombrovski, A. Y., Szanto, K., Clark, L., Reynolds, C. F., & Siegle, G. J. (2013). Reward signals, attempted suicide, and impulsivity in late-life depression. Journal of the American Medical Association: Psychiatry, 70(10), 1. doi: 10.1001/jamapsychiatry.2013.75.Google ScholarPubMed
Glascher, J., Hampton, A. N., & O'Doherty, J. P. (2009). Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making. Cerebral Cortex, 19(2), 483–495. doi: 10.1093/cercor/bhn098.CrossRefGoogle ScholarPubMed
Gradin, V. B., Kumar, P., Waiter, G., Ahearn, T., Stickle, C., Milders, M., … Steele, J. D. (2011). Expected value and prediction error abnormalities in depression and schizophrenia. Brain, 134(6), 1751–1764. doi: 10.1093/brain/awr059.CrossRefGoogle Scholar
Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system. Oxford: Oxford University Press.Google Scholar
Greenberg, T., Chase, H. W., Almeida, J. R., Stiffler, R., Zevallos, C. R., Aslam, H. A., … Phillips, M. L. (2015). Moderation of the relationship between reward expectancy and prediction error-related ventral striatal reactivity by anhedonia in unmedicated major depressive disorder: Findings from the EMBARC Study. American Journal of Psychiatry, 172(9), 881–891. doi: 10.1176/appi.ajp.2015.14050594.CrossRefGoogle ScholarPubMed
Ho, T. C., Sacchet, M. D., Connolly, C. G., Margulies, D. S., Tymofiyeva, O., Paulus, M. P., … Yang, T. T. (2017). Inflexible functional connectivity of the dorsal anterior cingulate cortex in adolescent major depressive disorder. Neuropsychopharmacology, 42(12), 2434–2445. doi: 10.1038/npp.2017.103.CrossRefGoogle ScholarPubMed
Holroyd, C. B., & Umemoto, A. (2016). The research domain criteria framework: The case for anterior cingulate cortex. Neuroscience and Biobehavioral Reviews, 71, 418–443. doi: 10.1016/j.neubiorev.2016.09.021.CrossRefGoogle ScholarPubMed
Huys, Q., Daw, N. D., & Dayan, P. (2015). Depression: A decision-theoretic analysis. Annual Review of Neuroscience, 38, 1–23. doi: 10.1146/annurev-neuro-071714-033928.CrossRefGoogle ScholarPubMed
Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013). Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis. Biology of Mood and Anxiety Disorders, 3(1), 12. doi: 10.1186/2045-5380-3-12.CrossRefGoogle ScholarPubMed
Huys, Q., & Renz, D. (2017). A formal valuation framework for emotions and their control. Biological Psychiatry, 82(6), 413–420. doi: 10.1016/j.biopsych.2017.07.003.CrossRefGoogle ScholarPubMed
Johnston, B. A., Tolomeo, S., Gradin, V., Christmas, D., Matthews, K., & Steele, J. D. (2015). Failure of hippocampal deactivation during loss events in treatment-resistant depression. Brain, 138(Pt 9), 2766–2776. doi: 10.1093/brain/awv177.CrossRefGoogle ScholarPubMed
Keren, H., O'Callaghan, G., Vidal-Ribas, P., Buzzell, G. A., Brotman, M. A., Leibenluft, E., … Stringaris, A. (2018). Reward processing in depression: A conceptual and meta-analytic review across fMRI and EEG studies. American Journal of Psychiatry, 175(11), 1111–1120. doi: 10.1176/appi.ajp.2018.17101124. Epub 2018 Jun 20.CrossRefGoogle ScholarPubMed
Kumar, P., Goer, F., Murray, L., Dillon, D. G., Beltzer, M. L., Cohen, A. L., … Pizzagalli, D. A. (2018). Impaired reward prediction error encoding and striatal-midbrain connectivity in depression. Neuropsychopharmacology, 43(7), 1581–1588. doi: 10.1038/s41386-018-0032-x.CrossRefGoogle ScholarPubMed
Kumar, P., Waiter, G., Ahearn, T., Milders, M., Reid, I., & Steele, J. D. (2008). Abnormal temporal difference reward-learning signals in major depression. Brain, 131(Pt 8), 2084–2093. doi: 10.1093/brain/awn136.CrossRefGoogle ScholarPubMed
Lawson, R. P., Nord, C. L., Seymour, B., Thomas, D. L., Dayan, P., Pilling, S., & Roiser, J. P. (2017). Disrupted habenula function in major depression. Molecular Psychiatry, 22(2), 202–208. doi: 10.1038/mp.2016.81.CrossRefGoogle ScholarPubMed
Mai, J. K., Matjtanik, M., & Paxinos, G. (2015). Atlas of the human brain (4th ed.). Cambridge, Massachusetts: Academic Press.Google Scholar
McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. Neuroimage, 61(4), 1277–1286. doi: 10.1016/j.neuroimage.2012.03.068.CrossRefGoogle ScholarPubMed
Nelson, H. E., & Wilson, J. R. (1991). The revised national adult reading test-test manual. Winsor: NFER-Wilson.Google Scholar
O'Doherty, J. P., Hampton, A., & Kim, H. (2007). Model-based fMRI and its application to reward learning and decision making. Annals of the New York Academy of Sciences, 1104, 35–53. doi: 10.1196/annals.1390.022.CrossRefGoogle ScholarPubMed
Perrin, J. S., Merz, S., Bennett, D. M., Currie, J., Steele, D. J., Reid, I. C., & Schwarzbauer, C. (2012). Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder. Proceedings of the National Academy of Sciences of the United States of America, 109(14), 5464–5468. doi: 10.1073/pnas.1117206109.CrossRefGoogle ScholarPubMed
Pizzagalli, D. A. (2014). Depression, stress, and anhedonia: Toward a synthesis and integrated model. Annual Review of Clinical Psychology, 10, 393–423. doi: 10.1146/annurev-clinpsy-050212-185606.CrossRefGoogle Scholar
Redish, A. D. (2004). Addiction as a computational process gone awry. Science, 306(5703), 1944–1947. doi: DOI 10.1126/science.1102384.CrossRefGoogle ScholarPubMed
Redish, A. D., Jensen, S., & Johnson, A. (2008). A unified framework for addiction: Vulnerabilities in the decision process. Behavioral and Brain Sciences, 31(4), 415–437, discussion 437–487. doi: 10.1017/S0140525X0800472X.CrossRefGoogle ScholarPubMed
Rizvi, S. J., Pizzagalli, D. A., Sproule, B. A., & Kennedy, S. H. (2016). Assessing anhedonia in depression: Potentials and pitfalls. Neuroscience & Biobehavioral Reviews, 65, 21–35. doi: 10.1016/j.neubiorev.2016.03.004.CrossRefGoogle ScholarPubMed
Rupprechter, S., Stankevicius, A., Huys, Q., Steele, J. D., & Series, P. (2018). Major depression impairs the Use of reward values for decision-making. Scientific Reports, 8(1), 13798. doi: 10.1038/s41598-018-31730-w.CrossRefGoogle ScholarPubMed
Schmaal, L., Hibar, D. P., Samann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., … Veltman, D. J. (2017). Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Molecular Psychiatry, 22(6), 900–909. doi: 10.1038/mp.2016.60. Epub 2016 May 3.CrossRefGoogle ScholarPubMed
Schmaal, L., Veltman, D. J., van Erp, T. G., Samann, P. G., Frodl, T., Jahanshad, N., … Hibar, D. P. (2015). Subcortical brain alterations in major depressive disorder: Findings from the ENIGMA Major Depressive Disorder working group. Molecular Psychiatry. doi: 10.1038/mp.2015.69.Google ScholarPubMed
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011). The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience, 12(3), 154–167. doi: 10.1038/nrn2994.CrossRefGoogle ScholarPubMed
Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., … Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl 20), 22–33, quiz 34–57.Google ScholarPubMed
Shen, X., Reus, L. M., Cox, S. R., Adams, M. J., Liewald, D. C., Bastin, M. E., … McIntosh, A. M.(2017). Subcortical volume and white matter integrity abnormalities in major depressive disorder: Findings from UK Biobank imaging data. Scientific Reports, 7(1), 5547. doi: 10.1038/s41598-017-05507-6.CrossRefGoogle ScholarPubMed
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217–240. doi: 10.1016/j.neuron.2013.07.007.CrossRefGoogle ScholarPubMed
Slotnick, S. D., Moo, L. R., Segal, J. B., & Hart, J. Jr. (2003). Distinct prefrontal cortex activity associated with item memory and source memory for visual shapes. Brain Research: Cognitive Brain Research, 17(1), 75–82.Google ScholarPubMed
Stankevicius, A., Huys, Q., Kalra, A., & Series, P. (2014). Optimism as a prior belief about the probability of future reward. PLoS Computational Biology, 10, 1–9. doi: e1003605.CrossRefGoogle ScholarPubMed
Steele, J. D., Kumar, P., & Ebmeier, K. P. (2007). Blunted response to feedback information in depressive illness. Brain, 130(Pt 9), 2367–2374. doi: 10.1093/brain/awm150.CrossRefGoogle ScholarPubMed
Steele, J. D., & Paulus, M. P. (2019). Pragmatic neuroscience for clinical psychiatry. British Journal of Psychiatry, 215(1), 404–408. doi: 10.1192/bjp.2019.88.CrossRefGoogle ScholarPubMed
Stephan, K. E., Bach, D. R., Fletcher, P. C., Flint, J., Frank, M. J., Friston, K. J., … Breakspear, M. (2016a). Charting the landscape of priority problems in psychiatry, part 1: Classification and diagnosis. The Lancet. Psychiatry, 3(1), 77–83. doi: 10.1016/S2215-0366(15)00361-2.CrossRefGoogle Scholar
Stephan, K. E., Binder, E. B., Breakspear, M., Dayan, P., Johnstone, E. C., Meyer-Lindenberg, A., … Friston, K. J. (2016b). Charting the landscape of priority problems in psychiatry, part 2: Pathogenesis and aetiology. The Lancet. Psychiatry, 3(1), 84–90. doi: 10.1016/S2215-0366(15)00360-0.CrossRefGoogle Scholar
Tolomeo, S., Christmas, D., Jentzsch, I., Johnston, B. A., Sprenglemeyer, R., Matthews, K., & Steele, J. D. (2016). A causal role for the anterior mid-cingulate cortex in negative affect and cognitive control. Brain, 139(6), 1844–1854.CrossRefGoogle ScholarPubMed
Treadway, M. T., Buckholtz, J. W., Cowan, R. L., Woodward, N. D., Li, R., Ansari, M. S., … Zald, D. H. (2012). Dopaminergic mechanisms of individual differences in human effort-based decision-making. Journal of Neuroscience, 32(18), 6170–6176. doi: 10.1523/JNEUROSCI.6459-11.2012.CrossRefGoogle ScholarPubMed
Whiteford, H. A., Degenhardt, L., Rehm, J., Baxter, A. J., Ferrari, A. J., Erskine, H. E., … Vos, T. (2013). Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. Lancet, 382(9904), 1575–1586. doi: 10.1016/S0140-6736(13)61611-6.CrossRefGoogle ScholarPubMed
WHO (2018). Depression: Fact Sheet (updated Feb 2017). Retrieved from http://www.who.int/mediacentre/factsheets/fs369/en/Google Scholar
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. doi: 10.1038/nmeth.1635.CrossRefGoogle ScholarPubMed
Zhang, W. N., Chang, S. H., Guo, L. Y., Zhang, K. L., & Wang, J. (2013). The neural correlates of reward-related processing in major depressive disorder: A meta-analysis of functional magnetic resonance imaging studies. Journal of Affective Disorders. doi: 10.1016/j.jad.2013.06.039.CrossRefGoogle ScholarPubMed
Supplementary material: File

Rupprechter et al. supplementary material

Rupprechter et al. supplementary material

Download Rupprechter et al. supplementary material(File)
File 1.9 MB