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Hyper-responsivity to losses in the anterior insula during economic choice scales with depression severity

Published online by Cambridge University Press:  07 June 2017

J. B. Engelmann
Affiliation:
Center for Research in Experimental Economics and Political Decision Making (CREED), Amsterdam School of Economics, University of Amsterdam and The Tinbergen Institute, Amsterdam, The Netherlands Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, EN Nijmegen, The Netherlands
G. S. Berns
Affiliation:
Department of Psychology, Emory University, Atlanta, GA, USA
B. W. Dunlop*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30329, USA
*
*Address for correspondence: B. W. Dunlop, M.D., Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, 12 Executive Park NE, 3rd Floor, Atlanta, GA 30329, USA. (Email: bdunlop@emory.edu)

Abstract

Background

Commonly observed distortions in decision-making among patients with major depressive disorder (MDD) may emerge from impaired reward processing and cognitive biases toward negative events. There is substantial theoretical support for the hypothesis that MDD patients overweight potential losses compared with gains, though the neurobiological underpinnings of this bias are uncertain.

Methods

Twenty-one unmedicated patients with MDD were compared with 25 healthy controls (HC) using functional magnetic resonance imaging (fMRI) together with an economic decision-making task over mixed lotteries involving probabilistic gains and losses. Region-of-interest analyses evaluated neural signatures of gain and loss coding within a core network of brain areas known to be involved in valuation (anterior insula, caudate nucleus, ventromedial prefrontal cortex).

Results

Usable fMRI data were available for 19 MDD and 23 HC subjects. Anterior insula signal showed negative coding of losses (gain > loss) in HC subjects consistent with previous findings, whereas MDD subjects demonstrated significant reversals in these associations (loss > gain). Moreover, depression severity further enhanced the positive coding of losses in anterior insula, ventromedial prefrontal cortex, and caudate nucleus. The hyper-responsivity to losses displayed by the anterior insula of MDD patients was paralleled by a reduced influence of gain, but not loss, stake size on choice latencies.

Conclusions

Patients with MDD demonstrate a significant shift from negative to positive coding of losses in the anterior insula, revealing the importance of this structure in value-based decision-making in the context of emotional disturbances.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2017 

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References

Armel, KC, Beaumel, A, Rangel, A (2008). Biasing simple choices by manipulating relative visual attention. Judgment and Decision Making 3, 396403.Google Scholar
Armstrong, T, Olatunji, BO (2012). Clinical psychology review. Elsevier Ltd Clinical Psychology Review 32, 704723.Google Scholar
Bartra, O, McGuire, JT, Kable, JW (2013). The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. NeuroImage 76, 412427.Google Scholar
Beck, AT (2008). The evolution of the cognitive model of depression and its neurobiological correlates. American Psychiatric Association American Journal of Psychiatry 165, 969977.Google Scholar
Callicott, JH, Mattay, VS, Verchinski, BA, Marenco, S, Egan, MF, Weinberger, DR (2003). Complexity of prefrontal cortical dysfunction in schizophrenia: more than up or down. American Psychiatric Publishing American Journal of Psychiatry 160, 22092215.Google Scholar
Camerer, C (2005). Three cheers – psychological, theoretical, empirical – for loss aversion. American Marketing Association Journal of Marketing Research 42, 129133.Google Scholar
Canessa, N, Crespi, C, Motterlini, M, Baud-Bovy, G, Chierchia, G, Pantaleo, G, Tettamanti, M, Cappa, SF (2013). The functional and structural neural basis of individual differences in loss aversion. Society for Neuroscience Journal of Neuroscience 33, 1430714317.CrossRefGoogle ScholarPubMed
Cohn, A, Engelmann, J, Fehr, E, Maréchal, MA (2015). Evidence for countercyclical risk aversion: an experiment with financial professionals. American Economic Review 105, 860885.CrossRefGoogle Scholar
Der-Avakian, A, Markou, A (2012). The neurobiology of anhedonia and other reward-related deficits. Trends in Neurosciences 35, 6877.CrossRefGoogle ScholarPubMed
DiMatteo, MR, Lepper, HS, Croghan, TW (2000). Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Archives of Internal Medicine 160, 21012107.Google Scholar
Disner, SG, Beevers, CG, Haigh, EAP, Beck, AT (2011). Neural mechanisms of the cognitive model of depression. Nature Publishing Group Nature Reviews Neuroscience 12, 467477.CrossRefGoogle ScholarPubMed
Dunlop, BW (2015). Prediction of treatment outcomes in major depressive disorder. Expert Review of Clinical Pharmacology 8, 669672.Google Scholar
Dunlop, BW, Kelley, ME, McGrath, CL (2015). Preliminary findings supporting insula metabolic activity as a predictor of outcome to psychotherapy and medication treatments for depression. Journal of Neuropsychiatry and Clinical Neurosciences 27, 237239.Google Scholar
Dunlop, BW, Rajendra, JK, Craighead, WE, Kelley, ME, McGrath, CL, Choi, KS, Kinkead, B, Nemeroff, CB, Mayberg, HS (2017). Functional connectivity of the subcallosal cingulate cortex identifies differential outcomes to treatment with cognitive behavior therapy or antidepressant medication for major depressive disorder. American Journal of Psychiatry. doi: 10.1176/appi.ajp.2016.16050518.Google Scholar
Engelmann, JB, Maciuba, B, Vaughan, C, Paulus, MP, Dunlop, BW (2013). Posttraumatic stress disorder increases sensitivity to long term losses among patients with major depressive disorder. Ed. A Bruce PLoS ONE 8, e78292.Google Scholar
Engelmann, JB, Meyer, F, Fehr, E, Ruff, CC (2015). Anticipatory anxiety disrupts neural valuation during risky choice. Society for Neuroscience Journal of Neuroscience 35, 30853099.Google Scholar
First, MB (1995). Structured Clinical Interview for the DSM (SCID), pp. 16. John Wiley & Sons, Inc.: Hoboken, NJ, USA.Google Scholar
Gradin, VB, Kumar, P, Waiter, G, Ahearn, T, Stickle, C, Milders, M, Reid, I, Hall, J, Steele, JD (2011). Expected value and prediction error abnormalities in depression and schizophrenia. Oxford University Press Brain 134, awr059aw1764.Google Scholar
Gradin, VB, Pérez, A, MacFarlane, JA, Cavin, I, Waiter, G, Engelmann, J, Dritschel, B, Pomi, A, Matthews, K, Steele, JD (2015). Abnormal brain responses to social fairness in depression: an fMRI study using the Ultimatum Game. Psychological Medicine 45, 12411251.Google Scholar
Grinband, J, Wager, TD, Lindquist, M, Ferrera, VP, Hirsch, J (2008). Detection of time-varying signals in event-related fMRI designs. NeuroImage 43, 509520.Google Scholar
Hamilton, M (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology 32, 5055.CrossRefGoogle ScholarPubMed
Hamilton, M (1967). Development of a rating scale for primary depressive illness. Blackwell Publishing Ltd British Journal of Social and Clinical Psychology 6, 278296.Google Scholar
Harlé, KM, Allen, JJB, Sanfey, AG (2010). The impact of depression on social economic decision making. Journal of Abnormal Psychology 119, 440446.Google Scholar
Harlé, KM, Chang, LJ, van't Wout, M, Sanfey, AG (2012). The neural mechanisms of affect infusion in social economic decision-making: a mediating role of the anterior insula. NeuroImage 61, 3240.Google Scholar
Henson, RNA, Price, CJ, Rugg, MD, Turner, R, Friston, KJ (2002). Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations. NeuroImage 15, 8397.CrossRefGoogle ScholarPubMed
Huys, QJ, Pizzagalli, DA, Bogdan, R, Dayan, P (2013). Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. BioMed Central Ltd Biology of Mood & Anxiety Disorders 3, 12.Google Scholar
Kahneman, D, Knetsch, JL, Thaler, RH (1990). Experimental tests of the endowment effect and the Coase theorem. The University of Chicago Press Journal of Political Economy 98, 13251348.Google Scholar
Knutson, B, Greer, SM (2008). Anticipatory affect: neural correlates and consequences for choice. Philosophical Transactions of the Royal Society B: Biological Sciences 363, 37713786.Google Scholar
Knutson, B, Wimmer, GE, Kuhnen, CM, Winkielman, P (2008). Nucleus accumbens activation mediates the influence of reward cues on financial risk taking. NeuroReport 19, 509513.Google Scholar
Krajbich, I, Armel, C, Rangel, A (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Publishing Group Nature Neuroscience 13, 12921298.Google Scholar
Krajbich, I, Bartling, B, Hare, T, Fehr, E (2015). Rethinking fast and slow based on a critique of reaction-time reverse inference. Nature Communications 6, 7455.Google Scholar
Krajbich, I, Lu, D, Camerer, C, Rangel, A (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology 3, 193.Google Scholar
Leahy, RL (2001). Depressive decision making: validation of the portfolio theory model. Journal of Cognitive Psychotherapy 15, 341362.Google Scholar
Levy, DJ, Glimcher, PW (2012). The root of all value: a neural common currency for choice. Elsevier Ltd Current Opinion in Neurobiology 22, 10271038.Google Scholar
Lim, S-L, O'Doherty, JP, Rangel, A (2011). The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. Society for Neuroscience Journal of Neuroscience 31, 1321413223.Google Scholar
McClure, EB, Monk, CS, Nelson, EE, Parrish, JM, Adler, A, Blair, RJR, Fromm, SJ, Charney, DS, Leibenluft, E, Ernst, M, Pine, DS (2007). Abnormal attention modulation of fear circuit function in pediatric generalized anxiety disorder. Archives of General Psychiatry 64, 97106.Google Scholar
McClure, SM, York, MK, Montague, PR (2004). The neural substrates of reward processing in humans: the modern role of fMRI. SAGE Publications Neuroscientist 10, 260268.Google Scholar
McGrath, CL, Kelley, ME, Holtzheimer, PE, Dunlop, BW, Craighead, WE, Franco, AR, Craddock, RC, Mayberg, HS (2013). Toward a neuroimaging treatment selection biomarker for major depressive disorder. American Medical Association JAMA Psychiatry 70, 821829.Google Scholar
Pammi, CVS, Pillai Geethabhavan Rajesh, P, Kesavadas, C, Rappai Mary, P, Seema, S, Radhakrishnan, A, Sitaram, R (2015). Neural loss aversion differences between depression patients and healthy individuals: a functional MRI investigation. SAGE Publications Neuroradiology Journal 28, 97105.Google Scholar
Paulus, MP, Rogalsky, C, Simmons, A, Feinstein, JS, Stein, MB (2003). Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. NeuroImage 19, 14391448.Google Scholar
Paulus, MP, Stein, MB (2006). An insular view of anxiety. BPS 60, 383387.Google ScholarPubMed
Paulus, MP, Yu, AJ (2012). Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends in Cognitive Sciences 16, 476483.CrossRefGoogle ScholarPubMed
Peckham, AD, McHugh, RK, Otto, MW (2010). A meta-analysis of the magnitude of biased attention in depression. Wiley Subscription Services, Inc., A Wiley Company Depression and Anxiety 27, 11351142.Google Scholar
Pessiglione, M, Seymour, B, Flandin, G, Dolan, RJ, Frith, CD (2006). Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature 442, 10421045.CrossRefGoogle ScholarPubMed
Pizzagalli, D, Holmes, A, Dillon, D, Goetz, E, Birk, J, Bogdan, R, Dougherty, D, Iosifescu, D, Rauch, S, Fava, M (2009). Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. American Psychiatric Association American Journal of Psychiatry 166, 702710.Google Scholar
Price, JL, Drevets, WC (2010). Neurocircuitry of mood disorders. Nature Publishing Group Neuropsychopharmacology 35, 192216.Google Scholar
Rangel, A, Camerer, C, Montague, PR (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience 9, 545556.Google Scholar
Richard-Devantoy, S, Olié, E, Guillaume, S, Courtet, P (2016). Decision-making in unipolar or bipolar suicide attempters. Journal of Affective Disorders 190, 128136.CrossRefGoogle ScholarPubMed
Seymour, B, Daw, N, Dayan, P, Singer, T, Dolan, R (2007). Differential encoding of losses and gains in the human striatum. Society for Neuroscience Journal of Neuroscience 27, 48264831.Google Scholar
Sharp, C, Monterosso, J, Montague, PR (2012). Neuroeconomics: a bridge for translational research. Biological Psychiatry 72, 8792.Google Scholar
Sliz, D, Hayley, S (2012). Major depressive disorder and alterations in insular cortical activity: a review of current functional magnetic imaging research. Frontiers Media SA Frontiers in Human Neuroscience 6, 323.Google Scholar
Sokol-Hessner, P, Camerer, CF, Phelps, EA (2013). Emotion regulation reduces loss aversion and decreases amygdala responses to losses. Oxford University Press Social Cognitive and Affective Neuroscience 8, 341350.Google Scholar
Thaler, RH, Johnson, EJ (1990). Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice. INFORMS Management Science 36, 643660.Google Scholar
Thompson, MG, Heller, K (1993). Distinction between quality and quantity of problem-solving responses among depressed older women. Psychology and Aging 8, 347359.Google Scholar
Tom, SM, Fox, CR, Trepel, C, Poldrack, RA (2007). The neural basis of loss aversion in decision-making under risk. Science 315, 515518.CrossRefGoogle ScholarPubMed
Treadway, MT, Zald, DH (2011). Reconsidering anhedonia in depression: lessons from translational neuroscience. Neuroscience and Biobehavioral Reviews 35, 537555.Google Scholar
Trivedi, MH, Greer, TL (2014). Cognitive dysfunction in unipolar depression: implications for treatment. Journal of Affective Disorders 152–154, 1927.Google Scholar
Ubl, B, Kuehner, C, Kirsch, P, Ruttorf, M, Diener, C, Flor, H (2015). Altered neural reward and loss processing and prediction error signalling in depression. Oxford University Press Social Cognitive and Affective Neuroscience 10, nsu158ns1112.CrossRefGoogle ScholarPubMed
Wager, TD, Keller, MC, Lacey, SC, Jonides, J (2005). Increased sensitivity in neuroimaging analyses using robust regression. NeuroImage 26, 99113.Google Scholar
Weber, B, Aholt, A, Neuhaus, C, Trautner, P, Elger, CE, Teichert, T (2007). Neural evidence for reference-dependence in real-market-transactions. NeuroImage 35, 441447.Google Scholar
Wechsler, D (1999). Wechsler Abbreviated Scale of Intelligence (WASI). Journal of Psychoeducational Assessment, vol 31, pp. 337341. Psychological Corporation: San Antonio, TX.Google Scholar
Whooley, MA, Kiefe, CI, Chesney, MA, Markovitz, JH, Matthews, K, Hulley, SB (2002). Depressive symptoms, unemployment, and loss of income: the CARDIA study. American Medical Association Archives of Internal Medicine 162, 26142620.Google Scholar
Wilkinson, D, Halligan, P (2004). The relevance of behavioural measures for functional-imaging studies of cognition. Nature Reviews Neuroscience 5, 6773.Google Scholar
Yarkoni, T, Barch, DM, Gray, JR, Conturo, TE, Braver, TS (2009). BOLD correlates of trial-by-trial reaction time variability in gray and white matter: a multi-study fMRI analysis. Ed. B Baune. Public Library of Science PLoS ONE 4, e4257.Google Scholar
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