Abstract #301874

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JSM 2003 Abstract #301874
Activity Number: 251
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301874
Title: Analysis of fMRI Data Using Generalized Estimating Equations
Author(s): Wen-lin Luo*+ and Thomas E. Nichols
Companies: University of Michigan and University of Michigan
Address: Dept. of Biostatistics, Ann Arbor, MI, 48109-2009,
Keywords: temporal autocorrelation ; GEE ; sandwich estimator ; fMRI
Abstract:

A functional magnetic resonance imaging (fMRI) study exhibits temporal autocorrelation within subject. Approaches to the intrinsic autocorrelation have been proposed for fMRI data. Among them, estimation theory dictates that whitening provides the most efficient parameter estimation. However, whitening can render the analysis sensitive to inferential bias when assumed and actual autocorrelations are different. Thus we propose to analyze the fMRI data using the generalized estimating equation (GEE). The virtue of GEE is that it provides consistent estimates of the covariance matrix for parameter estimates even when the temporal autocorrelation structure is misspecified, and the more efficient estimator is obtained by careful specification of the working correlation. With insufficient number of subjects, we assume structured covariance, but one more general than usually assumed. We investigate the performance of GEE in fMRI study using a series of simulations. We compare our method to SPM2, the widely used toolbox analyzing fMRI data. We report on the efficiency of the estimated parameters using different correlations, and on the precision of their standard errors.

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