@article{Bommel2014-07Patte-26272,
  year={2014},
  doi={10.1007/s11336-013-9352-2},
  title={Risk Patterns and Correlated Brain Activities : Multidimensional Statistical Analysis of fMRI Data in Economic Decision Making Study},
  number={3},
  volume={79},
  issn={0033-3123},
  journal={Psychometrika},
  pages={489--514},
  author={Bömmel, Alena van and Song, Song and Majer, Piotr and Mohr, Peter N. C. and Heekeren, Hauke R. and Härdle, Wolfgang K.}
}

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    <dc:creator>Härdle, Wolfgang K.</dc:creator>
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    <dc:creator>Majer, Piotr</dc:creator>
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    <dc:contributor>Majer, Piotr</dc:contributor>
    <dc:contributor>Bömmel, Alena van</dc:contributor>
    <dc:language>eng</dc:language>
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    <dc:contributor>Mohr, Peter N. C.</dc:contributor>
    <dcterms:title>Risk Patterns and Correlated Brain Activities : Multidimensional Statistical Analysis of fMRI Data in Economic Decision Making Study</dcterms:title>
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    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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    <dc:creator>Mohr, Peter N. C.</dc:creator>
    <dc:contributor>Heekeren, Hauke R.</dc:contributor>
    <dc:creator>Heekeren, Hauke R.</dc:creator>
    <dc:creator>Song, Song</dc:creator>
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    <dc:contributor>Härdle, Wolfgang K.</dc:contributor>
    <dc:contributor>Song, Song</dc:contributor>
    <dc:creator>Bömmel, Alena van</dc:creator>
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