Brain Topography

, Volume 30, Issue 1, pp 149–159 | Cite as

Temporal Characteristics of EEG Microstates Mediate Trial-by-Trial Risk Taking

  • Andreas Pedroni
  • Lorena R. R. Gianotti
  • Thomas Koenig
  • Dietrich Lehmann
  • Pascal Faber
  • Daria Knoch
Original Paper


People seem to have difficulties when perceiving events whose outcome has no influence on the outcome of future events. This illusion that patterns exist where there are none may lead to adverse consequences, such as escalating losses in financial trading or gambling debt. Despite the enormous social consequences of these cognitive biases, however, their neural underpinnings are poorly understood. Attempts to investigate them have so far relied on evoked neural activity, whereas spontaneous brain activity has been treated as noise to be averaged out. Here, we focus on the spontaneous electroencephalographic (EEG) activity during inter-trial-intervals (ITI) in a sequential risky decision-making task. Using multilevel mediation analyses, our results show that the percentage of time covered by two EEG microstates (i.e., functional brain-states of coherent activity) mediate the influence of outcomes of prior decisions on subsequent risk taking on a trial-by-trial basis. The devised multilevel mediation analysis of the temporal characteristics of EEG microstates during ITI provides a new window into the neurobiology of decision making by bringing the spontaneous brain activity to the forefront of the analysis.


Risk taking EEG Microstates Temporal characteristics 



This work was supported by grants from the Swiss National Science Foundation to Daria Knoch (PP00P1-123381) and the Mens Sana Foundation to Daria Knoch.

Compliance with Ethical Standards

Conflict of interest

The authors declared that they had no conflict of interest with respect to their authorship or the publication of this article.


  1. Aklin WM, Lejuez CW, Zvolensky MJ, Kahler CW, Gwadz M (2005) Evaluation of behavioral measures of risk taking propensity with inner city adolescents. Behav Res Ther 43:215–228CrossRefPubMedGoogle Scholar
  2. Albert NB, Robertson EM, Miall RC (2009) The resting human brain and motor learning. Curr Biol 19:1023–1027CrossRefPubMedPubMedCentralGoogle Scholar
  3. Aron AR (2008) Progress in executive-function research: from tasks to functions to regions to networks. Curr Dir Psychol Sci 17:124–129CrossRefGoogle Scholar
  4. Britz J, Michel CM (2011) State-dependent visual processing. Front Psychol 2:370CrossRefPubMedPubMedCentralGoogle Scholar
  5. Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170CrossRefPubMedGoogle Scholar
  6. Britz J, Díaz Hernàndez L, Ro T, Michel C (2014) EEG-microstate dependent emergence of perceptual awareness. Front Behav Neurosci 8:163CrossRefPubMedPubMedCentralGoogle Scholar
  7. Buchel C, Brassen S, Yacubian J, Kalisch R, Sommer T (2011) Ventral striatal signal changes represent missed opportunities and predict future choice. Neuroimage 57:1124–1130CrossRefPubMedGoogle Scholar
  8. Chau AW, Phillips JG (1995) Effects of perceived control upon wagering and attributions in computer blackjack. J Gen Psychol 122:253–269CrossRefGoogle Scholar
  9. Cohen M, Ranganath C (2005) Behavioral and neural predictors of upcoming decisions. Cogn Affect Behav Ne 5:117–126CrossRefGoogle Scholar
  10. Coste CP, Sadaghiani S, Friston KJ, Kleinschmidt A (2011) Ongoing brain activity fluctuations directly account for intertrial and indirectly for intersubject variability in Stroop task performance. Cereb Cortex 21:2612–2619CrossRefPubMedGoogle Scholar
  11. Croson R, Sundali J (2005) The Gambler’s fallacy and the hot hand: empirical data from casinos. J Risk Uncertain 30:195–209CrossRefGoogle Scholar
  12. Cummins LF, Nadorff MR, Kelly AE (2009) Winning and positive affect can lead to reckless gambling. Psychol Addict Behav 23:287–294CrossRefPubMedGoogle Scholar
  13. Curtis CE, Lee D (2010) Beyond working memory: the role of persistent activity in decision making. Trends Cogn Sci 14:216–222CrossRefPubMedPubMedCentralGoogle Scholar
  14. Cvetkovich G, Grote B, Bjorseth A, Sarkissian J (1975) Psychology of adolescents use of contraceptives. J Sex Res 11:256–270CrossRefGoogle Scholar
  15. Davidson RJ, Ekman P, Saron CD, Senulis JA, Friesen WV (1990) Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology I. J Pers Soc Psychol 58:330–341CrossRefPubMedGoogle Scholar
  16. Destexhe A, Contreras D (2006) Neuronal computations with stochastic network states. Science 314:85–90CrossRefPubMedGoogle Scholar
  17. Diamond A (2013) Executive functions. Annu Rev Psychol 64:135–168CrossRefPubMedGoogle Scholar
  18. Gianotti LR, Knoch D, Faber PL, Lehmann D, Pascual-Marqui RD, Diezi C, Schoch C, Eisenegger C, Fehr E (2009) Tonic activity level in the right prefrontal cortex predicts individuals’ risk taking. Psychol Sci 20:33–38CrossRefPubMedGoogle Scholar
  19. Gianotti LRR, Figner B, Ebstein RP, Knoch D (2012) Why some people discount more than others: baseline activation in the dorsal PFC mediates the link between COMT genotype and impatient choice. Front Neurosci 6:54CrossRefPubMedPubMedCentralGoogle Scholar
  20. Guidotti R, Del Gratta C, Baldassarre A, Romani GL, Corbetta M (2015) Visual learning induces changes in resting-state fMRI multivariate pattern of information. J Neurosci 35:9786–9798CrossRefPubMedGoogle Scholar
  21. Heatherton TF, Wagner DD (2011) Cognitive neuroscience of self-regulation failure. Trends Cogn Sci 15:132–139CrossRefPubMedPubMedCentralGoogle Scholar
  22. Hoffrage U, Weber A, Hertwig R, Chase VM (2003) How to keep children safe in traffic: find the daredevils early. J Exp Psychol Appl 9:249–260CrossRefPubMedGoogle Scholar
  23. Huettel SA, Mack PB, McCarthy G (2002) Perceiving patterns in random series: dynamic processing of sequence in prefrontal cortex. Nat Neurosci 5:485–490PubMedGoogle Scholar
  24. Huettel SA, Song AW, McCarthy G (2005) Decisions under uncertainty: probabilistic context influences activation of prefrontal and parietal cortices. J Neurosci 25:3304–3311CrossRefPubMedGoogle Scholar
  25. Kenny DA, Korchmaros JD, Bolger N (2003) Lower level mediation in multilevel models. Psychol Methods 8:115–128CrossRefPubMedGoogle Scholar
  26. Khanna A, Pascual-Leone A, Michel CM, Farzan F (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav R 49:105–113CrossRefGoogle Scholar
  27. Knoch D, Gianotti LR, Baumgartner T, Fehr E (2010) A neural marker of costly punishment behavior. Psychol Sci 21:337–342CrossRefPubMedGoogle Scholar
  28. Knutson B, Huettel SA (2015) The risk matrix. Curr Opin Behav Sci 5:141–146CrossRefGoogle Scholar
  29. Koenig T, Lehmann D, Merlo MC, Kochi K, Hell D, Koukkou M (1999) A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest. Eur Arch Psy Clin N 249:205–211CrossRefGoogle Scholar
  30. Kolling N, Wittmann M, Rushworth MF (2014) Multiple neural mechanisms of decision making and their competition under changing risk pressure. Neuron 81:1190–1202CrossRefPubMedPubMedCentralGoogle Scholar
  31. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT (2000) Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 10:120–131CrossRefPubMedGoogle Scholar
  32. Lehmann D (1971) Multichannel topography of human alpha EEG fields. Electroen Clin Neuro 31:439–449CrossRefGoogle Scholar
  33. Lehmann D, Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroen Clin Neuro 48:609–621CrossRefGoogle Scholar
  34. Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroen Clin Neuro 67:271–288CrossRefGoogle Scholar
  35. Lehmann D, Strik W, Henggeler B, Koenig T, Koukkou M (1998) Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts. Int J Psychophysiol 29:1–11CrossRefPubMedGoogle Scholar
  36. Lejuez CW, Read JP, Kahler CW, Richards JB, Ramsey SE, Stuart GL, Strong DR, Brown RA (2002) Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). J Exp Psychol Appl 8:75–84CrossRefPubMedGoogle Scholar
  37. Lejuez CW, Aklin WM, Zvolensky MJ, Pedulla CM (2003) Evaluation of the Balloon Analogue Risk Task (BART) as a predictor of adolescent real-world risk-taking behaviours. J Adolesc 26:475–479CrossRefPubMedGoogle Scholar
  38. Leopard A (1978) Risk preference in consecutive gambling. J Exp Psychol Human 4:521–528CrossRefGoogle Scholar
  39. Lewis CM, Baldassarre A, Committeri G, Romani GL, Corbetta M (2009) Learning sculpts the spontaneous activity of the resting human brain. Proc Natl Acad Sci USA 106:17558–17563CrossRefPubMedPubMedCentralGoogle Scholar
  40. Lorch RF, Myers JL (1990) Regression analyses of repeated measures data in cognitive research. J Exp Psychol Learn 16:149–157CrossRefGoogle Scholar
  41. MacKinnon DP, Fairchild AJ, Fritz MS (2007) Mediation analysis. Annu Rev Psychol 58:593CrossRefPubMedPubMedCentralGoogle Scholar
  42. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B (2001) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos T Roy Soc B 356:1293–1322CrossRefGoogle Scholar
  43. Michel CM, Murray MM (2012) Towards the utilization of EEG as a brain imaging tool. Neuroimage 61:371–385CrossRefPubMedGoogle Scholar
  44. Michel CM, Brandeis D, Koenig T (2009) Electrical neuroimaging in the time domain. In: Michel CM, Koenig T, Brandeis D, Gianotti LRR, Wackermann J (eds) Electrical neuroimaging. Cambridge University Press, Cambridge, pp 111–143CrossRefGoogle Scholar
  45. Mobascher A, Brinkmeyer J, Warbrick T, Musso F, Wittsack HJ, Saleh A, Schnitzler A, Winterer G (2009a) Laser-evoked potential P2 single-trial amplitudes covary with the fMRI BOLD response in the medial pain system and interconnected subcortical structures. Neuroimage 45:917–926CrossRefPubMedGoogle Scholar
  46. Mobascher A, Brinkmeyer J, Warbrick T, Musso F, Wittsack HJ, Stoermer R, Saleh A, Schnitzler A, Winterer G (2009b) Fluctuations in electrodermal activity reveal variations in single trial brain responses to painful laser stimuli: a fMRI/EEG study. Neuroimage 44:1081–1092CrossRefPubMedGoogle Scholar
  47. Müller TJ, Koenig T, Wackermann J, Kalus P, Fallgatter A, Strik W, Lehmann D (2005) Subsecond changes of global brain state in illusory multistable motion perception. J Neural Trans 112:565–576CrossRefGoogle Scholar
  48. Murray MM, Brunet D, Michel CM (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr 20:249–264CrossRefPubMedGoogle Scholar
  49. Musso F, Brinkmeyer J, Mobascher A, Warbrick T, Winterer G (2010) Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. Neuroimage 52:1149–1161CrossRefPubMedGoogle Scholar
  50. Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25CrossRefPubMedGoogle Scholar
  51. Nuwer MR, Comi G, Emerson R, Fuglsang-Frederiksen A, Guerit JM, Hinrichs H, Ikeda A, Luccas FJ, Rappelsburger P (1998) IFCN standards for digital recording of clinical EEG. Electroen Clin Neuro 106:259–261CrossRefGoogle Scholar
  52. Odean T (1998) Are investors reluctant to realize their losses? J Finance 53:1775–1798CrossRefGoogle Scholar
  53. Oskarsson AT, Van Boven L, McClelland GH, Hastie R (2009) What’s Next? Judging sequences of binary events. Psychol Bull 35:262–285CrossRefGoogle Scholar
  54. Pascual-Marqui RD (2002) Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Method Find Exp Clin Pharmacol 24(Suppl D):5–12Google Scholar
  55. Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE T Bio Med Eng 42:658–665CrossRefGoogle Scholar
  56. 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 19:1439–1448CrossRefPubMedGoogle Scholar
  57. Pleskac TJ (2008) Decision making and learning while taking sequential risks. J Exp Psychol Learn Mem Cogn 34:167–185CrossRefPubMedGoogle Scholar
  58. Raichle ME (2015) The restless brain: how intrinsic activity organizes brain function. Philos T Roy Soc B 370:20140172CrossRefGoogle Scholar
  59. Rullmann M, Anwander A, Dannhauer M, Warfield S, Duffy F, Wolters C (2009) EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model. Neuroimage 44:399–410CrossRefPubMedGoogle Scholar
  60. Sadaghiani S, Scheeringa R, Lehongre K, Morillon B, Giraud A-L, Kleinschmidt A (2010) Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J Neurosci 30:10243–10250CrossRefPubMedGoogle Scholar
  61. Sadaghiani S, Poline JB, Kleinschmidt A, D’Esposito M (2015) Ongoing dynamics in large-scale functional connectivity predict perception. Proc Natl Acad Sci USA 112:8463–8468CrossRefPubMedPubMedCentralGoogle Scholar
  62. Slovic P (1966) Risk-taking in children: age and sex differences. Child Dev 37:169–176CrossRefGoogle Scholar
  63. Smith BW, Mitchell DG, Hardin MG, Jazbec S, Fridberg D, Blair RJ, Ernst M (2009) Neural substrates of reward magnitude, probability, and risk during a wheel of fortune decision-making task. Neuroimage 44:600–609CrossRefPubMedGoogle Scholar
  64. Staw BM (1976) Knee-deep in the big muddy: a study of escalating commitment to a chosen course of action. Organ Behav Hum Perf 16:27–44CrossRefGoogle Scholar
  65. Studer B, Apergis-Schoute AM, Robbins TW, Clark L (2012) What are the odds? The neural correlates of active choice during gambling. Front Neurosci 6:46CrossRefPubMedPubMedCentralGoogle Scholar
  66. Studer B, Pedroni A, Rieskamp J (2013). Predicting risk-taking behavior from prefrontal resting-state activity and personality. PloS One 8:e76861CrossRefPubMedPubMedCentralGoogle Scholar
  67. Thaler RH, Johnson EJ (1990) Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice. Manage Sci 36:643–660CrossRefGoogle Scholar
  68. Tung KC, Uh J, Mao D, Xu F, Xiao G, Lu H (2013) Alterations in resting functional connectivity due to recent motor task. Neuroimage 78:316–324CrossRefPubMedPubMedCentralGoogle Scholar
  69. van Leijenhorst L, Crone EA, Bunge SA (2006) Neural correlates of developmental differences in risk estimation and feedback processing. Neuropsychologia 44:2158–2170CrossRefPubMedGoogle Scholar
  70. Van De Ville D, Britz J, Michel CM (2010) EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci USA 107:18179–18184CrossRefPubMedGoogle Scholar
  71. Wager TD, Waugh CE, Lindquist M, Noll DC, Fredrickson BL, Taylor SF (2009) Brain mediators of cardiovascular responses to social threat, Part I: reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity. Neuroimage 47:821–835CrossRefPubMedPubMedCentralGoogle Scholar
  72. Waites AB, Stanislavsky A, Abbott DF, Jackson GD (2005) Effect of prior cognitive state on resting state networks measured with functional connectivity. Hum Brain Mapp 24:59–68CrossRefPubMedGoogle Scholar
  73. Xue G, Lu Z, Levin IP, Bechara A (2010) The impact of prior risk experiences on subsequent risky decision-making: the role of the insula. Neuroimage 50:709–716CrossRefPubMedPubMedCentralGoogle Scholar
  74. Xue G, Lu Z, Levin IP, Bechara A (2011) An fMRI study of risk taking following wins and losses: implications for the gambler’s fallacy. Hum Brain Mapp 32:271–281CrossRefPubMedPubMedCentralGoogle Scholar
  75. Yuan H, Zotev V, Phillips R, Drevets WC, Bodurka J (2012) Spatiotemporal dynamics of the brain at rest: exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. Neuroimage 60:2062–2072CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Psychology, Methods of Plasticity ResearchUniversity of ZurichZurichSwitzerland
  2. 2.Department of Social Psychology and Social Neuroscience, Institute of PsychologyUniversity of BernBernSwitzerland
  3. 3.Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind ResearchUniversity Hospital of PsychiatryZurichSwitzerland
  4. 4.Translational Research Center, University Hospital of PsychiatryUniversity of BernBernSwitzerland

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