> This simple method combines the individual
> subject t-statistics (from the linear-contrast between
> event class A and class B) by voxel-wise averaging
> of the t-statistics across subjects and multiplying this
> by the square root of the number of
> subjects/experiments
This is essentially the so-called fixed-effect method, which is usually more liberal and lenient than random-effect analysis.
> I recently used 3danova on the beta weights of
> the same linear contrast, directly comparing the
> same 12 vs 12 subjects.
An alternative is to run a two-sample
t test with 3dttest.
> Could I use this t-test map of the ANOVA mean
> as a valid groupwise map? (In Bob's terms, would
> this be "reasonable"?). This map basically tells the
> same story as the meta-analytic formula-derived
> group map, but may be superior in that it
> incorporates random effects. Is this correct?
Yes, the result from -mean is more or less equivalent to a one-sample
t test; in other words, you could simply run a one-sample
t test on both groups to get the group activation maps. Either way cross-subject variability is accounted for in this approach that is more appropriate for making generation about populations.
> Could I present the anova-mean map of each
> group seperately (here adolescents and adults) to
> illustrate each group's distinct combined activation
> if the two groups did not have equal numbers of
> subjects, and still have a fair comparison?
> Alternatively, would the anova-mean map of the
> group with more subjects be biased toward a
> stronger voxel t-statistics by virtue of larger
> sample size and not an authentic group-wise
> difference in activation?
Well, if homogeneity of variance across groups is a concern, simply analyze each group separately with a one-sample
t test (3dttest).
Gang
