Yes, let's say you have 4 groups (eg, gender and sequence) and one covariate (BDI score). If you use a DODS model, then you will have 8 regressors (4 intercepts and 4 slopes). To test for an effect of covariate regressing out the group, then you would have

[0 0  0 0 .25 .25 .25 .25]

The +0.25 computes the mean slope across all groups (note you could also just use all 1s, you will get the same p-value).

On 03/09/2018 01:43 PM, k.ohmstedt@stud.uni-heidelberg.de wrote:

Hi Douglas,

thanks for the advice.

I have one more question: I want to find correlations of the lGI and the respective psychometric measurement, regressing out the effect of gender, sequence and the other covariates.
I just had a look at the examples of FSGDs containing two factors with two levels each and as far as I understand it, it is only possible to compare two of the groups or to find an interaction between groups and covariates.

Is it at all possible to find out how the lGI is correlated to one of my covariates, i.e. higher values of Beck's Depression Inventory correlate with higher/lower values of the lGI, when regressing out the other factors? If yes, how do I build the contrast file to do so correctly? I am stuck here.

Thank you for the time and effort!


Best regards

Kai

Zitat von Douglas Greve <dgreve@mgh.harvard.edu>:

Hi Kai, please remember to post to the list. Your FSGD file is not
quite right. Gender is a discrete variable and should be represented by
two groups not as a covariate. If Sequence is discrete, then you need
four groups (Gender by Sequence).

doug


On 3/9/18 3:20 AM, k.ohmstedt@stud.uni-heidelberg.de wrote:

Dear Douglas,

the fsgd file I used is attached. Thank you for your help!


Best regards

Kai

Zitat von "Douglas N. Greve" <dgreve@mgh.harvard.edu>:

can you send your fsgd file so that I have a better idea of what you are
mentioning?


On 03/08/2018 08:39 AM, k.ohmstedt@stud.uni-heidelberg.de wrote:

Dear all,

I am trying to correlate psychometric measurements and the local
Gyrification Index.
To do so, I use the FreeSurfer pipeline to calculate the lGI and then
PALM, following the advice in this thread
(https://mail.nmr.mgh.harvard.edu/pipermail//freesurfer/2017-March/050703.html). All my subjects are part of the same group, so I used a FSGD with  the
group "main" to create the design matrix and mask for my data that are
required by PALM.
Having a closer look at the design matrix that was created, I found
that there was a variable for the group that was the same for all my
patients. As it is the same for all patients, I thought eliminating it
would not be a problem. But after re-running PALM without it, there
were huge differences in my results and effects were notably larger
and more significant.

Do any of you have any experience which option is best in this case?
Is it a valid choice to eliminate the variable for the group, as it is
the same for each patient anyway?

Furthermore, would you recommend centering for the design matrix? I
found that this can have an impact, but I am lost on in which cases it
should be done and in which it shouldn't.


Thank you for your help!


Best regards

Kai Ohmstedt


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