Statistical Analysis

Hello AFNI

I have CBF maps for 50 subjects.
I want to know if they are any significant relationships between the CBF concentration and a clinical score (one number per subject) after controlling for age, gender and gray matter concentration.

Which program do you suggest me to do this analysis?

Thank you :slight_smile:

Hi, to be more specific.
My dependent variable is CBF map (50 in total, one for each subject)
My independent variable is the clinical measure (50 in total, one number for each subject)
My covariates are: age, gender and GMC: gray matter concentration (the GMC is map which is nifti file)

Try 3dMVM

Hi Gang thanks for answering me, I feel very confused with the design of my 3dMVM script, would you mind to help me a bit?
I want to see the relationship between the CBF_maps (voxel wise, .nii file per subject) with a quantitative clinical measure (loneliness, which is one single number per subject). I need to control for age, gender, depression scores and gray matter concentration (the gray matter concentration is a voxel wise covariate).

Dependent variable: voxel wise CBF
Independent variable: Loneliness (clinical measure)
Covariates: age, gender, depression and voxel wise gray matter concentration.

I have done this part of the script but I feel lost with the other part

3dMVM -prefix 3dMVM_test -jobs 4
-bsVars ‘loneliness’
-mask GM2mm_WFU_Mask.nii.gz
-qVars “age,gender,hamd”
-vVars ‘gmc’ \

I also create the txt file of the data and it looks something like this:

Subj age gender hamd hama gmc loneliness group InputFile
172_120916 68 1 21 9 c1w172_120916.nii 37 1 ASL_CBF_2MNI_6mm_172_120916.nii
222_103015 77 -1 19 13 c1w222_103015.nii 36 1 ASL_CBF_2MNI_6mm_222_103015.nii
225_010416 65 1 2 1 c1w225_010416.nii 23 1 ASL_CBF_2MNI_6mm_225_010416nii

Would you mind to help me with some guidance to build the script?

Thank you

I assume that all the variables are between-subjects. Try something like

-bsVars ‘loneliness+age+gender+hamd+gmc’ \

Hi Gang, here is my script. I am trying to see the main effect of loneliness, main effect of hamd and their interaction in my CBF maps. would you mind to tell me if I am doing it correctly?

3dMVM -prefix 3dMVM_test -jobs 24
-mask GM2mm_WFU_Mask.nii.gz
-bsVars ‘loneliness+age+gender+hamd’
-qVars ‘age,gender’
-num_glt 3
-gltLabel 1 Loneliness -gltCode 1 ‘loneliness’
-gltLabel 2 HAMD -gltCode 2 ‘hamd’
-gltLabel 3 Lon_by_HAMD -gltCode 3 'loneliness:hamd ’
-dataTable @FIXED.txt

Also, I try to run it and I am having an error in my table (data table is not rectangular) even after “fixing it” with the file_tool

tcsh -x 3dMVM.txt > diary.txt &

3dMVM -prefix 3dMVM_test -jobs 24 -mask GM2mm_WFU_Mask.nii.gz -bsVars loneliness+age+gender+hamd -qVars age,gender -num_glt 3 -gltLabel 1 Loneliness -gltCode 1 loneliness -gltLabel 2 HAMD -gltCode 2 hamd -gltLabel 3 Lon_by_HAMD -gltCode 3 loneliness:hamd -dataTable @FIXED.txt
Read 409 items

** Error:
The content under -dataTable is not rectangular ! 358 7

[1]+ Done tcsh -x 3dMVM.txt > diary.txt

file_tool -infile FIXED.txt -test
FIXED.txt has 0 bad characters

FIXED.txt file type: UNIX

file_tool -infile 3dMVM.txt -test
3dMVM.txt has 0 bad characters

3dMVM.txt file type: UNIX

According this line,

-qVars ‘age,gender’ \

‘age’ and ‘gender’ are the only quantitative variables. First of all, ‘gender’ is not a quantitative variable. Also, do you have other quantitative variables in your model? All those 3 -gltCode lines are not correct. Follow the examples in the 3dMVM help. Consider using the 3dMVM validator: