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Hello Swati,
your points 1-4 look fine, as far as I can see.
Regarding 5, the recommended spatiotemporal mass-univariate approach consists
of calling three functions:
lme_mass_fit_EMinit, lme_mass_RgGrow, lme_mass_fit_Rgw
in that order. If that does not work, it is in my eyes possible to use the more
simple mass-univariate approach using
lme_mass_fit_vw
as an alternative.
Regarding 6, which specific questions do you have?
Best regards,
Kersten
On Do, 2020-03-12 at 05:58 +0000, Swati Rane wrote:
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Dear FreeSurfer experts,
I simply do not understand the LME setup for longitudinal analyses.
Here is what I have: 3 groups each with variable longitudinal data (in terms of
umber of time points and interval between timepoints). I am interested in
detecting if the rates of longitudinal atrophy in each of the 3 groups and
whether it is significantly different between the 3 groups.
1. My Qdec file contains: fsid, fsid_base, time, age, group, gender
2. I read in the relevant files with (output of surf2surf, lh.cortex,
lh.sphere)
3. I read the Qdec file removed the first column, obtained the subject IDs,
keep numerical data and get a sorted ‘M’ matrix
4. X=[intercept time grp1 time*grp1 grp2 time*grp2 grp3 time*grp3
age@timepoint1 gender]
5. After that, should I use voxel wise mass-univariate model or
spatiotemporal mass-univariate model lme_mass_fit_vw or lme_mass_fit_EMinit?
6. After that I don’t understand the subsequent steps. Can you help me
understand?
Any help is appreciated.
Thanks!
Swati
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