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Risk Patterns and Correlated Brain Activities. Multidimensional Statistical Analysis of fMRI Data in Economic Decision Making Study

Author

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  • Alena Bömmel
  • Song Song
  • Piotr Majer
  • Peter Mohr
  • Hauke Heekeren
  • Wolfgang Härdle

Abstract

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b ). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009 ) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior. Copyright The Psychometric Society 2014

Suggested Citation

  • Alena Bömmel & Song Song & Piotr Majer & Peter Mohr & Hauke Heekeren & Wolfgang Härdle, 2014. "Risk Patterns and Correlated Brain Activities. Multidimensional Statistical Analysis of fMRI Data in Economic Decision Making Study," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 489-514, July.
    • Handle: RePEc:spr:psycho:v:79:y:2014:i:3:p:489-514
    DOI: 10.1007/s11336-013-9352-2
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ying Chen & Wolfgang K. Härdle & Qiang He & Piotr Majer, 2015. "Risk Related Brain Regions Detected with 3D Image FPCA," SFB 649 Discussion Papers SFB649DP2015-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Barbara Choroś-Tomczyk & Wolfgang Karl Härdle & Ostap Okhrin, 2013. "CDO Surfaces Dynamics," SFB 649 Discussion Papers SFB649DP2013-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Casado-Aranda, Luis-Alberto & Liébana-Cabanillas, Francisco & Sánchez-Fernández, Juan, 2018. "A Neuropsychological Study on How Consumers Process Risky and Secure E-payments," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 151-164.
    4. Chen Ying & Härdle Wolfgang K. & He Qiang & Majer Piotr, 2018. "Risk related brain regions detection and individual risk classification with 3D image FPCA," Statistics & Risk Modeling, De Gruyter, vol. 35(3-4), pages 89-110, July.
    5. Chao, Shih-Kang & Härdle, Wolfgang K. & Huang, Chen, 2018. "Multivariate factorizable expectile regression with application to fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 1-19.
    6. Shih-Kang Chao & Wolfgang K. Härdle & Chen Huang, 2016. "Multivariate Factorisable Sparse Asymmetric Least Squares Regression," SFB 649 Discussion Papers SFB649DP2016-058, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Li, Yingxing & Huang, Chen & Härdle, Wolfgang K., 2019. "Spatial functional principal component analysis with applications to brain image data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 263-274.
    8. Choroś-Tomczyk, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2016. "A semiparametric factor model for CDO surfaces dynamics," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 151-163.
    9. Likai Chen & Weining Wang & Wei Biao Wu, 2017. "Dynamic Semiparametric Factor Model with a Common Break," SFB 649 Discussion Papers SFB649DP2017-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Maria Grith & Wolfgang K. Härdle & Alois Kneip & Heiko Wagner, 2016. "Functional Principal Component Analysis for Derivatives of Multivariate Curves," SFB 649 Discussion Papers SFB649DP2016-033, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Piotr Majer & Peter Mohr & Hauke Heekeren & Wolfgang Karl Härdle, 2014. "Portfolio Decisions and Brain Reactions via the CEAD method," SFB 649 Discussion Papers SFB649DP2014-036, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Yingxing Li & Chen Huang & Wolfgang Karl Härdle, 2017. "Spatial Functional Principal Component Analysis with Applications to Brain Image Data," SFB 649 Discussion Papers SFB649DP2017-024, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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