A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data

  • Seyoung Kim
  • Padhraic Smyth
  • Hal Stern
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Traditional techniques for statistical fMRI analysis are often based on thresholding of individual voxel values or averaging voxel values over a region of interest. In this paper we present a mixture-based response-surface technique for extracting and characterizing spatial clusters of activation patterns from fMRI data. Each mixture component models a local cluster of activated voxels with a parametric surface function. A novel aspect of our approach is the use of Bayesian nonparametric methods to automatically select the number of activation clusters in an image. We describe an MCMC sampling method to estimate both parameters for shape features and the number of local activations at the same time, and illustrate the application of the algorithm to a number of different fMRI brain images.


Mixture Model fMRI Data Dirichlet Process Activation Cluster Expert Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Hartvig, N.: A stochastic geometry model for fMRI data. Research Report 410, Department of Theoretical Statistics, University of Aarhus (1999)Google Scholar
  2. 2.
    Penny, W., Friston, K.: Mixtures of general linear models for functional neuroimaging. IEEE Transactions on Medical Imaging 22(4), 504–514 (2003)CrossRefGoogle Scholar
  3. 3.
    Kim, S., Smyth, P., Stern, H., Turner, J.: Parametric Response Surface Models for Analysis of Multi-Site fMRI data Mixtures of general linear. In: Proceedings of the 8th International Conference on Medical Image Computing and Computer Assisted Intervention (2005)Google Scholar
  4. 4.
    Furguson, T.: A Bayesian analysis of some nonparametric problems. Annals of Statistics 1(2), 209–230 (1973)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Rasmussen, C.E.: The infinite Gaussian mixture model. In: Solla, S.A., Leen, T.K., Muller, K.-R. (eds.) Advances in Neural Information Processing Systems 12, pp. 554–560. MIT Press, Cambridge, MA (2000)Google Scholar
  6. 6.
    Neal, R.M.: Markov chain sampling methods for Dirichlet process mixture models. Technical Report 4915, Department of Statistics, University of Toronto (1998)Google Scholar
  7. 7.
    Escobar, M., West, M.: Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90, 577–588 (1995)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seyoung Kim
    • 1
  • Padhraic Smyth
    • 1
  • Hal Stern
    • 1
  1. 1.Bren School of Information and Computer SciencesUniversity of CaliforniaIrvine

Personalised recommendations