External Email - Use Caution We still have problems with the analyze described above.
Can you help us moving on?
Attached is our FSGD file (text format "study") and qdec table (dat format
"qdec.table.dat").
Thank you!
Best,
Josefine and Co.
Fra: Josefine Tingdal Taube Danielsen
Sendt: 22. juli 2024 21:24
Til: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Emne: SV: [Freesurfer] Longitudinal analyses using Repeated measures anova
I am sorry.
We have actually been trying to do this instead:
We decided to run mris_glmfit using the guide for longitudinal two stage model.
We are not able to run the first script:
long_mris_slopes --qdec qdec.table.dat \
--meas thickness \
--hemi lh \
--sd /mnt/c/CogimmunStudy/FS \
--do-pc1 --do-label \
--time weeks \
--fwhm 15 \
--qcache fsaverage \
--stack-pc1 lh.testretest.thickness-pc1.stack.mgh \
--isec-labels lh.testretest.fsaverage.cortex.label
The error:
"Traceback (most recent call last): File
"/usr/local/freesurfer/7.4.1/python/scripts/long_mris_slopes", line 1005, in
<module> if fwhm > 0: TypeError: '>' not supported between instances of 'str'
and 'int'"
We have made both a bash and a python script in our subject directory trying to
solve the problem, but without any results.
bash:
#!/bin/bash
# Source the FreeSurfer environment
source /usr/local/freesurfer/SetUpFreeSurfer.sh
# Run the Python script to check the fwhm value
fwhm=$(./check_fwhm.py)
if [ $? -ne 0 ]; then
echo "Error: Invalid fwhm value received from Python script."
exit 1
fi
# Convert fwhm to integer explicitly
fwhm=$((fwhm))
# Check if fwhm is a valid positive integer
if ! [[ $fwhm =~ ^[1-9][0-9]*$ ]]; then
echo "Error: Invalid fwhm value $fwhm. Expected a positive integer greater
than 0."
exit 1
fi
# Run long_mris_slopes with the specified parameters
long_mris_slopes --qdec ./qdec.table.dat \
--meas thickness \
--hemi lh \
--sd /mnt/c/CogimmunStudy/FS \
--do-avg --do-rate --do-pc1 --do-spc --do-stack --do-label \
--time weeks \
--fwhm $fwhm \
--qcache fsaverage \
--stack-pc1 lh.thickness-pc1.stack.mgh \
--isec-labels lh.fsaverage.cortex.label
python:
#!/usr/bin/env python3
import sys
def check_fwhm_value():
# Simulate or retrieve the FWHM value, for example:
fwhm = 15 # Replace with your actual method of retrieving FWHM
# Validate the FWHM value (optional)
try:
fwhm = int(fwhm)
if fwhm <= 0:
raise ValueError("FWHM must be a positive integer greater than 0.")
except ValueError as ve:
print(f"Error: {ve}", file=sys.stderr)
sys.exit(1)
return fwhm
if __name__ == "__main__":
fwhm = check_fwhm_value()
print(fwhm)
Can you guide us to move on with the analysis?
Best,
Josefine
Fra:
freesurfer-boun...@nmr.mgh.harvard.edu<mailto:freesurfer-boun...@nmr.mgh.harvard.edu>
<freesurfer-boun...@nmr.mgh.harvard.edu<mailto:freesurfer-boun...@nmr.mgh.harvard.edu>>
På vegne af Douglas N. Greve
Sendt: 22. juli 2024 15:46
Til: freesurfer@nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>
Emne: Re: [Freesurfer] Longitudinal analyses using Repeated measures anova
Sorry, there is nothing here (ie, no fsgd file, no images). Did you mean to
attached?
On 7/10/2024 5:31 AM, Josefine Tingdal Taube Danielsen wrote:
External Email - Use Caution
Hi Freesurfer support,
I'm using Freesurfer version 7.4.1 and Ubuntu 22_x86_64.
freesurfer-linux-ubuntu22_x86_64-7.4.1-20230614-7eb8460.
I'm trying to run longitudinal analysis using Repeated Measures Anova
(https://surfer.nmr.mgh.harvard.edu/fswiki/RepeatedMeasuresAnova).
My study has 2 groups (MM=patient group and HC=healthy controls) and 2 time
points (1 and 2). I am looking at cortical thickness and cortical volume.
Here is some information:
We have been using these Contrast files:
# Create 1-vs-2 contrast file
echo "0 0 1" > 1-vs-2.mtx
# Create group-effect contrast file
echo "0 1 0" > group-effect.mtx
# Create interaction contrast file
echo "0 1 -1" > interaction.mtx
# Create mean contrast file
echo "1 0 0" > mean.mtxfree
And then followed this:
Preproc using FSGD
mris_preproc --target fsaverage --hemi lh --meas thickness --out
lh.thickness.mgh --fsgd rmanova.fsgd
Smooth data
mri_surf2surf --s fsaverage --hemi lh --fwhm 5 --sval lh.thickness.mgh --tval
lh.thickness.sm05.mgh
Run GLM fit
mri_glmfit --glmdir lh.rmanova --y lh.thickness.sm05.mgh --fsgd rmanova.fsgd
doss \
--C 1-vs-2.mtx --C group-effect.mtx --C interaction.mtx --C mean.mtx \
--surface fsaverage lh
This was our results:
INFO: gd2mtx_method is doss Reading source surface
/mnt/c/CogimmunStudy/FS/fsaverage/surf/lh.white Number of vertices 163842
Number of faces 327680 Total area 65417.000000 AvgVtxArea 0.399269 AvgVtxDist
0.721953 StdVtxDist 0.195472 7.4.1 cwd /mnt/c/CogimmunStudy/FS cmdline
mri_glmfit --glmdir lh.rmanova --y lh.thickness.sm05.mgh --fsgd rmanova.fsgd
doss --C 1-vs-2.mtx --C group-effect.mtx --C interaction.mtx --C mean.mtx
--surface fsaverage lh sysname Linux hostname D56979 machine x86_64 user josdan
FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 0 y
/mnt/c/CogimmunStudy/FS/lh.thickness.sm05.mgh logyflag 0 usedti 0 FSGD
rmanova.fsgd labelmask /mnt/c/CogimmunStudy/FS/fsaverage/label/lh.cortex.label
maskinv 0 glmdir lh.rmanova IllCondOK 0 ReScaleX 1 DoFFx 0 SigUseDouble 1
Creating output directory lh.rmanova Loading y from
/mnt/c/CogimmunStudy/FS/lh.thickness.sm05.mgh ... done reading. INFO:
gd2mtx_method is doss Saving design matrix to lh.rmanova/Xg.dat Computing
normalized matrix Normalized matrix condition is 1 Matrix condition is 3.15
Found 149955 points in label. Pruning voxels by thr: 1.175494e-38 Found 149955
voxels in mask Saving mask to lh.rmanova/mask.mgh Reshaping mriglm->mask...
search space = 74612.577841 DOF = 123 Starting fit and test Fit completed in
0.0220333 minutes Computing spatial AR1 on surface Residual: ar1mn=0.986733,
ar1std=0.003725, gstd=3.501824, fwhm=8.246166 Writing results 1-vs-2 maxvox
sig=-2.06583 F=7.13254 at index 102162 0 0 seed=1720706961 Computing efficiency
group-effect maxvox sig=114.013 F=8295.3 at index 155689 0 0 seed=1720706961
Computing efficiency interaction maxvox sig=106.775 F=6297.15 at index 64378 0
0 seed=1720706961 Computing efficiency mean maxvox sig=133.337 F=17231.6 at
index 155689 0 0 seed=1720706961 Computing efficiency mri_glmfit done
Now our questions:
1. Have we been using the right contrasts?
1. Do the results look realistic?
1. Is it possible to have some more information about results (e.g. summary,
areas being significant, visualization, sig.mgh, t-values, p-values not i
-log10)?
Thank you!
Best,
Josefine Danielsen
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qdec.table.dat
Description: qdec.table.dat
GroupDescriptorFile 1 Title CogimmunStudy Class HC Class MM Variables Input 1002_1 HC Input 1002_2 HC Input 1003_1 HC Input 1003_2 HC Input 1004_1 HC Input 1004_2 HC Input 1005_1 HC Input 1005_2 HC Input 1010_1 HC Input 1010_2 HC Input 1012_1 HC Input 1012_2 HC Input 1021_1 HC Input 1021_2 HC Input 1023_1 HC Input 1023_2 HC Input 1024_1 HC Input 1024_2 HC Input 1025_1 HC Input 1025_2 HC Input 1027_1 HC Input 1027_2 HC Input 1029_1 HC Input 1029_2 HC Input 1030_1 HC Input 1030_2 HC Input 1031_1 HC Input 1031_2 HC Input 1032_1 HC Input 1032_2 HC Input 1033_1 HC Input 1033_2 HC Input 1034_1 HC Input 1034_2 HC Input 1035_1 HC Input 1035_2 HC Input 1036_1 HC Input 1036_2 HC Input 1037_1 HC Input 1037_2 HC Input 1039_1 HC Input 1039_2 HC Input 1041_1 HC Input 1041_2 HC Input 1042_1 HC Input 1042_2 HC Input 1050_1 MM Input 1050_2 MM Input 1052_1 MM Input 1052_2 MM Input 1053_1 MM Input 1053_2 MM Input 1054_1 MM Input 1054_2 MM Input 1056_1 MM Input 1056_2 MM Input 1057_1 MM Input 1057_2 MM Input 1058_1 MM Input 1058_2 MM Input 1059_1 MM Input 1059_2 MM Input 1060_1 MM Input 1060_2 MM Input 1061_1 HC Input 1061_2 HC Input 1062_1 MM Input 1062_2 MM Input 1065_1 MM Input 1065_2 MM Input 1070_1 MM Input 1070_2 MM Input 1071_1 HC Input 1071_2 HC Input 1073_1 HC Input 1073_2 HC Input 1076_1 HC Input 1076_2 HC Input 1081_1 HC Input 1081_2 HC Input 1082_1 HC Input 1082_2 HC Input 1083_1 MM Input 1083_2 MM Input 1084_1 HC Input 1084_2 HC Input 1085_1 MM Input 1085_2 MM Input 1086_1 MM Input 1086_2 MM Input 1088_1 HC Input 1088_2 HC Input 1090_1 HC Input 1090_2 HC Input 1092_1 MM Input 1092_2 MM Input 1093_1 HC Input 1093_2 HC Input 1094_1 HC Input 1094_2 HC Input 1096_1 HC Input 1096_2 HC Input 1098_1 HC Input 1098_2 HC Input 1099_1 MM Input 1099_2 MM Input 1100_1 HC Input 1100_2 HC Input 1101_1 HC Input 1101_2 HC Input 1102_1 MM Input 1102_2 MM Input 1103_1 MM Input 1103_2 MM Input 1104_1 MM Input 1104_2 MM Input 1105_1 HC Input 1105_2 HC Input 1106_1 HC Input 1106_2 HC Input 1109_1 HC Input 1109_2 HC Input 1110_1 HC Input 1110_2 HC Input 1111_1 HC Input 1111_2 HC
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