Risk seeking for losses modulates the functional connectivity of the default mode and left frontoparietal networks in young males

  • Yacila I. Deza Araujo
  • Stephan Nebe
  • Philipp T. Neukam
  • Shakoor Pooseh
  • Miriam Sebold
  • Maria Garbusow
  • Andreas Heinz
  • Michael N. Smolka


Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters.


Value-based decision making Intrinsic connectivity networks Probabilistic discounting for losses Default mode network Frontoparietal network 

Supplementary material

13415_2018_586_MOESM1_ESM.docx (83 kb)
ESM 1 (DOCX 83 kb)


  1. Ainslie, G. (1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82, 463–496.CrossRefPubMedGoogle Scholar
  2. Aminoff, E. M., Kveraga, K., & Bar, M. (2013). The role of the parahippocampal cortex in cognition. Trends in Cognitive Sciences, 17, 379–390.  https://doi.org/10.1016/j.tics.2013.06.009 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Amlung, M., Vedelago, L., Acker, J., Balodis, I., & MacKillop, J. (2017). Steep delay discounting and addictive behavior: A meta-analysis of continuous associations. Addiction, 112, 51–62.  https://doi.org/10.1111/add.13535 CrossRefPubMedGoogle Scholar
  4. Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65, 550–562.  https://doi.org/10.1016/j.neuron.2010.02.005 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Baik, J. H. (2013). Dopamine signalling in reward-related behaviours. Frontiers in Neural Circuits, 7, 152.  https://doi.org/10.3389/fncir.2013.00152 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Ball, I. L., Farnill, D., & Wangeman, J. F. (1984). Sex and age-differences in sensation seeking—Some national comparisons. British Journal of Psychology, 75, 257–265.CrossRefGoogle Scholar
  7. Barkley-Levenson, E., & Galvan, A. (2014). Neural representation of expected value in the adolescent brain. Proceedings of the National Academy of Sciences, 111, 1646–1651.  https://doi.org/10.1073/pnas.1319762111 CrossRefGoogle Scholar
  8. 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.  https://doi.org/10.1016/j.neuroimage.2013.02.063 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Beckmann, C. F., Mackay, C. E., Nicola, F., & Smith, S. M. (2009). Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. NeuroImage, 47(Suppl. 1), S148.  https://doi.org/10.1016/S1053-8119(09)71511-3 CrossRefGoogle Scholar
  10. Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23, 137–152. Retrieved from www.ncbi.nlm.nih.gov/pubmed/14964560 CrossRefPubMedGoogle Scholar
  11. Bernhardt, N., Nebe, S., Pooseh, S., Sebold, M., Sommer, C., Birkenstock, J., … Smolka, M. N. (2017). Impulsive decision making in young adult social drinkers and detoxified alcohol-dependent patients: A cross-sectional and longitudinal study. Alcoholism: Clinical and Experimental Research, 41, 1794–1807.  https://doi.org/10.1111/acer.13481 CrossRefGoogle Scholar
  12. Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance Medicine, 34, 537–541.CrossRefGoogle Scholar
  13. Blum, R. W., & Nelson-Mmari, K. (2004). The health of young people in a global context. Journal of Adolescent Health, 35, 402–418.  https://doi.org/10.1016/j.jadohealth.2003.10.007 CrossRefPubMedGoogle Scholar
  14. Braams, B. R., van Duijvenvoorde, A. C., Peper, J. S., & Crone, E. A. (2015). Longitudinal changes in adolescent risk-taking: A comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior. Journal of Neuroscience, 35, 7226–7238.  https://doi.org/10.1523/JNEUROSCI.4764-14.2015 CrossRefPubMedGoogle Scholar
  15. Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.  https://doi.org/10.1196/annals.1440.011 CrossRefPubMedGoogle Scholar
  16. Burnett, S., Bault, N., Coricelli, G., & Blakemore, S. J. (2010). Adolescents’ heightened risk-seeking in a probabilistic gambling task. Cognitive Development, 25, 183–196.  https://doi.org/10.1016/j.cogdev.2009.11.003 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14, 140–151.  https://doi.org/10.1002/hbm.1048 CrossRefPubMedGoogle Scholar
  18. Christopoulos, G. I., Tobler, P. N., Bossaerts, P., Dolan, R. J., & Schultz, W. (2009). Neural correlates of value, risk, and risk aversion contributing to decision making under risk. Journal of Neuroscience, 29, 12574–12583.  https://doi.org/10.1523/jneurosci.2614-09.2009 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Cole, D. M., Beckmann, C. F., Oei, N. Y., Both, S., van Gerven, J. M., & Rombouts, S. A. (2013). Differential and distributed effects of dopamine neuromodulations on resting-state network connectivity. NeuroImage, 78, 59–67.  https://doi.org/10.1016/j.neuroimage.2013.04.034 CrossRefPubMedGoogle Scholar
  20. Cole, D. M., Smith, S. M., & Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 8.  https://doi.org/10.3389/fnsys.2010.00008 PubMedPubMedCentralGoogle Scholar
  21. Cox, C. L., Gotimer, K., Roy, A. K., Castellanos, F. X., Milham, M. P., & Kelly, C. (2010). Your resting brain cares about your risky behavior. PLoS ONE, 5, e12296.  https://doi.org/10.1371/journal.pone.0012296 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Crockford, D. N., Goodyear, B., Edwards, J., Quickfall, J., & el-Guebaly, N. (2005). Cue-induced brain activity in pathological gamblers. Biological Psychiatry, 58, 787–795.  https://doi.org/10.1016/j.biopsych.2005.04.037 CrossRefPubMedGoogle Scholar
  23. Cservenka, A., Casimo, K., Fair, D. A., & Nagel, B. J. (2014). Resting state functional connectivity of the nucleus accumbens in youth with a family history of alcoholism. Psychiatry Research, 221, 210–219.  https://doi.org/10.1016/j.pscychresns.2013.12.004 CrossRefPubMedGoogle Scholar
  24. Davis, F. C., Knodt, A. R., Sporns, O., Lahey, B. B., Zald, D. H., Brigidi, B. D., & Hariri, A. R. (2013). Impulsivity and the modular organization of resting-state neural networks. Cerebral Cortex, 23, 1444–1452.  https://doi.org/10.1093/cercor/bhs126 CrossRefPubMedGoogle Scholar
  25. DeWitt, S. J., Aslan, S., & Filbey, F. M. (2014). Adolescent risk-taking and resting state functional connectivity. Psychiatry Research: Neuroimaging, 222, 157–164.  https://doi.org/10.1016/j.pscychresns.2014.03.009 CrossRefPubMedGoogle Scholar
  26. Dickman, S. J. (1990). Functional and dysfunctional impulsivity: Personality and cognitive correlates. Journal of Personality and Social Psychology, 58, 95–102.CrossRefPubMedGoogle Scholar
  27. Dixon, W. J. (1960). Simplified estimation from censored normal samples. Annals of Mathematical Statistics, 31, 385–391.  https://doi.org/10.1214/aoms/1177705900 CrossRefGoogle Scholar
  28. Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., … Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, 104, 11073–11078.  https://doi.org/10.1073/pnas.0704320104 CrossRefGoogle Scholar
  29. Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 113, 7900–7905.  https://doi.org/10.1073/pnas.1602413113 CrossRefGoogle Scholar
  30. Filippini, N., MacIntosh, B. J., Hough, M. G., Goodwin, G. M., Frisoni, G. B., Smith, S. M., … Mackay, C. E. (2009). Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proceedings of the National Academy of Sciences, 106, 7209–7214.  https://doi.org/10.1073/pnas.0811879106 CrossRefGoogle Scholar
  31. Franken, I. H., van Strien, J. W., Nijs, I., & Muris, P. (2008). Impulsivity is associated with behavioral decision-making deficits. Psychiatry Research, 158, 155–163.  https://doi.org/10.1016/j.psychres.2007.06.002 CrossRefPubMedGoogle Scholar
  32. Galvan, A., Hare, T., Voss, H., Glover, G., & Casey, B. J. (2007). Risk-taking and the adolescent brain: Who is at risk? Developmental Science, 10, F8–F14.  https://doi.org/10.1111/j.1467-7687.2006.00579.x CrossRefPubMedGoogle Scholar
  33. Green, L., Myerson, J., & Ostaszewski, P. (1999). Amount of reward has opposite effects on the discounting of delayed and probabilistic outcomes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 418–427.PubMedGoogle Scholar
  34. Grill-Spector, K., & Malach, R. (2004). The human visual cortex. Annual Review of Neuroscience, 27, 649–677.  https://doi.org/10.1146/annurev.neuro.27.070203.144220 CrossRefPubMedGoogle Scholar
  35. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62, 782–790.  https://doi.org/10.1016/j.neuroimage.2011.09.015 CrossRefPubMedGoogle Scholar
  36. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.  https://doi.org/10.2307/1914185 CrossRefGoogle Scholar
  37. Klaassens, B. L., Rombouts, S. A. R. B., Winkler, A. M., van Gorsel, H. C., van der Grond, J., & van Gerven, J. M. A. (2016). Time related effects on functional brain connectivity after serotonergic and cholinergic neuromodulation. Human Brain Mapping, 38, 308–325.  https://doi.org/10.1002/hbm.23362 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Klumpers, L. E., Cole, D. M., Khalili-Mahani, N., Soeter, R. P., Te Beek, E. T., Rombouts, S. A., & van Gerven, J. M. A. (2012). Manipulating brain connectivity with delta(9)-tetrahydrocannabinol: a pharmacological resting state FMRI study. NeuroImage, 63, 1701–1711.  https://doi.org/10.1016/j.neuroimage.2012.07.051 CrossRefPubMedGoogle Scholar
  39. Kong, X. Z., Zhen, Z., Li, X., Lu, H. H., Wang, R., Liu, L., … Liu, J. (2014). Individual differences in impulsivity predict head motion during magnetic resonance imaging. PLoS ONE, 9, e104989.  https://doi.org/10.1371/journal.pone.0104989 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., … Fox, P. T. (2011). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience, 23, 4022–4037.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Lieberman, M. D., & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: Re-balancing the scale. Social Cognitive and Affective Neuroscience, 4, 423–428.  https://doi.org/10.1093/scan/nsp052 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Little, R. J. A., & Smith, P. J. (1987). Editing and imputation for quantitative survey data. Journal of the American Statistical Association, 82, 58–68.  https://doi.org/10.2307/2289125 CrossRefGoogle Scholar
  43. Marco-Pallarés, J., Mohammadi, B., Samii, A., & Münte, T. F. (2010). Brain activations reflect individual discount rates in intertemporal choice. Brain Research, 1320, 123–129.  https://doi.org/10.1016/j.brainres.2010.01.025 CrossRefPubMedGoogle Scholar
  44. Mazur, J. E. (1988). Estimation of indifference points with an adjusting-delay procedure. Journal of the Experimental Analysis of Behavior, 49, 37–47.  https://doi.org/10.1901/jeab.1988.49-37 CrossRefPubMedPubMedCentralGoogle Scholar
  45. McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306, 503–507.  https://doi.org/10.1126/science.1100907 CrossRefPubMedGoogle Scholar
  46. Mennes, M., Kelly, C., Zuo, X. N., Di Martino, A., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. NeuroImage, 50, 1690–1701.  https://doi.org/10.1016/j.neuroimage.2010.01.002 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Mumford, J. A. (2017). A comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers? NeuroImage, 147, 658–668.  https://doi.org/10.1016/j.neuroimage.2016.12.058 CrossRefPubMedGoogle Scholar
  48. Mumford, J. A., Poline, J. B., & Poldrack, R. A. (2015). Orthogonalization of regressors in fMRI models. PLoS ONE, 10, e0126255.  https://doi.org/10.1371/journal.pone.0126255 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Murty, V. P., FeldmanHall, O., Hunter, L. E., Phelps, E. A., & Davachi, L. (2016). Episodic memories predict adaptive value-based decision-making. Journal of Experimental Psychology: General, 145, 548–558.  https://doi.org/10.1037/xge0000158 CrossRefGoogle Scholar
  50. Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15, 1–25.  https://doi.org/10.1002/hbm.1058 CrossRefPubMedGoogle Scholar
  51. Odum, A. L. (2011). Delay discounting: I’m a k, you’re a k. Journal of the Experimental Analysis of Behavior, 96, 427–439.  https://doi.org/10.1901/jeab.2011.96-423 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Peters, J., & Buchel, C. (2011). The neural mechanisms of inter-temporal decision-making: Understanding variability. Trends in Cognitive Sciences, 15, 227–239.  https://doi.org/10.1016/j.tics.2011.03.002 CrossRefPubMedGoogle Scholar
  53. Pooseh, S., Bernhardt, N., Guevara, A., Huys, Q. J., & Smolka, M. N. (2018). Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior. Behavior Research Methods, 50, 236–249.  https://doi.org/10.3758/s13428-017-0866-x CrossRefPubMedGoogle Scholar
  54. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59, 2142–2154.  https://doi.org/10.1016/j.neuroimage.2011.10.018 CrossRefPubMedGoogle Scholar
  55. Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008). Human insula activation reflects risk prediction errors as well as risk. Journal of Neuroscience, 28, 2745–2752.  https://doi.org/10.1523/JNEUROSCI.4286-07.2008 CrossRefPubMedGoogle Scholar
  56. Rachlin, H., Raineri, A., & Cross, D. (1991). Subjective probability and delay. Journal of the Experimental Analysis of Behavior, 55, 233–244.  https://doi.org/10.1901/jeab.1991.55-233 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Ray, K. L., McKay, D. R., Fox, P. M., Riedel, M. C., Uecker, A. M., Beckmann, C. F., … Laird, A. R. (2013). ICA model order selection of task co-activation networks. Frontiers in Neuroscience, 7, 237.  https://doi.org/10.3389/fnins.2013.00237 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Reineberg, A. E., Andrews-Hanna, J. R., Depue, B. E., Friedman, N. P., & Banich, M. T. (2015). Resting-state networks predict individual differences in common and specific aspects of executive function. NeuroImage, 104, 69–78.  https://doi.org/10.1016/j.neuroimage.2014.09.045 CrossRefPubMedGoogle Scholar
  59. Ripke, S., Hubner, T., Mennigen, E., Muller, K. U., Li, S. C., & Smolka, M. N. (2015). Common neural correlates of intertemporal choices and intelligence in adolescents. Journal of Cognitive Neuroscience, 27, 387–399.  https://doi.org/10.1162/jocn_a_00698 CrossRefPubMedGoogle Scholar
  60. Ripke, S., Hubner, T., Mennigen, E., Muller, K. U., Rodehacke, S., Schmidt, D., … Smolka, M. N. (2012). Reward processing and intertemporal decision making in adults and adolescents: The role of impulsivity and decision consistency. Brain Research, 1478, 36–47.  https://doi.org/10.1016/j.brainres.2012.08.034 CrossRefPubMedGoogle Scholar
  61. Romer, D. (2010). Adolescent risk taking, impulsivity, and brain development: Implications for prevention. Developmental Psychobiology, 52, 263–276.  https://doi.org/10.1002/dev.20442 PubMedPubMedCentralGoogle Scholar
  62. Rushworth, M. F., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011). Frontal cortex and reward-guided learning and decision-making. Neuron, 70, 1054–1069.  https://doi.org/10.1016/j.neuron.2011.05.014 CrossRefPubMedGoogle Scholar
  63. Schoenbaum, G., Takahashi, Y., Liu, T. L., & McDannald, M. A. (2011). Does the orbitofrontal cortex signal value? Annals of the New York Academy of Sciences, 1239, 87–99.  https://doi.org/10.1111/j.1749-6632.2011.06210.x CrossRefPubMedPubMedCentralGoogle Scholar
  64. Shannon, B. J., Raichle, M. E., Snyder, A. Z., Fair, D. A., Mills, K. L., Zhang, D., … Kiehl, K. A. (2011). Premotor functional connectivity predicts impulsivity in juvenile offenders. Proceedings of the National Academy of Sciences, 108, 11241–11245.  https://doi.org/10.1073/pnas.1108241108 CrossRefGoogle Scholar
  65. Shead, N. W., & Hodgins, D. C. (2009). Probability discounting of gains and losses: Implications for risk attitudes and impulsivity. Journal of the Experimental Analysis of Behavior, 92, 1–16.  https://doi.org/10.1901/jeab.2009.92-1 CrossRefPubMedPubMedCentralGoogle Scholar
  66. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106, 13040–13045.  https://doi.org/10.1073/pnas.0905267106 CrossRefGoogle Scholar
  67. Smith, S. M., Hyvarinen, A., Varoquaux, G., Miller, K. L., & Beckmann, C. F. (2014). Group-PCA for very large fMRI datasets. NeuroImage, 101, 738–749.  https://doi.org/10.1016/j.neuroimage.2014.07.051 CrossRefPubMedPubMedCentralGoogle Scholar
  68. Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44, 83–98.  https://doi.org/10.1016/j.neuroimage.2008.03.061 CrossRefPubMedGoogle Scholar
  69. Smoski, M. J., Lynch, T. R., Rosenthal, M. Z., Cheavens, J. S., Chapman, A. L., & Krishnan, R. R. (2008). Decision-making and risk aversion among depressive adults. Journal of Behavior Therapy and Experimental Psychiatry, 39, 567–576.  https://doi.org/10.1016/j.jbtep.2008.01.004 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106.  https://doi.org/10.1016/j.dr.2007.08.002 CrossRefPubMedPubMedCentralGoogle Scholar
  71. Story, G. W., Vlaev, I., Seymour, B., Darzi, A., & Dolan, R. J. (2014). Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective. Frontiers in Behavioral Neuroscience, 8, 76.  https://doi.org/10.3389/fnbeh.2014.00076 CrossRefPubMedPubMedCentralGoogle Scholar
  72. Szewczyk-Krolikowski, K., Menke, R. A., Rolinski, M., Duff, E., Salimi-Khorshidi, G., Filippini, N., … Mackay, C. E. (2014). Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neurology, 83, 208–214.  https://doi.org/10.1212/WNL.0000000000000592 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Tamura, M., Moriguchi, Y., Higuchi, S., Hida, A., Enomoto, M., Umezawa, J., & Mishima, K. (2012). Neural network development in late adolescents during observation of risk-taking action. PLoS ONE, 7, e39527.  https://doi.org/10.1371/journal.pone.0039527 CrossRefPubMedPubMedCentralGoogle Scholar
  74. Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315, 515–518.  https://doi.org/10.1126/science.1134239 CrossRefPubMedGoogle Scholar
  75. Turner, C., & McClure, R. (2003). Age and gender differences in risk-taking behaviour as an explanation for high incidence of motor vehicle crashes as a driver in young males. Injury Control and Safety Promotion, 10, 123–130.  https://doi.org/10.1076/icsp. CrossRefPubMedGoogle Scholar
  76. Vaidya, C. J., & Gordon, E. M. (2013). Phenotypic variability in resting-state functional connectivity: Current status. Brain Connections, 3, 99–120.  https://doi.org/10.1089/brain.2012.0110 CrossRefGoogle Scholar
  77. Weber, B. J., & Huettel, S. A. (2008). The neural substrates of probabilistic and intertemporal decision making. Brain Research, 1234, 104–115.  https://doi.org/10.1016/j.brainres.2008.07.105 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Wei, Z., Yang, N., Liu, Y., Yang, L., Wang, Y., Han, L., … Zhang, X. (2016). Resting-state functional connectivity between the dorsal anterior cingulate cortex and thalamus is associated with risky decision-making in nicotine addicts. Scientific Reports, 6, 21778.  https://doi.org/10.1038/srep21778 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Weiland, B. J., Heitzeg, M. M., Zald, D., Cummiford, C., Love, T., Zucker, R. A., & Zubieta, J. K. (2014). Relationship between impulsivity, prefrontal anticipatory activation, and striatal dopamine release during rewarded task performance. Psychiatry Research, 223, 244–252.  https://doi.org/10.1016/j.pscychresns.2014.05.015 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Weissman, D. G., Schriber, R. A., Fassbender, C., Atherton, O., Krafft, C., Robins, R. W., … Guyer, A. E. (2015). Earlier adolescent substance use onset predicts stronger connectivity between reward and cognitive control brain networks. Developmental Cognitive Neuroscience, 16, 121–129.  https://doi.org/10.1016/j.dcn.2015.07.002 CrossRefPubMedPubMedCentralGoogle Scholar
  81. Whelan, R., Conrod, P. J., Poline, J. B., Lourdusamy, A., Banaschewski, T., Barker, G. J., … Consortium, I. (2012). Adolescent impulsivity phenotypes characterized by distinct brain networks. Nature Neuroscience, 15, 920–925.  https://doi.org/10.1038/nn.3092 CrossRefPubMedGoogle Scholar
  82. Zermatten, A., Van der Linden, M., d’Acremont, M., Jermann, F., & Bechara, A. (2005). Impulsivity and decision making. Journal of Nervous and Mental Disease, 193, 647–650.CrossRefPubMedGoogle Scholar
  83. Zhang, S., & Li, C. S. R. (2012). Functional networks for cognitive control in a stop signal task: Independent component analysis. Human Brain Mapping, 33, 89–104.  https://doi.org/10.1002/hbm.21197 CrossRefPubMedGoogle Scholar
  84. Zhou, Y., Li, S., Dunn, J., Li, H., Qin, W., Zhu, M., … Jiang, T. (2014). The neural correlates of risk propensity in males and females using resting-state fMRI. Frontiers in Behavioral Neuroscience, 8, 2.  https://doi.org/10.3389/fnbeh.2014.00002 PubMedPubMedCentralGoogle Scholar
  85. Zhu, X., Cortes, C. R., Mathur, K., Tomasi, D., & Momenan, R. (2015). Model-free functional connectivity and impulsivity correlates of alcohol dependence: A resting-state study. Addiction Biology, 22, 206–217.  https://doi.org/10.1111/adb.12272 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2018

Authors and Affiliations

  • Yacila I. Deza Araujo
    • 1
  • Stephan Nebe
    • 1
  • Philipp T. Neukam
    • 1
  • Shakoor Pooseh
    • 1
  • Miriam Sebold
    • 2
  • Maria Garbusow
    • 2
  • Andreas Heinz
    • 2
  • Michael N. Smolka
    • 1
  1. 1.Department of Psychiatry and Neuroimaging CenterTechnische Universität DresdenDresdenGermany
  2. 2.Department of Psychiatry and PsychotherapyCharité–Universitätsmedizin Berlin, Campus Charité MitteBerlinGermany

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