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Parametric Response Surface Models for Analysis of Multi-site fMRI Data

  • Seyoung Kim
  • Padhraic Smyth
  • Hal Stern
  • Jessica Turner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3749)

Abstract

Analyses of fMRI brain data are often based on statistical tests applied to each voxel or use summary statistics within a region of interest (such as mean or peak activation). These approaches do not explicitly take into account spatial patterns in the activation signal. In this paper, we develop a response surface model with parameters that directly describe the spatial shapes of activation patterns. We present a stochastic search algorithm for parameter estimation. We apply our method to data from a multi-site fMRI study, and show how the estimated parameters can be used to analyze different sources of variability in image generation, both qualitatively and quantitatively, based on spatial activation patterns.

Keywords

Posterior Distribution Gibbs Sampler Precentral Gyrus Variance Component Analysis Conditional Posterior Distribution 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Seyoung Kim
    • 1
  • Padhraic Smyth
    • 1
  • Hal Stern
    • 1
  • Jessica Turner
    • 2
  1. 1.Bren School of Information and Computer SciencesUniversity of CaliforniaIrvine
  2. 2.Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvine

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