Performance of AIC-Selected Spatial Covariance Structures for fMRI Data

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.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Stromberg, David A.

• fMRI
• Spatial Statistics
• AIC
• Covariance Structure
• Statistics and Probability

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/634

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1633