Last updated: 2020-10-22
Checks: 7 0
Knit directory: Viso-NODDI-in-CUD/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20201022)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 372d376. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Unstaged changes:
Deleted: analysis/DTI-FWE.Rmd
Deleted: analysis/DTI-preprocessing.Rmd
Deleted: analysis/T1w-preprocessing.Rmd
Modified: analysis/_site.yml
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Workflow.Rmd
) and HTML (docs/Workflow.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 372d376 | JalilRT | 2020-10-22 | Publish the initial files for Viso-NODDI-in-CUD |
To reproduce the results, please follow the following flow and using codes we provided.
#! /bin/bash
DIR=$PWD #Base directory path
List=${DIR}/Subjects.txt #List of Subjects
for Sub in `cat ${List}`
do
echo ${Sub}
T1_Nifti=${DIR}/NIFTIs/${Sub}/sub-${Sub}_T1w.nii.gz #Raw T1 structural data
mkdir -p ${DIR}/Freesurfer/${Sub}
export SUBJECTS_DIR=${DIR}/Freesurfer
mkdir -p ${SUBJECTS_DIR}/${Sub}/mri/orig
rm ${SUBJECTS_DIR}/${Sub}/scripts/IsRunning.lh+rh
mri_convert ${T1_Nifti} ${SUBJECTS_DIR}/${Sub}/mri/orig/001.mgz #converting nifti to mgz
cd ${SUBJECTS_DIR}
recon-all -all -s ${Sub} #perform T1 preprocessing and Compute Parcellation of whole brain
mri_convert ${SUBJECTS_DIR}/mri/brain.mgz ${SUBJECTS_DIR}/mri/nifti/brain.nii.gz
mri_convert ${SUBJECTS_DIR}/mri/aparc+aseg.mgz ${SUBJECTS_DIR}/freeLabels.nii.gz
done
#! /bin/bash
FSLOUTPUTTYPE=NIFTI_GZ
export FSLOUTPUTTYPE
DIR=$PWD #Base directory path
DTI_dir=${DIR}/NIFTIs #Raw data Directory
index=${DIR}/Index.txt #acqp indexor each DWI volume
acqp=${DIR}/aqcp_Param.txt #DWI aqcusition parametes
List=${DIR}/Subjects.txt
for Sub in `cat ${List}`
do
echo ${Sub}
mkdir -p ${DIR}/BedpostX/${Sub}
Sub_dir=${DIR}/BedpostX/${Sub}
cp -rf ${DTI_dir}/${Sub}/sub-${Sub}_dwi.nii.gz ${Sub_dir}/${Sub}_DWI.nii.gz
DWI=${Sub_dir}/${Sub}_DWI.nii.gz
bvc=${DTI_dir}/${Sub}/sub-${Sub}_dwi.bvec
bvl=${DTI_dir}/${Sub}/sub-${Sub}_dwi.bval
echo "Distortion Correction......"
mkdir -p ${Sub_dir}/${Sub}/dwi_volumes
mkdir -p ${Sub_dir}/${Sub}/epi-1_volumes
fslsplit ${Sub_dir}/${Sub}/sub-${Sub}_dwi.nii.gz ${Sub_dir}/${Sub}/dwi_volumes/sub-${Sub}_dwi_x -t
fslsplit ${Sub_dir}/${Sub}/sub-${Sub}_run-01_epi.nii.gz ${Sub_dir}/${Sub}/epi-1_volumes/sub-${Sub}_run-01_epi_x -t
cp -rf ${Sub_dir}/${Sub}/dwi_volumes/sub-${Sub}_dwi_x0000.nii.gz ${Sub_dir}/${Sub}/dwi_volumes/sub-${Sub}_no_diff_PA.nii.gz
cp -rf ${Sub_dir}/${Sub}/epi-1_volumes/sub-${Sub}_run-01_epi_x0000.nii.gz ${Sub_dir}/${Sub}/epi-1_volumes/sub-${Sub}_no_diff_AP.nii.gz
fslmerge -t ${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff.nii.gz ${Sub_dir}/${Sub}/dwi_volumes/sub-${Sub}_no_diff_PA.nii.gz ${Sub_dir}/${Sub}/epi-1_volumes/sub-${Sub}_no_diff_AP.nii.gz
topup --imain=${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff.nii.gz --datain=${Sub_dir}/${Sub}/aqcp_PAAP.txt --out=${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected_1 --iout=${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected
fslmaths ${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected -Tmean ${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected
bet ${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected ${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected_brain -f 0.45 -m -R
echo "Eddy-corraction and bvec rotation..."
eddy_openmp --imain=${Sub_dir}/${Sub}_SS --mask=${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected_brain_mask --acqp=${acqp} --index=${index} --topup=${Sub_dir}/${Sub}/${Sub}_PA-AP_no_diff_corrected_1 --bvecs=${bvc} --bvals=${bvl} --out=${Sub_dir}/${Sub}_eddy
echo "Brain Mask Creation..."
bet ${Sub_dir}/${Sub}_eddy ${Sub_dir}/brain_output -f 0.15 -m -F -R
fslmaths ${Sub_dir}/${Sub}_eddy -mul ${Sub_dir}/brain_output_mask ${Sub_dir}/${Sub}_eddy_SS
echo "Tensor Modeling..."
dtifit -k ${Sub_dir}/${Sub}_eddy_ss.nii.gz -o ${Sub_dir}/${Sub}_DWI -m ${Sub_dir}/brain_output_mask -r ${Sub_dir}/${Sub}_eddy.eddy_rotated_bvecs -b ${bvl}
cp -rf ${bvl} ${Sub_dir}/${Sub}/sub-${Sub}_dwi_eddy_SS.bval
cp -rf ${Sub_dir}/${Sub}_eddy.eddy_rotated_bvecs ${Sub_dir}/${Sub}/sub-${Sub}_dwi_eddy_SS.bvec
#! /bin/bash
DIR=$PWD
mkdir ${DIR}/freeROI_FW_1
mkdir ${DIR}/jhuTract_FW_1
mkdir ${DIR}/jhuWM_FW_1
mkdir ${DIR}/JHUroi_FW_1
List=${DIR}/Subjects.txt #List of Subjects
for Sub in `cat ${List}`
do
echo ${i}
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_fiso -M >> ${DIR}/OpenNeuro_FW_mean.txt #Compute mean FW for Wholebrain
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_ficvf -M >> ${DIR}/OpenNeuro_ficv_mean.txt #Compute mean icvf for Wholebrain
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_odi -M >> ${DIR}/OpenNeuro_odi_mean.txt #Compute mean odi for Wholebrain
echo "extract FW for Structural Rois"
antsRegistrationSyN.sh -f ${DIR}/${i}/FWE_DTI/${i}_DWI_FA.nii.gz -m ${DIR}/Freesurfer/${Sub}/mri/nifti/brain.nii.gz -t s -o ${DIR}/${i}/FWE_DTI/${i}_DWI2T1 -n 8 #Compute Registration between DWI and Structural bain
antsApplyTransforms -i ${DIR}/Freesurfer/${Sub}/freeLabels.nii.gz -r ${DIR}/${i}/FWE_DTI/${i}_DWI_FA.nii.gz -o ${DIR}/${i}/FWE_DTI/${i}_T12DWI.nii.gz -t [${DIR}/${i}/FWE_DTI/${i}_DWI2T10GenericAffine.mat, 0] -t ${DIR}/${i}/FWE_DTI/${i}_DWI2T11Warp.nii.gz -n NearestNeighbor
list=/home/scmia/Documents/OpenNeuro/86_labels.txt # list of Desikan Structural brain rois (68 cortical + 18 sub cortical)
mkdir ${DIR}/${i}/labelMASKS_DWI
for j in `cat ${list}`;do
echo $j
fslmaths ${DIR}/${i}/FWE_DTI/${i}_T12DWI.nii.gz -uthr ${j} -thr ${j} ${DIR}/${i}/labelMASKS_DWI/${j}_mask_dwi.nii.gz
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_fiso -k ${DIR}/${i}/labelMASKS_DWI/${j}_mask_dwi.nii.gz -M >> ${DIR}/freeROI_FW_1/${j}_FW.txt #Compute mean FW for structure rois
done
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_fiso -k ${DIR}/${i}/labelMASKS_DWI/CC_mask_dwi.nii.gz -M >> ${DIR}/freeROI_FW_1/CC_FW.txt
echo "extract FW for JHU Rois"
antsRegistrationSyN.sh -f ${DIR}/${i}/FWE_DTI/${i}_DWI_FA.nii.gz -m ${FSLDIR}/data/atlases/JHU/JHU-ICBM-FA-1mm.nii.gz -t s -o ${DIR}/${i}/FWE_DTI/${i}_DWI2JHU -n 8 # comupute registration between
antsApplyTransforms -i ${FSLDIR}/data/atlases/JHU/JHU-ICBM-tracts-maxprob-thr25-1mm.nii.gz -r ${DIR}/${i}/FWE_DTI/${i}_DWI_FA.nii.gz -o ${DIR}/${i}/FWE_DTI/${i}_JHU2DWI.nii.gz -t [${DIR}/${i}/FWE_DTI/${i}_DWI2JHU0GenericAffine.mat, 0] -t ${DIR}/${i}/FWE_DTI/${i}_DWI2JHU1Warp.nii.gz -n NearestNeighbor #apply registration to JHU tract atlas
antsApplyTransforms -i ${FSLDIR}/data/atlases/JHU/JHU-ICBM-labels-1mm.nii.gz -r ${DIR}/${i}/FWE_DTI/${i}_DWI_FA.nii.gz -o ${DIR}/${i}/FWE_DTI/${i}_JHUlabels2DWI.nii.gz -t [${DIR}/${i}/FWE_DTI/${i}_DWI2JHU0GenericAffine.mat, 0] -t ${DIR}/${i}/FWE_DTI/${i}_DWI2JHU1Warp.nii.gz -n NearestNeighbor #apply registration to JHU wm atlas
mkdir ${DIR}/${i}/FWE_DTI/JHU_dwi
for k in {1..20} ; do
echo ${k}
fslmaths ${DIR}/${i}/FWE_DTI/${i}_JHU2DWI.nii.gz -uthr ${k} -thr ${k} ${DIR}/${i}/FWE_DTI/JHU_dwi/${k}_jhu2dwi_tract.nii.gz #Extract JHU tract rois individual
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_fiso -k ${DIR}/${i}/FWE_DTI/JHU_dwi/${k}_jhu2dwi_tract.nii.gz -M >> ${DIR}/jhuTract_FW_1/${k}_tract_FW.txt #Compute mean FW for JHU tract rois
done
mkdir ${DIR}/${i}/FWE_DTI/JHU_WM_dwi
for k in {1..48} ; do
echo ${k}
fslmaths ${DIR}/${i}/FWE_DTI/${i}_JHUlabels2DWI.nii.gz -uthr ${k} -thr ${k} ${DIR}/${i}/FWE_DTI/JHU_WM_dwi/${k}_jhuWM2dwi_tract.nii.gz #Extract JHU wm rois individual
fslstats -t ${DIR}/${i}/${i}_DWI_NoddiFit_fiso -k ${DIR}/${i}/FWE_DTI/JHU_WM_dwi/${k}_jhuWM2dwi_tract.nii.gz -M >> ${DIR}/jhuWM_FW_1/${k}_WMroi_FW.txt #Compute mean FW for JHU wm rois
done
done
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=es_MX.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=es_MX.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=es_MX.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.11 whisker_0.4 knitr_1.30
[5] magrittr_1.5 R6_2.4.1 rlang_0.4.8 stringr_1.4.0
[9] tools_3.6.3 xfun_0.18 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 rprojroot_1.3-2 yaml_2.2.1 digest_0.6.26
[17] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.4 promises_1.1.1 fs_1.5.0 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.5 stringi_1.5.3 compiler_3.6.3
[29] pillar_1.4.6 backports_1.1.10 httpuv_1.5.4 pkgconfig_2.0.3