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Risk Patterns and Correlated Brain Activities. Multidimensional statistical analysis of fMRI data with application to risk patterns

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Author Info

  • Alena Myšičková
  • Song Song
  • Piotr Majer
  • Peter N.C. Mohr
  • Hauke R. Heekeren
  • Wolfgang K. 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 reanalyze functional magnetic resonance imaging (fMRI) data on 17 subjects which were exposed to an investment decision task from Mohr et al. (2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. Our goal is to capture the dynamic behavior of specific brain regions of all subjects in this high-dimensional time series data, by a flexible factor approach resulting in a low dimensional representation. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park et al. (2009) and identify task-related activations in space and dynamics in time. Further, we classify the risk attitudes of all subjects based on the estimated lowdimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects' decision behavior.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2011-085.

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Length: 39 pages
Date of creation: Dec 2011
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2011-085

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Keywords: risk; risk attitude; fMRI; decision making; medial orbifrontal cortex; semiparametric model; factor structure; SVM;

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References

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  30. Lu Lin & Feng Li & Lixing Zhu & Wolfgang Karl Härdle, 2011. "Mean Volatility Regressions," SFB 649 Discussion Papers SFB649DP2011-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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