--- That's fine. Commonly, we report t value along with our results. Do you control for anything in these two groups. Are the controls matched with sex ang age? The demographic information matters for white matter analysis according to my experience.

2)Can you please confirm if is correct the fact that:

In tbss_tstat1, the intensity value is among 0-3, and FA of controls is > significant higher than patients in the voxels where intensity is between 0-0.05 while in tbss_tstat2, the intensity value is also among 0-3, FA of > patients is significant higher than > controls in the voxels where intensity is between 0-0.05?

--- Intensity in tbss_tstat1 should be your t values. 0-0.05 is too small to me. for t distribution, t>2.353 for p< 0.05 with d.f. 3. Are you sure that you look at the right image? The p values should be in 0.95-1 range for significance. (due to the FSL setting).

I have 30 controls and 32 patients and the way I defined up my matrices is as you can see in the attached files: design.mat and design.con (I saved them for you in a .txt format so you can open them). While checking with imglob *_FA.*
I saw first the controls files then the patients files so in the desing.mat I wrote first my controls with 0 in the first column and 1 in the second column and the I added the patients with 1 and the 0 in the second column.

--- Sorry, I did not see the design matrix that you mentioned in the mail. (no .txt file) You can send to me at [log in to unmask] instead of FSL mailing list. Maybe the attachments are not allowed here.

design_ttest2 design 30 32

randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d design.mat -t design.con -n 5000 --T2 -V

I don't understand why my matrix changed. If I open now after randomise my design. mat I see first patients with 1 and 0 the controls with 0 and 1. Can you please explain how this is possible?

4) If I visualize the UNTHRESHOLDED t-stats results what's the meaning of the values that comes after -b:

fslview $FSLDIR/data/standard/MNI152_T1_1mm mean_FA_skeleton -l Green -b 0.2,0.8 tbss_tstat1 -l Red-Yellow -b 3,6 tbss_tstat2 -l Blue-Lightblue -b 3,6

--- After -b, it is the threshold range that you use for your mean_FA_skeleton so that the messy small branches will go away only the main skeleton will be displayed. 0.2-0.8 According to your data, you can change that. If you patients have significantly lower FA, you can use 0.15-0.6 etc.

--- From this command, it seems to me that your tstat values are in the range of 3 to 6. That makes more sense than 0-0.05 you mentioned earlier.

I suppose is a threshold only for data visualization or is something else. I saw some people use 1.3,3 instead of 3, 6. Can you pleases explain? Which one is the best to be used?

--- This is really for display. Arbitrary numbers. That's why I wrote the commands in the former email for you to mask your t-stat image. Then you can use #fslstats tbss_tstat1_masked -R and see the new t value range. Use that for your display.

fslview $FSLDIR/data/standard/MNI152_T1_1mm mean_FA_skeleton -l Green -b 0.2,0.7 tbss_tfce_corrp_tstat1 -l Red-Yellow -b 0.9,1 the only clusters which survived is for a 0.9 threshold. In this case I got two clusters with Z-max = 0.913.

Is this value equal with 1-p so in this case the p value for the two clusters will be 0.087? How is possible to get the same p value for both clusters? I suppose this clusters are not significant statistically since >0.05. Am I right?

--- Yes. it is not significant. p>0.05. Your corrected p value. You can report uncorrected p value. Personally, I am skeptical about this whole multiple comparison thing. Different p value for different individual due to the multiple comparison correction. You can use AFNI commands like 3dttest++ to do the statistics analysis. It is parametric method instead of non-parametric. 3dttest++ use FDR for multiple comparison correction. Or you can try to use fslstats to look at your raw data and have a basic feeling about the data. Then, you probably will know why it is not significant. Randomise is non-parametric (not sensitive) compared with parametric method. You have n>30 for each group. It should be safe to use parametric methods. Also, according to Dr. Smith's paper, the skeletonised images follow Gaussian distribution. read this paper: doi: 10.1016/j.eplepsyres.2011.02.001 It stated pros and cons of TBSS. They used it for epilepsy patients.

From: [log in to unmask]
Subject: Re: [FSL] t statistics
To: [log in to unmask]

Hi Antonella,

From this command you wrote, you will have the t-map as well. Look at the folder. It should have files named tbss_tstats... Depends on your contrasts ...

For eg, if you have two contrasts (1,2), you will see six files after you run: randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d design.mat -t design.con -n 5000 --T2 -V

tbss_tfce_corrp_tstat1.nii.gz -- corrected p value

tbss_tfce_corrp_tstat2.nii.gz -- corrected p value

tbss_tfce_p_tstat1.nii.gz -- uncorrected p value

tbss_tfce_p_tstat2.nii.gz -- uncorrected p value for contrast 2

tbss_tstat1.nii.gz -- t value for contrast 1 -- that's what you wanted

tbss_tstat2.nii.gz -- t value for contrast 2

you can use following command lines to get the significant t map:

steps

(1) fslmaths tbss_tfce_corrp_tstat1.nii.gz -thr 0.95 tbss_tfce_corrp_tstat1_th95

(2) fslmaths tbss_tstat1.nii.gz -mas tbss_tfce_corrp_tstat1_th95 tbss_tstat1_masked

If you want to know the x,y,z for your corrected map.

(3) cluster -i tbss_tfce_corrp_tstat1.nii.gz -t 0.95 --mm

Hope these are helpful to you.

Cheers,

Yingying

===========================================================
Yingying Wang, Graduate Student, Biomedical Engineering, University of Cincinnati.

Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center.

MLC 5033, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, United States.

O: 513-636-3495 C: 513-833-7448 Email: [log in to unmask]

Blog: http://wang2yg.blogspot.com Homepage: http://homepages.uc.edu/~wang2yg

===========================================================

>>> antonella torok <[log in to unmask]> 11/3/2011 4:44 PM >>>

Dear FSL experts,

Is theer a way to get the t statistics values in significant clusters from my TFCE results? For TFCE I was using the following command: design_ttest2 design 30 32

randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d design.mat -t design.con -n 5000 --T2 -V

Thank you for your time and your valuable support.

Best regards,
Claudia

------------------------------
Antonella Kis, PhD
Research Fellow
Department of Life Sciences
University of Toronto at Scarborough
Phone: 416-208-4869
Fax: 416-287-7642