Dear Christian,
Thanks a lot for your reply and explanation.
The reason why I did not use the flexible factorial design, was that I understood this to model the variance for every subject separately and therefore making any
covariates obsolete, since individual variance would already be accounted for.
Maybe I missunderstood this one!?
I also have two follow-up questions:
1) What is the interpretation of the negative values in the grey matter images? I assume they arise from the registration to the average image, but the process is not
quite clear to me yet.
2) Do you think the negative values in the data provide a problem for creating the difference image, in the sense that when two negative values are substracted from
one another, this results in an increase rather than a decrease ? (i.e. (-3)- (-2)=(-3)+2=1) Or would that maybe even be desirable? I guess this also depends on the
above answer.
Thanks a lot for your help!
Best Wishes,
Marie.
----- Ursprüngliche Mail -----
Von: "Christian Gaser" <[log in to unmask]>
An: [log in to unmask], "Marie Uhlig" <[log in to unmask]>
CC: "Christian Gaser" <[log in to unmask]>
Gesendet: Montag, 26. September 2016 23:44:32
Betreff: Re: delta grey matter images
Dear Marie,
the motivation why you are using residual images to consider TIV is not clear for me. If you are interested in an analysis of an interaction between time and group I would suggest to use one of these approaches:
1. Use a flexible factorial model with the factors subject, group, and time and model an interaction between factors 2 and 3 (group and time) and a main factor 1 (subject). Use TIV as nuisance variable. There is an example in the CAT12 manual at page 24.
2. Calculate difference images between the two time points and compare the difference image using a two sample t test. Use the mean TIV of both time points as nuisance parameter. Please don't use an absolute threshold for masking because the difference images also contain meaningful information < 0. I would suggest to create a mask based on the thresholded mean image of GM of one time point.
Both models use modulated images and TIV as nuisance parameter and allow to test for different time curves between both groups (interaction time x group).
Best,
Christian
On Sun, 25 Sep 2016 03:33:28 +0200, Marie Uhlig <[log in to unmask]> wrote:
>Dear SPM-Mailing List,
>
>I am running a VBM-Analysis investigating the effect of an intervention on two different groups.
>
>I have used the longitudinal segmentation of the cat12-toolbox and i am working with the (s)mwp1-(grey matter)-images.
>
>I used a multiple regression with the TIV covariate (separately for each timepoint) in order to obtain the residual images (Res_*.nii), that have been corrected for any effect of modulation.
>
>In order to model the difference of these images i have calculated the delta (timepoint2-timepoint1).
>
>However, i am running into the problem, that negative values are present in my data before subtraction already. Therefore a decrease in GM may result in larger values in the delta-image instead of smaller
>
>(eg. (-3) - (-2)= (-1) ).
>
>
>Does anyone have any idea on how to circumvent this?
>
>
>I assume using working with the unmodulated, segmented grey matter images would be the optimal choice here, but are there other approaches when using the modulated data?
>
>
>
>Thanks a lot for your help in advance!
>
>All the best!
>
>Marie.
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