Hi Steven,
With regard to distinguishing more activation from less deactivation,
I think the solution is to use contrast masking, which you can perhaps
do within your framework, but it will probably be easier with the
within-subject ANOVA model that Sterling suggests.
Sterling Johnson wrote:
> sounds like you want the interaction between time and group.
Just to be clear, for a two group, two time (repeated measures) ANOVA,
this time-group interaction is equivalent to a two-sample t-test of
the difference images.
> assuming you enter groups in this order:
> A1 A2 B1 B2
> 1 -1 -1 1 is the contrast you are interested in. this is the interaction. an F-test will tell you where groups differ with respect to change over time.
and a t-test of this will tell you where B2-B1 > A2-A1, or where
B1-B2 < A1-A2 (i.e. B activates more or deactivates less; swap all
signs if interested in A activating more than B)
> -1 1 -1 1 as a t-contrast will tell you the main effect of time (where activation increases over time) regardless of group.
> 1 1 -1 -1 and
> -1 -1 1 1 are main effects of group.
So I think if you mask the interaction t-contrast with a [-1 -1 1 1]
t-contrast, then you will find only regions where B activates more
than A, and if you mask it by [1 1 -1 -1] then you will find the
regions where B deactivates less than A.
Hope that helps,
Ged.
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