¥OLSQ = consistent estimator of FMRI
time series fit parameter vector b
H No matter what the temporal (AKA serial) correlation structure of
the noise
oÒConsistentÓ means that if you repeated the identical experiment infinitely many
times, and averaged the
estimated value (e.g., b ; variance), result would be the true value
¥But OLSQ estimate of time series noise variance is not consistent when serial correlation is present
HOLSQ variance estimator will usually be biased too small with serial
correlation
¥Variance estimate is in denominators of formulas for t- and F-statistics
HResult: individual subject t-
and F-values will be too large and/or their
DOF parameters will be
too large
HUpshot: Significance of individual subject activations will be
over-estimated (p-values will be too small)
HThresholded individual
subject FMRI maps
might show too much activation
HObvious impacts on ROIs
generated directly from individual subject activation maps (e.g.,
for connectivity analysis)
HHowever, statistics taking into account serial correlation can be
too conservative, and
understate the extent of the ÒtrueÓ regions of
activation
o For this reason, and to avoid selection bias, perhaps it is
best to define FMRI-derived ROIs
using a spherical Òpunch outÓ around each
activation map peak