Skip to main content
SpringerLink
Log in

Functional brain networks involved in decision-making under certain and uncertain conditions

  • Functional Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states.

Methods

In this cross-sectional study, 19 healthy subjects ages 18–35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB.

Results

The uncertain > certain comparison yielded three clusters—a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition.

Conclusion

The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Triebel K, Martin R, Griffith H, Marceaux J, Okonkwo O, Harrell L, Clark D, Brockington J, Bartolucci A, Marson DC (2009) Declining financial capacity in mild cognitive impairment. Neurology 73(12):928–934. https://doi.org/10.1212/WNL.0b013e3181b87971

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Matsuzawa D, Shirayama Y, Niitsu T, Hashimoto K, Iyo M (2015) Deficits in emotion based decision-making in schizophrenia; a new insight based on the Iowa Gambling Task. Prog Neuro-Psychopharmacol Biol Psychiatry 57:52–59. https://doi.org/10.1016/j.pnpbp.2014.10.007

    Article  Google Scholar 

  3. Zhang L, Dong Y, Ji Y, Zhu C, Yu F, Ma H, Chen X, Wang K (2015) Dissociation of decision making under ambiguity and decision making under risk: a neurocognitive endophenotype candidate for obsessive-compulsive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry 57:60–68. https://doi.org/10.1016/j.pnpbp.2014.09.005

    Article  Google Scholar 

  4. Ritschel F, Geisler D, King JA, Bernardoni F, Seidel M, Boehm I, Vettermann R, Biemann R, Roessner V, Smolka MN, Ehrlich S (2017) Neural correlates of altered feedback learning in women recovered from anorexia nervosa. Sci Rep 7(1):5421. https://doi.org/10.1038/s41598-017-04761-y

    Article  PubMed  PubMed Central  Google Scholar 

  5. Demanuele C, Kirsch P, Esslinger C, Zink M, Meyer-Lindenberg A, Durstewitz D (2015) Area-specific information processing in prefrontal cortex during a probabilistic inference task: a multivariate fMRI BOLD time series analysis. PLoS One 10(8):e0135424. https://doi.org/10.1371/journal.pone.0135424

    Article  PubMed  PubMed Central  Google Scholar 

  6. White TP, Engen NH, Sørensen S, Overgaard M, Shergill SS (2014) Uncertainty and confidence from the triple-network perspective: voxel-based meta-analyses. Brain Cogn 85:191–200. https://doi.org/10.1016/j.bandc.2013.12.002

    Article  PubMed  Google Scholar 

  7. Krug A, Cabanis M, Pyka M, Pauly K, Walter H, Landsberg M, Shah NJ, Winterer G, Wölwer W, Musso F, Müller BW, Wiedemann G, Herrlich J, Schnell K, Vogeley K, Schilbach L, Langohr K, Rapp A, Klingberg S, Kircher T (2014) Investigation of decision-making under uncertainty in healthy subjects: a multi-centric fMRI study. Behav Brain Res 261:89–96. https://doi.org/10.1016/j.bbr.2013.12.013

    Article  CAS  PubMed  Google Scholar 

  8. Philiastides MG, Biele G, Heekeren HR (2010) A mechanistic account of value computation in the human brain. Proc Natl Acad Sci 107(20):9430–9435. https://doi.org/10.1073/pnas.1001732107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hutchinson JB, Uncapher MR, Wagner AD (2015) Increased functional connectivity between dorsal posterior parietal and ventral occipitotemporal cortex during uncertain memory decisions. Neurobiol Learn Mem 117:71–83. https://doi.org/10.1016/j.nlm.2014.04.015

    Article  PubMed  Google Scholar 

  10. Daw ND, Doya K (2006) The computational neurobiology of learning and reward. Curr Opin Neurobiol 16(2):199–204. https://doi.org/10.1016/j.conb.2006.03.006

    Article  CAS  PubMed  Google Scholar 

  11. Garrido MI, Barnes GR, Kumaran D, Maguire EA, Dolan RJ (2015) Ventromedial prefrontal cortex drives hippocampal theta oscillations induced by mismatch computations. NeuroImage 120:362–370. https://doi.org/10.1016/j.neuroimage.2015.07.016

    Article  PubMed  PubMed Central  Google Scholar 

  12. Stalnaker TA, Calhoon GG, Ogawa M, Roesch MR, Schoenbaum G (2012) Reward prediction error signaling in posterior dorsomedial striatum is action specific. J Neurosci 32(30):10296–10305. https://doi.org/10.1523/JNEUROSCI.0832-12.2012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Samejima K, Ueda Y, Doya K, Kimura M (2005) Representation of action-specific reward values in the striatum. Science 310(5752):1337–1340. https://doi.org/10.1126/science.1115270

    Article  CAS  PubMed  Google Scholar 

  14. Bogacz R, Larsen T (2011) Integration of reinforcement learning and optimal decision-making theories of the basal ganglia. Neural Comput 23(4):817–851. https://doi.org/10.1162/NECO_a_00103

    Article  PubMed  Google Scholar 

  15. Nieuwenhuys R, Voogd J, van Huijzen F.M.A.A. C (2008) The human central nervous system: Springer Berlin Heidelberg

  16. Botvinick MM (2007) Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cogn Affect Behav Neurosci 7(4):356–366. https://doi.org/10.3758/CABN.7.4.356

    Article  PubMed  Google Scholar 

  17. Logothetis NK (2002) The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philos Trans R Soc Lond Ser B Biol Sci 357(1424):1003–1037. https://doi.org/10.1098/rstb.2002.1114

    Article  Google Scholar 

  18. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021

    Article  PubMed  Google Scholar 

  19. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825–841. https://doi.org/10.1006/nimg.2002.1132

    Article  PubMed  Google Scholar 

  20. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, de Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, de Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(Suppl 1):S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051

    Article  PubMed  Google Scholar 

  21. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3):1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003

    Article  PubMed  Google Scholar 

  22. Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One 8(7):e68910. https://doi.org/10.1371/journal.pone.0068910

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bach DR, Pryce CR, Seifritz E (2011) The experimental manipulation of uncertainty. In: Raber J (ed) Animal models of behavioral analysis. Humana Press, Totowa, pp 193–216. https://doi.org/10.1007/978-1-60761-883-6_8

    Chapter  Google Scholar 

  24. Reuman L, Jacoby RJ, Fabricant LE, Herring B, Abramowitz JS (2015) Uncertainty as an anxiety cue at high and low levels of threat. J Behav Ther Exp Psychiatry 47:111–119. https://doi.org/10.1016/j.jbtep.2014.12.002

    Article  PubMed  Google Scholar 

  25. Zhang H, Daw ND, Maloney LT (2015) Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions. Nat Neurosci 18(8):1152–1158. https://doi.org/10.1038/nn.4055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bengtsson SL, Haynes J-D, Sakai K, Buckley MJ, Passingham RE (2009) The representation of abstract task rules in the human prefrontal cortex. Cereb Cortex 19(8):1929–1936. https://doi.org/10.1093/cercor/bhn222

    Article  PubMed  Google Scholar 

  27. Kepecs A, Uchida N, Zariwala HA, Mainen ZF (2008) Neural correlates, computation and behavioural impact of decision confidence. Nature 455(7210):227–231. https://doi.org/10.1038/nature07200

    Article  CAS  PubMed  Google Scholar 

  28. Klein-Flügge MC, Barron HC, Brodersen KH et al (2013) Segregated encoding of reward-identity and stimulus-reward associations in human orbitofrontal cortex. J Neurosci 33(7):3202–3211. https://doi.org/10.1523/JNEUROSCI.2532-12.2013

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bhanji JP, Beer JS, Bunge SA (2010) Taking a gamble or playing by the rules: dissociable prefrontal systems implicated in probabilistic versus deterministic rule-based decisions. NeuroImage 49(2):1810–1819. https://doi.org/10.1016/j.neuroimage.2009.09.030

    Article  PubMed  Google Scholar 

  30. Klein TA, Ullsperger M, Jocham G (2017) Learning relative values in the striatum induces violations of normative decision making. Nat Commun 8:16033. https://doi.org/10.1038/ncomms16033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kiani R, Shadlen MN (2009) Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324(5928):759–764. https://doi.org/10.1126/science.1169405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Beck JM, Ma WJ, Kiani R, Hanks T, Churchland AK, Roitman J, Shadlen MN, Latham PE, Pouget A (2008) Probabilistic population codes for Bayesian decision making. Neuron 60(6):1142–1152. https://doi.org/10.1016/j.neuron.2008.09.021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Andersen RA, Cui H (2009) Intention, action planning, and decision making in parietal-frontal circuits. Neuron 63(5):568–583. https://doi.org/10.1016/j.neuron.2009.08.028

    Article  CAS  PubMed  Google Scholar 

  34. Zhang W, Schneider DM, Belova MA, Morrison SE, Paton JJ, Salzman CD (2013) Functional circuits and anatomical distribution of response properties in the primate amygdala. J Neurosci 33(2):722–733. https://doi.org/10.1523/JNEUROSCI.2970-12.2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Berntson GG, Bechara A, Damasio H, Tranel D, Cacioppo JT (2007) Amygdala contribution to selective dimensions of emotion. Soc Cogn Affect Neurosci 2(2):123–129. https://doi.org/10.1093/scan/nsm008

    Article  PubMed  PubMed Central  Google Scholar 

  36. Styliadis C, Ioannides AA, Bamidis PD, Papadelis C (2014) Amygdala responses to valence and its interaction by arousal revealed by MEG. Int J Psychophysiol 93(1):121–133. https://doi.org/10.1016/j.ijpsycho.2013.05.006

    Article  PubMed  Google Scholar 

  37. de Bruin G, Rassin E, Muris P (2006) Worrying in the lab: does intolerance of uncertainty have predictive value? Behav Chang 23(02):138–147. https://doi.org/10.1375/bech.23.2.138

    Article  Google Scholar 

  38. Rosen NO, Knäuper B (2009) A little uncertainty goes a long way: state and trait differences in uncertainty interact to increase information seeking but also increase worry. Health Commun 24(3):228–238. https://doi.org/10.1080/10410230902804125

    Article  PubMed  Google Scholar 

  39. Lebreton M, Bertoux M, Boutet C, Lehericy S, Dubois B, Fossati P, Pessiglione M (2013) A critical role for the hippocampus in the valuation of imagined outcomes. PLoS Biol 11(10):e1001684. https://doi.org/10.1371/journal.pbio.1001684

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tei S, Fujino J, Kawada R, Jankowski KF, Kauppi JP, van den Bos W, Abe N, Sugihara G, Miyata J, Murai T, Takahashi H (2017) Collaborative roles of temporoparietal junction and dorsolateral prefrontal cortex in different types of behavioural flexibility. Sci Rep 7(1):6415. https://doi.org/10.1038/s41598-017-06662-6

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Anatomy and Neurobiology, Boston University School of Medicine, 650 Albany St, Basement, Boston, MA, 02118, USA

    Danielle C. Farrar, Mark B. Moss & Ronald J. Killiany

  2. Department of Radiology, Boston University School of Medicine, Boston, MA, USA

    Asim Z. Mian

  3. VA Boston Healthcare System, Boston, MA, USA

    Andrew E. Budson

Authors
  1. Danielle C. Farrar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asim Z. Mian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew E. Budson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark B. Moss
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ronald J. Killiany
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danielle C. Farrar.

Ethics declarations

Funding

This study was funded by departmental funds from the Department of Anatomy and Neurobiology at the Boston University School of Medicine.

Conflict of interest

AEB has been an investigator for clinical trials for the following companies: AstraZenica, Hoffmann-La Roche, Eli Lily, FORUM Pharmaceuticals, Avanir, Axovant, and Neuronetrix. MBM serves as a consultant for Pfizer, Inc. AZM is a shareholder of Boston Imaging Core Lab.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farrar, D.C., Mian, A.Z., Budson, A.E. et al. Functional brain networks involved in decision-making under certain and uncertain conditions. Neuroradiology 60, 61–69 (2018). https://doi.org/10.1007/s00234-017-1949-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-017-1949-1

Keywords

Search

Navigation