Dear Colin,
When you use "adjust data for everything", you regress out your entire
design matrix, ie you get the residuals so that's probably not what you
want here.
What you could do is save a mask image of your cluster of interest
('save > current cluster' when looking at results) and then use
spm_summarise:
V = cellstr(spm_select(Inf,'image'));
spm_summarise(V,'cluster.nii','litres')
Best regards,
Guillaume.
On 08/12/16 21:53, Colin Sauder wrote:
> Good evening,
>
> A quick question. I'm interested in extracting estimates of GM density
> from a VBM analysis. Specifically, I want to estimate for each
> participant the "volume" within a significant cluster. I've been using
> the eigenvariate tool in results to do so, but am unsure about whether
> to "adjust data for everything" or do no adjustment. It appears as
> though "adjust for everything" is doing some sort of mean center, which
> I would not want in this case since I have two groups within the
> analysis. Finally, I've been using the data from xY.u, which seem to
> match the graph. The first 20 are control, the second 20 are clinical.
> One final note, this is a between samples T-test analysis, although I
> could easily do an F-test.
>
> Am I correct in the assumptions above? Should I be adjusting or not?
>
> Thank you,
> Colin
>
> --
>
> Colin L. Sauder, Ph.D.
>
> Assistant Professor, Child and Adolescent Psychiatry
>
> Department of Psychiatry
>
> University of Texas Health Science Center at San Antonio
>
> 7703 Floyd Curl Drive MC 7719
>
> San Antonio, TX 78229
>
> V: 210-567-5417
>
> F: 210-567-5677
>
> [log in to unmask] <mailto:[log in to unmask]>
>
--
Guillaume Flandin, PhD
Wellcome Trust Centre for Neuroimaging
University College London
12 Queen Square
London WC1N 3BG
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