A Fully Bayesian Two-Stage Model for Detecting Brain Activity in fMRI

  • Alicia Quirós
  • Raquel Montes Diez
  • Juan A. Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)


Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for obtaining a series of images over time under a certain stimulation paradigm. We are interested in identifying regions of brain activity by observing differences in blood magnetism due to haemodynamic response to such stimulus.

Here, we extend Kornak (2000) work by proposing a fully Bayesian two–stage model for detecting brain activity in fMRI. The only assumptions that the model makes about the activated areas is that they emit higher signals in response to an stimulus than non-activated areas do, and that they form connected regions, providing a framework for detecting activity much as a neurologist might.

Due to the model complexity and following the Bayesian paradigm, we use Markov chain Monte Carlo (MCMC) methods to make inference over the parameters. A simulated study is used to check the model applicability and sensitivity.


Markov Chain Monte Carlo fMRI Data Haemodynamic Response Human Brain Mapping Markov Chain Monte Carlo Iteration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Descombes, X., Kruggel, F., von Cramon, D.Y.: Spatio-temporal fMRI analysis using Markov random fields. IEEE Transactions on Medical Imaging 17, 1028–1039 (1998)CrossRefGoogle Scholar
  2. Frackowiak, R.S.J., Friston, K.J., Frith, C.D., Dolan, R.J., Mazziota, J.C.: Human Brain Function. Academic Press, London (1997)Google Scholar
  3. Friston, K.J., Turner, R.: Analysis of functional MRI time series. Human Brain Mapping 1, 153–171 (1994)CrossRefGoogle Scholar
  4. Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo in Practice. Chapman & Hall, Boca Raton (1996)MATHGoogle Scholar
  5. Hartvig, N.V.: A stochastic geometry model for fMRI data, Technical Report 410, University of Aarhus (1999)Google Scholar
  6. Hartvig, N.V., Jensen, J.L.: Spatial mixture modelling of fMRI data, Technical Report 414, University of Aarhus (2000)Google Scholar
  7. Kornak, J.: Bayesian Spatial Inference from Haemodynamic Response Parameters in Functional Magnetic Resonance Imaging, University of Nottingham, PhD. Thesis (2000)Google Scholar
  8. Lange, N., Zeger, S.L.: Non-linear Fourier time series analysis for human brain mapping by functional magnetic reonance imaging (with discussion). Journal of Applied Statistics 46(1), 1–29 (1997)MATHMathSciNetGoogle Scholar
  9. Meyer, F.G., Chinrungrueng, J.: Clustering of spatiotemporal signals: application to the analysis of fMRI data. IEEE Transactions on Medical Imaging 22(8), 933–939 (2003)CrossRefGoogle Scholar
  10. Rajapakse, J.C., Kruggel, F., Maisog, J.M., von Cramon, D.Y.: Modeling hemodynamic response for analysis of functional MRI time-series. Human Brain Mapping 6, 283–300 (1998)CrossRefGoogle Scholar
  11. Wang, Y., Schultz, R.T., Constable, R.T., Staib, L.H.: Nonlinear stimation and modeling of fMRI data using spatio-temporal support vector regression. In: Information Processing in Medical Imaging Proceedings, pp. 647–659 (2003)Google Scholar
  12. Winkler, G.: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introduction. Springer, cop, Berlin (2003)Google Scholar
  13. Woolrich, M.W., Jenkinson, J.M., Brady, J.M., Smith, S.M.: Fully bayesian spatio-temporal modeling of fMRI data. IEEE Transactions on Medical Imaging 23(2) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alicia Quirós
    • 1
  • Raquel Montes Diez
    • 1
  • Juan A. Hernández
    • 1
  1. 1.University Rey Juan CarlosMadridSpain

Personalised recommendations