Hi
I have a question about using GLM Flex. My experiment is a pharmacological infusion paradigm and I have followed the psuedoblock approach implemented by others. My design is a fully-within 2 (placebo/drug) x 15 (each 15 minute scan subdivided in 1 minute blocks), i.e. 30 contrast images per subject - 18 subjects in total. Would it be possible to confirm the design specified below (same attached) is correct for asking the standard ANOVA questions (main effects and interactions).
Many thanks.
Clear IN F;
%
N.N_subs = [18];
IN.Within = [2 15];
IN.WithinLabs = {{‘Fac1Lev1’ ‘Fac1Lev2’} {‘Fac2Lev1’ ‘Fac2Lev2’ ‘Fac2Lev3’ ‘Fac2Lev4’.....
‘Fac2Lev5’ ‘Fac2Lev6’ ‘Fac2Lev7’ ‘Fac2Lev8’ ‘Fac2Lev9’ ‘Fac2Lev10 ‘Fac2Lev11’....
‘Fac2Lev12’ ‘Fac2Lev14’ ‘Fac2Lev15’}};
IN.Interactions = {[1 2]};
IN.FactorLabs = {‘Drug’ ‘PsuedoBlock’};
IN.EqualVar = [0 0]; % Do variance corrections
IN.Independent = [0 0]; % Do independence correction
%
F = CreateDesign(IN);
figure(20); clf
imagesc(F.XX); colormap(gray); shg
%
I.OutputDir = pwd;
I.F = F;
I.minN = 5;
I.DoOnlyAll = 1;
I.RemoveOutliers=0;
I.Scans = {...
‘s01_Fac1_Lev1.nii’; %Subject 1 factor 1 level 1
‘s01_Fac1_Lev2.nii’;
‘s01_Fac1_Lev3.nii’;
unitl
‘s01_Fac1_Lev15.nii’;
‘s01_Fac2_Lev1.nii’; % Subject 1 factor 2 level 1
‘s01_Fac2_Lev2.nii’;
‘s01_Fac2_Lev3.nii’;
unitl
‘s01_Fac2_Lev15.nii’;
repeat above for all 18 subjects
};
%
I.estSmooth = 1;
I.Cons(1).name = 'Main effect of Scan';
I.Cons(1).Groups = {1 2};
I.Cons(1).Levs = [2];
I.Cons(1).ET = 2;
I.Cons(1).mean = 0;
I.Cons(2).name = 'Main effect of PsuedoBlock';
I.Cons(2).Groups = {3 4 5 6 7 8 9 10 11 12 13 14 15 16};
I.Cons(2).Levs = [15];
I.Cons(2).ET = 3;
I.Cons(2).mean = 0;
I.Cons(3).name = 'Scan by PsuedoBlock Interaction';
I.Cons(3).Groups = {17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 ...
41 42 43 44 45 46 47};
I.Cons(3).Levs = [2 15];
I.Cons(3).ET = 4;
I.Cons(3).mean = 0;
I = GLM_Flex(I);
Clear IN F;
%
N.N_subs = [18];
IN.Within = [2 15];
IN.WithinLabs = {{‘Fac1Lev1’ ‘Fac1Lev2’} {‘Fac2Lev1’ ‘Fac2Lev2’ ‘Fac2Lev3’ ‘Fac2Lev4’.....
‘Fac2Lev5’ ‘Fac2Lev6’ ‘Fac2Lev7’ ‘Fac2Lev8’ ‘Fac2Lev9’ ‘Fac2Lev10 ‘Fac2Lev11’....
‘Fac2Lev12’ ‘Fac2Lev14’ ‘Fac2Lev15’}};
IN.Interactions = {[1 2]};
IN.FactorLabs = {‘Drug’ ‘Block’};
IN.EqualVar = [0 0]; % Do variance corrections
IN.Independent = [0 0]; % Do independence correction
%
F = CreateDesign(IN);
figure(20); clf
imagesc(F.XX); colormap(gray); shg
%
I.OutputDir = pwd;
I.F = F;
I.minN = 5;
I.DoOnlyAll = 1;
I.RemoveOutliers=0;
I.Scans = {...
‘s01_Fac1_Lev1.nii’;
‘s01_Fac1_Lev2.nii’;
‘s01_Fac1_Lev3.nii’;
unitl
‘s01_Fac1_Lev15.nii’;
‘s01_Fac2_Lev1.nii’;
‘s01_Fac2_Lev2.nii’;
‘s01_Fac2_Lev3.nii’;
unitl
‘s01_Fac2_Lev15.nii’;
repeat above for all 18 subjects
};
%
I.estSmooth = 1;
I.Cons(1).name = 'Main effect of Scan';
I.Cons(1).Groups = {1 2};
I.Cons(1).Levs = [2];
I.Cons(1).ET = 2;
I.Cons(1).mean = 0;
I.Cons(2).name = 'Main effect of PsuedoBlock';
I.Cons(2).Groups = {3 4 5 6 7 8 9 10 11 12 13 14 15 16};
I.Cons(2).Levs = [15];
I.Cons(2).ET = 3;
I.Cons(2).mean = 0;
I.Cons(3).name = 'Scan by PsuedoBlock Interaction';
I.Cons(3).Groups = {17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 ...
41 42 43 44 45 46 47};
I.Cons(3).Levs = [2 15];
I.Cons(3).ET = 4;
I.Cons(3).mean = 0;
I = GLM_Flex(I);
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