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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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