Model-based fMRI and its application to reward learning and decision making

Ann N Y Acad Sci. 2007 May;1104:35-53. doi: 10.1196/annals.1390.022. Epub 2007 Apr 7.


In model-based functional magnetic resonance imaging (fMRI), signals derived from a computational model for a specific cognitive process are correlated against fMRI data from subjects performing a relevant task to determine brain regions showing a response profile consistent with that model. A key advantage of this technique over more conventional neuroimaging approaches is that model-based fMRI can provide insights into how a particular cognitive process is implemented in a specific brain area as opposed to merely identifying where a particular process is located. This review will briefly summarize the approach of model-based fMRI, with reference to the field of reward learning and decision making, where computational models have been used to probe the neural mechanisms underlying learning of reward associations, modifying action choice to obtain reward, as well as in encoding expected value signals that reflect the abstract structure of a decision problem. Finally, some of the limitations of this approach will be discussed.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Brain / anatomy & histology
  • Brain / pathology
  • Brain / physiology
  • Brain Mapping
  • Computer Simulation
  • Decision Making*
  • Humans
  • Learning*
  • Magnetic Resonance Imaging / methods*
  • Models, Neurological
  • Prefrontal Cortex / anatomy & histology
  • Reproducibility of Results
  • Reward*
  • Software