I launched randomise with the following command
-i Cognition.nii.gz -m L_Puta_mask.nii.gz -o ./L_Puta_randomise/L_Puta -d Group_model.mat -t Group_model.con -f Group_model.fts --T2 --vxl=4,5,6 --vxf=L_Puta_Int_alelle0Shape.nii.gz,L_Puta_Int_alelle1Shape.nii.gz,L_Puta_Int_alelle2Shape.nii.gz
When the program start the permutation, after having calculated the statistic for the unpermuted data it gives me a warning saying:
"Warning: The unpermuted statistic image for the current image contains no positive values, and cannot be processed with TFCE. A blank output image will be created."
This happen for the first of the contrast that you suggested, that is the f contrast based on the following t contrast
1 -1 0 0 0 0
1 0 -1 0 0 0
But not if I skip the first contrast and I run directly the second f contrast
0 0 0 1 -1 0
0 0 0 1 0 -1
If I understood correctly, this kind of warning is related either to bad contrast or to something wrong in the images. In this case I don't think the contrast is wrong, and the fact that the second f contrast works make me think that the images are fine too.
Just for clarification, the input image is a 4d volume containing the mask of the putamen multiplied by the (demeaned) cognitive score of each subject.
Have you got any idea about what could have gone wrong ?
Thanks
A
Hi Anderson,
just a quick followup question.
I decided to use the design you suggested (6 columns, 1 for each of the 3 group and 3 for the interactions group x shape). To do this, I used the fsl_glm gui, entering 3 for the opetion "additional voxel-dependent EVs" and choosing the correct images. I then entered the proper EVs, the contrasts and saved the design.
If I open the .mat file, I see that there are 6 columns: the first 3 are the columns that I entered in the gui, but I wonder from where the value in the last 3 columns came, and if they have any meaning or importance.
Do they interact with the 4d images that I entered in the randomise command line (--vxl=4,5,6 --vxf=myfile1,myfile2,myfile3) ?
Thank in advance
A
Hi everybody,
in the context of a shape analysis using FIRST, I want to observe the effect of shape, SNPs and their interaction on a cognitive variable.
Anderson Winkler and Mark Jenkinson suggested that the correct way of doing this would be to test a model in randomise using cognition as a dependent variable.
In particular, the model will look like this
Cognition = SNP + Shape + SNPxShape + a bunch of covariables like sex etc
To do so, i need to use some voxelwise EV in my models, and being the first time, I am getting confused.
The first thing that I did is multiplying the structure of interest mask for the cognitive measure of intereset for each subject, and then create a 4D volume. This volume will be the dependent variable in the randomise model (i.e. the input volume).
Then, for the interaction between shape and SNP, I would need to multiply the shape volume for the SNP, again for each subject.
Now, in this analysis SNP is coded as group (3 levels), rather than as a continuous variable.
This make unclear to me what I should do. I am thinking of coding the three groups with a dummy coding (i.e. two columns, 0 0 for group 1, 1 0 for group 2, 0 1 for group 3) and then multiplying the shape volume for these two columns.
In this way, the model in randomise would look like this:
Cognition(4D volume) = SNPDummy1 + SNPDummy2 + shapeXSNPDummy1 (4D volume) + shapeXSNPDummy2 (4D volume) + covariates
Does this make sense ?
I have more doubts:
1) Should I demean the two dummy columns that code for the group ?
2) Is correct to leave SNPDummy1, SNPDummy2 and all the other covariables as usual EV in the .mat file ?
3) Since I am mixing EV in the .mat file and voxelwise EV, how can I construct the contrasts of interest ? In which order the voxelwise EV are entered in the model ?
Thank for your time
Alain