FMRI datasets allow scientists to assess functionality of the brain by measuring the response of blood flow to a stimulus. Since the responses from neighboring locations within the brain are correlated, simple linear models that assume independence of measurements across locations are inadequate. Mixed models can be used to model the spatial correlation between observations, however selecting the correct covariance structure is difficult. Information criteria, such as AIC are often used to choose among covariance structures. Once the covariance structure is selected, significance tests can be used to determine if a region of interest within the brain is significantly active. Through the use of simulations, this project explores the performance of AIC in selecting the covariance structure. Type I error rates are presented for the fixed effects using the the AIC chosen covariance structure. Power of the fixed effects are also discussed.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Stromberg, David A., "Performance of AIC-Selected Spatial Covariance Structures for fMRI Data" (2005). All Theses and Dissertations. 634.
fMRI, Spatial Statistics, AIC, Covariance Structure