Dear Donald,
thank's a lot for your answer!
There is in fact a within-subject factor in my design (condition, 2 levels).
In my original analysis I computed group*condition interactions.
for example:
condition1/group1 > condition1/group2
[ones(1,nSubj1)/nSubj1 -ones(1,nSubj2)/nSubj2 zeros(1,nSubj3) 1 0 -1
0 zeros(1,nConds)]
nSubj: number of subjects in the group
nConds: number of conditions (which is 2)
As you mentioned in your answer, this should be possible. Am I'm right?
Since I'm especially interested in 1 condition and was not aware of
the importance of the within-subject factor in a factorial design, I
didn't mention the within-subject factor bevore.
Regarding to your answer another question appeared. I can not
understand why I should not include covariates in a within-subjects
design. What is the reason for that? Except the vbm-covariate there is
another covariate ("smoker" - yes=1, no=0) which seems to be important
to ad (my collegue also added it in her vbm-analysis). Especially to
keep functional and structural analysis comparable, I would like to ad
it to my analysis.
Best,
Anja
2012/10/19, MCLAREN, Donald <[log in to unmask]>:
> On Wed, Oct 17, 2012 at 6:50 AM, Anja Dietrich <[log in to unmask]>
> wrote:
>> Dear experts,
>>
>> I performed a 2nd level fmri analysis (flexible factorial design) with a
>> subject factor (24 subs) and a group factor (3 groups). Regardig this I
>> found functional differences between the 3 groups. Further a colleague of
>> mine found differences in gray matter volume between the 3 groups and
>> relating to some functional relevant areas.
>
>>>> Group comparisons (unless its the group*condition interaction) are not
>>>> valid in the flexible factorial (or full factorial) or any GLM which
>>>> only has a single error term when you have repeated-measures. In my
>>>> previous posts, I've stated that between-subject effects are not
>>>> statistically valid in within-subject designs. If you only have one
>>>> condition per subject, then you don't need to enter subjects as a
>>>> factor.
>
>> Now I would like to analyze, if the functional differences between the
>> groups are a consequence of gray matter volume differences.
>> Regarding this I thought it should be a good idea to integrate the vbm
>> data as a covariate in the fmri analysis. I have heard of the bpm toolbox
>> (biological parametric mapping) which offers the opportunity to integrate
>> voxel-wise covariates. But I'm not sure if it is possible to realize the
>> flexible factorial design in the environment of this toolbox (the subject
>> factor seems to be important for my fmri analysis). I would be very
>> thankful if somebody could give me some advice. Maybe there are also other
>> opportunities in solving the problem.
>
>>>> Covariates should not be included in within-subject designs.
>
>>
>> Thank's in advance!
>>
>> Anja
>
