¥ 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