Analysis of Three Groups: 3dLME or 3dMEMA

AFNI Message Board
1

I am analyzing 3 groups with 2 covariates. Can I use 3dMEMA or 3dLME for this analysis? If yes, which is more suitable for this analysis, 3dMEMA or 3dLME?

2

At least three options are available for your data structure: 3dMEMA, 3dttest++ and 3dMVM. The last one is likely the easiest to set up.

3

Thanks Gang. I will examine my three options.
Lisa

4

Thanks again, Gang. I used the following 3dMVM code to analyze the three groups.
3dMVM -prefix Test3 -jobs 24
-bsVars SDS_deficit
-qVars “Age,Sex,RMS_rel_av”
-num_glt 3
-gltLabel 1 ‘nds-ds’ -gltCode 1 ‘SDS_deficit: +10 -11’
-gltLabel 2 ‘nds-hc’ -gltCode 2 ‘SDS_deficit: +10 -12’
-gltLabel 3 ‘ds-hc’ -gltCode 3 ‘SDS_deficit: +11 -12’
-dataTable
Subj SDS_deficit Age Sex RMS_rel_av InputFile
sub-control01 2 28 2 0.0023 sub-control01.ses-01.FA.warped_5mm.resample.rotate.nii
sub-control02 2 23 1 0.0061 sub-control02.ses-01.FA.warped_5mm.resample.rotate.nii \ …
Attached is my output image, Test3+tlrc. How do I view only the white matter of this image?
Thanks,
Lisa

Test3+tlrc_image.png
Test3+tlrc_image.png1000×374 255 KB

5

Hello,
I used 3dSeg to segment the brain volume into tissue classes from my 3dMVM output in the previous message. See the code below.
3dSeg -anat Test3.nii -mask AUTO
-classes ‘CSF ; GM ; WM’
-bias_fwhm 25 -mixfrac UNI -main_N 5
-blur_meth BFT
-prefix Test3WM

I received the following errors and warnings. How do I fix these errors and warnings?
*+ WARNING: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*+ WARNING: ss.cp+tlrc[1] scale to shorts mean misfit error = 15.6% – ** Take Care

  • a) Numerical precision has been lost when truncating results
    from 32-bit floating point to 16-bit integers (shorts).
  • b) Consider writing datasets out in float format.
    In most AFNI programs, use the ‘-float’ option.
  • c) This warning is a new message, but is an old issue
    that arises when storing results in an integer format.
  • d) Don’t panic! These messages likely originate in peripheral
    or unimportant voxels. They mean that you must examine your output.
    “Assess the situation and keep a calm head about you,
    because it doesn’t do anybody any good to panic.”
    ++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    *+ WARNING: ss.cp+tlrc[3] scale to shorts mean misfit error = 16.7% – ** Take Care
    *+ WARNING: ss.cp+tlrc[5] scale to shorts mean misfit error = 17.5% – ** Take Care
    *+ WARNING: ss.cp+tlrc[7] scale to shorts mean misfit error = 17.8% – ** Take Care
    – Error SUMA_Class_stats (SUMA_SegFunc.c:5474):
    Bad parameters for class CSF
    – Error SUMA_Class_stats (SUMA_SegFunc.c:5474):
    Bad parameters for class GM
    – Error SUMA_Class_stats (SUMA_SegFunc.c:5474):
    Bad parameters for class WM
    Bad Stats At SUMA_Class_stats() exit:
    Class Key num mean stdv meanL stdvL mix init.mix
    CSF 1 204 -nan 0 -nan 0 0.0001 0.333333
    GM 2 204 -nan 0 -nan 0 0.0001 0.333333
    WM 3 204 -nan 0 -nan 0 0.0001 0.333333
    – Error SUMA_SegEngine (SUMA_3dSeg.c:637):
    Failed in class stats
    – Error 3dSeg (SUMA_3dSeg.c:1019):
    Failed in SUMA_SegEngine

Thanks,
Lisa

6

I used 3dSeg to segment the brain volume into tissue classes from my 3dMVM output in the previous message

I don’t know much about segmentation, but I wonder you might have to segment the white matter based on anatomical data, not the modeling output from functional data.

7

How do I interpret the 3dMVM output image from my message on November 6?

8

You may want to remove any NaNs (“Not a Number”) before running 3dSeg with float_scan or set AFNI_FLOATSCAN to YES in your .afnirc file, which it typically is already as default.

9

Thanks Daniel and Gang. It is working now. I was using the wrong niftii file.