Re: [Freesurfer] Longitudinal analyses using Repeated measures anova

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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-bounces@nmr.mgh.harvard.edumailto:freesurfer-bounces@nmr.mgh.harvard.edu <freesurfer-bounces@nmr.mgh.harvard.edumailto:freesurfer-bounces@nmr.mgh.harvard.edu> På vegne af Douglas N. Greve Sendt: 22. juli 2024 15:46 Til: freesurfer@nmr.mgh.harvard.edumailto: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:

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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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