Functional brain networks involved in decision-making under certain and uncertain conditions
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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.
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.
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.
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.
KeywordsDecision-making fMRI Executive function
Compliance with ethical standards
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.
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 was obtained from all individual participants included in the study.
- 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 CrossRefGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedGoogle Scholar
- 15.Nieuwenhuys R, Voogd J, van Huijzen F.M.A.A. C (2008) The human central nervous system: Springer Berlin HeidelbergGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedPubMedCentralGoogle Scholar