Hi Gang,
Thank you again for your answer,
For the record, here is the new command:
3dLME -prefix /3dLME_glt.nii.gz \
-jobs 8 -model "gender*age" -qVars "age" -ranEff '~1+age' \
-num_glt 5 \
-gltLabel 1 '05MF' -gltCode 1 'gender : 0.5*M +0.5*F age :' \
-gltLabel 2 '1MF' -gltCode 2 'gender : 1*M -1*F age :' \
-gltLabel 3 '1MF' -gltCode 3 'gender : 1*M age :' \
-gltLabel 4 '1F' -gltCode 4 'gender : 1*F age :' \
-gltLabel 5 'population_effect' -gltCode 5 'gender : 0.5*M +0.5*F' \
-dataTable @3dLME_disign_matrix.txt
Sorry to ask again some "basic questions" but I am a little bit confused with the output and the different weight:
Tell me if I am correct:
For LME, the conditional distribution of the response is assumed to be normal.
For GLM, you can specify other distributions.
However, I am not sure of the difference between:
"0.5*M +0.5*F ag" and "1*M -1*F age"?
Why 0.5? Why 1?
I am not sure of the meaning of the first sub-bricks for each glt:
-- At sub-brick #4 '05MF' datum type is float
-- At sub-brick #5 '05MF Z' datum type is float
I assumed that the second is the z-score.
Then, I should work on the z-score for clustering and mutli- comparison correction?
Finally, base on the results of the analysis I should choose "manually" what is the best distribution?
"0.5*M +0.5*F ag" and "1*M -1*F age", only F or M?
Weirdly, I find, for example, a positive z-score in one of the regions of interest with "0.5*M +0.5*F age", "1F" and "1M".
And a negative z-score "1*M -1*F age"
Does it sound normal?
Thank you again for your help,
Clément
