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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:
External Email - Use Caution 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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