Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T14:52:25.141Z Has data issue: false hasContentIssue false
  • English
  • Français

The Allure of High-Risk Rewards in Huntington’s disease

Published online by Cambridge University Press:  28 December 2015

Nelleke C. van Wouwe ,
Kristen E. Kanoff ,
Daniel O. Claassen ,
K. Richard Ridderinkhof ,
Peter Hedera ,
Madaline B. Harrison  and
Scott A. Wylie
Nelleke C. van Wouwe*
Affiliation:
Department of Neurology, Vanderbilt University Medical Center, Tennessee
Kristen E. Kanoff
Affiliation:
Department of Neurology, Vanderbilt University Medical Center, Tennessee
Daniel O. Claassen
Affiliation:
Department of Neurology, Vanderbilt University Medical Center, Tennessee
K. Richard Ridderinkhof
Affiliation:
Department of Psychology, University of Amsterdam, the Netherlands Amsterdam Brain & Cognition (ABC), University of Amsterdam, the Netherlands
Peter Hedera
Affiliation:
Department of Neurology, Vanderbilt University Medical Center, Tennessee
Madaline B. Harrison
Affiliation:
Department of Neurology, University of Virginia, Virginia
Scott A. Wylie
Affiliation:
Department of Neurology, Vanderbilt University Medical Center, Tennessee
*
Correspondence and reprint requests to: Nelleke C. van Wouwe, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232. Email: nelleke.van.wouwe@vanderbilt.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Huntington’s disease (HD) is a neurodegenerative disorder that produces a bias toward risky, reward-driven decisions in situations where the outcomes of decisions are uncertain and must be discovered. However, it is unclear whether HD patients show similar biases in decision-making when learning demands are minimized and prospective risks and outcomes are known explicitly. We investigated how risk decision-making strategies and adjustments are altered in HD patients when reward contingencies are explicit. Methods: HD (N=18) and healthy control (HC; N=17) participants completed a risk-taking task in which they made a series of independent choices between a low-risk/low reward and high-risk/high reward risk options. Results: Computational modeling showed that compared to HC, who showed a clear preference for low-risk compared to high-risk decisions, the HD group valued high-risks more than low-risk decisions, especially when high-risks were rewarded. The strategy analysis indicated that when high-risk options were rewarded, HC adopted a conservative risk strategy on the next trial by preferring the low-risk option (i.e., they counted their blessings and then played the surer bet). In contrast, following a rewarded high-risk choice, HD patients showed a clear preference for repeating the high-risk choice. Conclusions: These results indicate a pattern of high-risk/high-reward decision bias in HD that persists when outcomes and risks are certain. The allure of high-risk/high-reward decisions in situations of risk certainty and uncertainty expands our insight into the dynamic decision-making deficits that create considerable clinical burden in HD. (JINS, 2016, 22, 426–435)

Keywords

basal ganglianeurodegenerative diseasecognitionexecutive testdecision-makingreward processing
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Issue 4 , April 2016 , pp. 426 - 435
Copyright
Copyright © The International Neuropsychological Society 2015 

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

Abada, Y.S., Nguyen, H.P., Ellenbroek, B., & Schreiber, R. (2013). Reversal learning and associative memory impairments in a BACHD rat model for Huntington disease. PLoS One, 8(11), e71633. doi:10.1371/journal.pone.0071633 CrossRefGoogle Scholar
Albin, R.L., Young, A.B., & Penney, J.B. (1989). The functional anatomy of basal ganglia disorders. Trends in Neuroscience, 12(10), 366–375.CrossRefGoogle ScholarPubMed
Aylward, E.H., Nopoulos, P.C., Ross, C.A., Langbehn, D.R., Pierson, R.K., & Mills, J.A., ... Coordinators of Huntington Study, Group (2011). Longitudinal change in regional brain volumes in prodromal Huntington disease. Journal of Neurology Neurosurgery & Psychiatry, 82(4), 405–410. doi:10.1136/jnnp.2010.208264 CrossRefGoogle ScholarPubMed
Barraclough, D.J., Conroy, M.L., & Lee, D. (2004). Prefrontal cortex and decision making in a mixed-strategy game. Nature Neuroscience, 7(4), 404–410. doi:10.1038/nn1209 CrossRefGoogle Scholar
Brandt, J., Inscore, A.B., Ward, J., Shpritz, B., Rosenblatt, A., Margolis, R.L., & Ross, C.A. (2008). Neuropsychological deficits in Huntington’s disease gene carriers and correlates of early “conversion”. Journal of Neuropsychiatry & Clinical Neurosciences, 20(4), 466–472. doi:10.1176/appi.neuropsych.20.4.466 CrossRefGoogle ScholarPubMed
Brooks, S.P., Janghra, N., Higgs, G.V., Bayram-Weston, Z., Heuer, A., Jones, L., & Dunnett, S.B. (2012). Selective cognitive impairment in the YAC128 Huntington’s disease mouse. Brain Research Bulletin, 88(2-3), 121–129. doi:10.1016/j.brainresbull.2011.05.010 CrossRefGoogle ScholarPubMed
Burnham, K.P., & Anderson, D.R. Model selection and Multimodel inference (2nd ed.). New York: Springer Verlag; 2002.Google Scholar
Busemeyer, J.R., & Stout, J.C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14(3), 253–262.CrossRefGoogle ScholarPubMed
Campbell, M.C., Stout, J.C., & Finn, P.R. (2004). Reduced autonomic responsiveness to gambling task losses in Huntington’s disease. Journal of the International Neuropsychological Society, 10(2), 239–245. doi:10.1017/S1355617704102105 CrossRefGoogle ScholarPubMed
Christensen, R. Log-linear models and logistic regression (2nd ed.). New York: Springer-Verlag; 1997.Google Scholar
Cohen, M., X., Heller, A.S., & Ranganath, C. (2005). Functional connectivity with anterior cingulate and orbitofrontal cortices during decision-making. Cognitive Brain Research, 23(1), 61–70. doi:10.1016/j.cogbrainres.2005.01.010 CrossRefGoogle ScholarPubMed
Cohen, M.X., & Ranganath, C. (2005). Behavioral and neural predictors of upcoming decisions. Cognitive, Affective & Behavioral Neuroscience, 5(2), 117–126.CrossRefGoogle ScholarPubMed
Cohen, M.X., & Ranganath, C. (2007). Reinforcement learning signals predict future decisions. Journal of Neuroscience, 27(2), 371–378. doi:10.1523/jneurosci.4421-06.2007 CrossRefGoogle ScholarPubMed
Cools, R., Frank, M.J., Gibbs, S.E., Miyakawa, A., Jagust, W., & D’Esposito, M. (2009). Striatal dopamine predicts outcome-specific reversal learning and its sensitivity to dopaminergic drug administration. Journal of Neuroscience, 29(5), 1538–1543. doi:10.1523/jneurosci.4467-08.2009 CrossRefGoogle ScholarPubMed
Duff, K., Paulsen, J.S., Beglinger, L.J., Langbehn, D.R., & Stout, J.C., Predict-HD Investigators of the Huntington Study Group. (2007). Psychiatric symptoms in Huntington’s disease before diagnosis: The predict-HD study. Biological Psychiatry, 62(12), 1341–1346. doi:10.1016/j.biopsych.2006.11.034 CrossRefGoogle ScholarPubMed
Duff, K., Paulsen, J.S., Beglinger, L.J., Langbehn, D.R., Wang, C., Stout, J.C., ... Predict-HD Investigators of the Huntington Study Group. (2010). “Frontal” behaviors before the diagnosis of Huntington’s disease and their relationship to markers of disease progression: Evidence of early lack of awareness. Journal of Neuropsychiatry & Clinical Neurosciences, 22(2), 196–207. doi:10.1176/appi.neuropsych.22.2.196 CrossRefGoogle ScholarPubMed
El Massioui, N., Ouary, S., Cheruel, F., Hantraye, P., & Brouillet, E. (2001). Perseverative behavior underlying attentional set-shifting deficits in rats chronically treated with the neurotoxin 3-nitropropionic acid. Experimental Neurology, 172(1), 172–181. doi:10.1006/exnr.2001.7766 CrossRefGoogle ScholarPubMed
Engelmann, J.B., & Tamir, D. (2009). Individual differences in risk preference predict neural responses during financial decision-making. Brain Research, 1290, 28–51. doi:10.1016/j.brainres.2009.06.078 CrossRefGoogle ScholarPubMed
Enzi, B., Edel, M.A., Lissek, S., Peters, S., Hoffmann, R., Nicolas, V., & Saft, C. (2012). Altered ventral striatal activation during reward and punishment processing in premanifest Huntington’s disease: A functional magnetic resonance study. Experimental Neurology, 235(1), 256–264. doi:10.1016/j.expneurol.2012.02.003 CrossRefGoogle ScholarPubMed
Euteneuer, F., Schaefer, F., Stuermer, R., Boucsein, W., Timmermann, L., Barbe, M.T., & Kalbe, E. (2009). Dissociation of decision-making under ambiguity and decision-making under risk in patients with Parkinson’s disease: A neuropsychological and psychophysiological study. Neuropsychologia, 47(13), 2882–2890. doi:10.1016/j.neuropsychologia.2009.06.014 CrossRefGoogle ScholarPubMed
Fink, K.D., Rossignol, J., Crane, A.T., Davis, K.K., Bavar, A.M., Dekorver, N.W., &Dunbar, G.L. (2012). Early cognitive dysfunction in the HD 51 CAG transgenic rat model of Huntington’s disease. Behavioral Neuroscience, 126(3), 479–487. doi:10.1037/a0028028 CrossRefGoogle Scholar
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.CrossRefGoogle ScholarPubMed
Frank, M., Seeberger, L., & O’R.eilly, R. (2004). By carrot or by stick: Cognitive reinforcement learning in parkinsonism. Science, 306(5703), 1940–1943.CrossRefGoogle ScholarPubMed
Hadzi, T.C., Hendricks, A.E., Latourelle, J.C., Lunetta, K.L., Cupples, L.A., Gillis, T., & Vonsattel, J.P. (2012). Assessment of cortical and striatal involvement in 523 Huntington disease brains. Neurology, 79(16), 1708–1715. doi:10.1212/WNL.0b013e31826e9a5d CrossRefGoogle ScholarPubMed
Kalkhoven, C., Sennef, C., Peeters, A., & van den Bos, R. (2014). Risk-taking and pathological gambling behavior in Huntington’s disease. Frontiers in Behavioral Neuroscience, 8, doi:10.3389/fnbeh.2014.00103 CrossRefGoogle ScholarPubMed
Kieburtz, K., Penney, J.B., Como, P., Ranen, N., Shoulson, I., Feigin, A., & Kremer, B. (1996). Unified Huntington’s disease rating scale: Reliability and consistency. Movement Disorders, 11(2), 136–142. doi:10.1002/mds.870110204 Google Scholar
Labudda, K., Brand, M., Mertens, M., Ollech, I., Markowitsch, H.J., & Woermann, F.G. (2010). Decision making under risk condition in patients with Parkinson’s disease: A behavioural and fMRI study. Behavioral Neurology, 23(3), 131–143. doi:10.3233/BEN-2010–0277 CrossRefGoogle ScholarPubMed
Lawrence, A.D., Sahakian, B.J., Rogers, R.D., Hodge, J.R., & Robbins, T.W. (1999). Discrimination, reversal, and shift learning in Huntington’s disease: Mechanisms of impaired response selection. Neuropsychologia, 37(12), 1359–1374.CrossRefGoogle ScholarPubMed
Liu, X., Hairston, J., Schrier, M., & Fan, J. (2011). Common and distinct networks underlying reward valence and processing stages: A meta-analysis of functional neuroimaging studies. Neuroscience & Biobehavioral Reviews, 35(5), 1219–1236. doi:10.1016/j.neubiorev.2010.12.012 CrossRefGoogle ScholarPubMed
Mink, J.W. (1996). The basal ganglia: Focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50(4), 381–425.CrossRefGoogle ScholarPubMed
Mink, J.W., & Thach, W.T. (1993). Basal ganglia intrinsic circuits and their role in behavior. Currernt Opinion in Neurobiology, 3(6), 950–957.CrossRefGoogle ScholarPubMed
Montague, P.R., Hyman, S.E., & Cohen, J.D. (2004). Computational roles for dopamine in behavioural control. Nature, 431(7010), 760–767. doi:10.1038/nature03015 CrossRefGoogle ScholarPubMed
Paulsen, J.S. (2009). Functional imaging in Huntington’s disease. Experimental Neurology, 216(2), 272–277. doi:10.1016/j.expneurol.2008.12.015 CrossRefGoogle ScholarPubMed
Rosas, H.D., Koroshetz, W.J., Chen, Y.I., Skeuse, C., Vangel, M., Cudkowicz, M.E., & Goldstein, J.M. (2003). Evidence for more widespread cerebral pathology in early HD: An MRI-based morphometric analysis. Neurology, 60(10), 1615–1620.CrossRefGoogle ScholarPubMed
Rosenblatt, A. (2007). Neuropsychiatry of Huntington’s disease. Dialogues in Clinical Neuroscience, 9(2), 191–197.CrossRefGoogle ScholarPubMed
Rutledge, R.B., Lazzaro, S.C., Lau, B., Myers, C.E., Gluck, M.A., & Glimcher, P.W. (2009). Dopaminergic drugs modulate learning rates and perseveration in Parkinson’s patients in a dynamic foraging task. Journal of Neuroscience, 29(48), 15104–15114. doi:10.1523/JNEUROSCI.3524-09.2009 CrossRefGoogle Scholar
Sanchez-Castaneda, C., Cherubini, A., Elifani, F., Peran, P., Orobello, S., Capelli, G.,& Squitieri, F. (2013). Seeking Huntington disease biomarkers by multimodal, cross-sectional basal ganglia imaging. Human Brain Mapping, 34(7), 1625–1635. doi:10.1002/hbm.22019 CrossRefGoogle ScholarPubMed
Schiebener, J., Zamarian, L., Delazer, M., & Brand, M. (2011). Executive functions, categorization of probabilities, and learning from feedback: What does really matter for decision making under explicit risk conditions? Journal of Clinical & Experimental Neuropsychology, 33(9), 1025–1039. doi:10.1080/13803395.2011.595702 CrossRefGoogle ScholarPubMed
Schroll, H., Beste, C., & Hamker, F.H. (2015). Combined lesions of direct and indirect basal ganglia pathways but not changes in dopamine levels explain learning deficits in patients with Huntington’s disease. European Journal of Neuroscience, 41(9), 1227–1244. doi:10.1111/ejn.12868 CrossRefGoogle Scholar
Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36(2), 241–263.CrossRefGoogle ScholarPubMed
Schwab, L.C., Garas, S.N., Drouin-Ouellet, J., Mason, S.L., Stott, S.R., & Barker, R.A. (2015). Dopamine and Huntington’s disease. Expert Review of Neurotherapeutics, 15(4), 445–458. doi:10.1586/14737175.2015.1025383 CrossRefGoogle ScholarPubMed
Stout, J.C., Rodawalt, W.C., & Siemers, E.R. (2001). Risky decision making in Huntington’s disease. Journal of the International Neuropsychological Society, 7(1), 92–101.CrossRefGoogle ScholarPubMed
Sutton, R.S., & Barto, A.G. Reinforcement learning: An introduction. Cambridge, MA: MIT Press; 1998.Google Scholar
Thiruvady, D.R., Georgiou-Karistianis, N., Egan, G.F., Ray, S., Sritharan, A., Farrow, M., & Cunnington, R. (2007). Functional connectivity of the prefrontal cortex in Huntington’s disease. Journal of Neurology, Neurosurgery, & Psychiatry, 78(2), 127–133. doi:10.1136/jnnp.2006.098368 Google Scholar
Treadway, M.T., & Zald, D.H. (2013). Parsing anhedonia: Translational models of reward-processing deficits in psychopathology. Current Directions in Psychological Science, 22(3), 244–249. doi:10.1177/0963721412474460 CrossRefGoogle ScholarPubMed
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458.CrossRefGoogle ScholarPubMed
van den Bogaard, S.J., Dumas, E.M., Acharya, T.P., Johnson, H., Langbehn, D.R., Scahill, R.I., ... Track-HD Investigator Group. (2011). Early atrophy of pallidum and accumbens nucleus in Huntington’s disease. Journal of Neurology, 258(3), 412–420. doi:10.1007/s00415-010-5768-0 CrossRefGoogle ScholarPubMed
Van Raamsdonk, J.M., Pearson, J., Slow, E.J., Hossain, S.M., Leavitt, B.R., & Hayden, M.R. (2005). Cognitive dysfunction precedes neuropathology and motor abnormalities in the YAC128 mouse model of Huntington’s disease. Journal of Neuroscience, 25(16), 4169–4180. doi:10.1523/JNEUROSCI.0590-05.2005 CrossRefGoogle ScholarPubMed
Vonsattel, J.P., & DiFiglia, M. (1998). Huntington disease. Journal of Neuropathology and Experimental Neurology, 57(5), 369–384.CrossRefGoogle ScholarPubMed
Vonsattel, J.P., Myers, R.H., Stevens, T.J., Ferrante, R.J., Bird, E.D., & Richardson, E.P. Jr. (1985). Neuropathological classification of Huntington’s disease. Journal of Neuropathology & Experimental Neurology, 44(6), 559–577.CrossRefGoogle ScholarPubMed
Whitton, A.E., Treadway, M.T., & Pizzagalli, D.A. (2015). Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. Current Opinion in Psychiatry, 28(1), 7–12. doi:10.1097/YCO.0000000000000122 CrossRefGoogle Scholar