¥ SPM5 and SPM2
H Estimate fixed ARMA(1,1) (more precisely, AR(1)+white noise) model for all
Òvoxels of interestÓ (pass an OLSQ F-test)
oBy averaging estimated auto-covariance matrix from OLSQ
residuals over these voxels
oSPM assumes AR parameter a È 0.2, and approximates
ARMA(1,1) correlations via linear Taylor series, to make correlation
parameter estimation easier to program
H Use GLSQ (same
for each voxel) to solve for b
s
oSPM99: Use
OLSQ and adjusts DOF downwards to allow for serial correlation
¥ FSL and FMRIstat (similar, but differ in important details at several
points)
HUse
OLSQ to get first-pass residuals; use these to estimate each voxelÕs auto-correlation matrix; smooth these matrices spatially (FSL & FMRIstat vary here)
HEstimate
AR(1) parameter for each voxel separately from smoothed matrices
HUse
GLSQ (different for each voxel) to solve for
b s
¥ All these programs use a non-REML method
to estimate serial correlation parameter(s) from the OLSQ residual auto-correlation
matrix, and then adjust these estimates to reduce the bias thus
introduced