Brian,
> We want the covariance estimate to be most accurate for the
> cluster(s) of interest. Would it then make sense to make cluster
> specific estimates resulting in several local covariance estimates
> instead of one global estimate? And if we know our contrast of
> interest apriori (as is the case for most clinical scans) would it
> be better to use a T-statistic to identify the voxels that enter the
> covariance estimate?
>
> For instance,
> 1) Perform OLS with T-based height and extent thresholds to identify
> most-likely clusters
> 2) Pool covariances over individual clusters in the normal manner to
> generate multiple V's
> 3) Perform WLS using cluster specific V's (with some metric,
> neighest-neighbor perhaps, for deciding which V to use for voxels not
> entering the covariance estimates)
>
> I'm probably in over my head with this (overfitting perhaps???) but
> it would seem like a logical extension to the SPM method at least
> for single-subject settings.
Something like this is reasonable but would be awkward to impliment at
the cluster level.
The best solution is to use some local but spatially regularized
estimate of the covariance. SPM5's Bayesian first level model does
this with priors, fmristat does this with smoothing of AR \rho maps
and FSL does it with non-stationary smoothing of ACF maps.
-Tom
-- Thomas Nichols -------------------- Department of Biostatistics
http://www.sph.umich.edu/~nichols University of Michigan
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-------------------------------------- Ann Arbor, MI 48109-2029
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