On Tue, Mar 6, 2012 at 3:57 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:

Hi Allie, yes, it means that each group is affected differently by age. The controls are increasing their thickness with age in this area (which seems strange, usually you expect general decreases with age). You might check the quality of the surfaces in this area.
doug

Allie Rosen wrote:

Hi Doug,

Thanks for the response. I've actually already run an interaction contrast and the same area is still significant (attached). I'm not sure how to interpret this either, unfortunately. Does it say that both groups are affected significantly differently by age? Can you tell me what it means? I'm having problems trying to do this myself.

Thanks very much,
Allie

On Tue, Mar 6, 2012 at 3:47 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> wrote:

Allie, to be more precise, you have selected control<patient
regressing out the effects of age. This second part is the key.
If you were to trace the regression lines back to age=0 (the
meaning of "regressing out age"), then control<patient by a wide
margin. But you have a problem here in that the regression lines
are crossed. This means that you have an interaction between age
and patient (no interaction means that the lines would be
parallel). An interaction means that you cannot assess whether
there is a difference in the thickness because this difference
changes depending upon the age. One thing you can try is to
actually compute an interaction contrast (something like [0 0 1
-1]). If it is significant in this area, then there's not much you
can do (though it may be interesting in itself). If it is not
significant, then you can switch to a DOSS model which forces the
lines to be parallel and makes the distance between the lines be
independent of age. Does this make sense?
doug

Allie Rosen wrote:

Hi All,

I'm having trouble reconciling the cluster colours with the
graph that can be made when you load the group descriptor file
and click on a point. I've attached an example.
In this study, I'm comparing thickness in a patient and
control group, with age regressed out. The contrast I've used
means that blue means control<patient. Therefore, patients are
thicker than controls. But in the graph, patients don't seem
to be thicker. I've seen the same results with red clusters
(i.e. they should mean that controls are thicker but the graph
shows otherwhise). Please let me know how I can interpret my
findings given the graph. Which group is actually thicker?

Thank you,
Allison

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