DTI preprocessing, statistical and cluster analysis

Double-check if your native and original images are in FLOAT32 and oriented with the MNI.

- Convert to float-

fslmaths dwidata.nii.gz dwi.nii.gz -odt float

DWI Denoising:

dwidenoise -datatype float32 -noise level_noise.nii.gz [IN] [OUT_denoised]

dwidenoise -datatype float32 -noise level_noise.nii.gz dwi.nii.gz dwi_denoised.nii.gz

mrdegibbs [OUT_denoised] [OUT_Gibbs]

mrdegibbs dwi_denoised.nii.gz dwi_Gibbs.nii.gz

need brain – bet doesn’t work as well as dwi2mask

dwi2mask -fslgrad bvecs.txt bvals.txt dwi_Gibbs.nii.gz temp_brain_mask.nii.gz

Run ‘nodif_brain_mask.sh’ script [e.g., bash nodif_brain_mask.sh dwi_Gibbs.nii.gz 4214_52 temp_brain_mask.nii.gz ] to get temporary nodif brain masks for eddy.

Rename both of these nodif files with the prefix ‘dwi_Gibbs_’ e.g., ‘dwi_Gibbs_brain_mask’ for eddy. Delete the ‘temp_brain’ file.

Run eddy correction and get rotated vector file

eddy --imain=dwi_Gibbs.nii.gz --mask=dwi_Gibbs_brain_mask.nii.gz --index=index.txt --acqp=acqparams.txt --bvecs=bvecs.txt --bvals=bvals.txt --fwhm=0 --flm=quadratic --slm=linear --out=eddy_unwarped_images --data_is_shelled

Nodif and Brain extraction on the data.nii.gz

fslroi data.nii.gznodif_data0 1

dwi2mask -fslgrad eddy_unwarped_images.eddy_rotated_bvecs.txt bvals.txt data.nii.gz temp_brain_mask.nii.gz

Run ‘nodif_brain_mask.sh’ script [e.g., bash nodif_brain_mask.sh data.nii.gz 4214_52 temp_brain_mask.nii.gz ] output is ‘nodif_brain_mask.nii.gz’ and ‘nodif_brain.nii.gz’ rename and add ‘data’ in front, so ‘data_nodif_brain’. Delete ‘temp_brain’ file.

Bias Field correction (via ANTs ):

dwibiascorrect ants data.nii.gz data_bias.nii.gz -fslgrad eddy_unwarped_images.eddy_rotated_bvecs.txt bvals.txt -mask data_nodif_brain_mask.nii.gz -bias bias_image.nii.gz -ants.b [100,3]

Upsample DWI images for better coregistration with MNI template

mrgrid -vox 1.25 data_bias.nii.gz regrid data_bias_HR.nii.gz

Fit tensor model

dtifit -k data_bias_HR.nii.gz -o DTI_map_w-linear_ -m nodif_brain_mask.nii.gz -r eddy_unwarped_images.eddy_rotated_bvecs.txt -b bvals.txt -w

Optional: create bash script to generate group template

Copy all the nodif_brain.nii.gz images into one folder and change directory to it. Then run:

*bash buildtemplateparallel.sh -d 3 -o template -c 0 -r 1 -n 0 -i 3 .nii.gz

WarpImageMultiTransform 3 [in: FA map in native space] [out: FA map in template space] -R templatetemplate.nii.gz [warp file] [affine file]

At this point all participant FA maps should be in template space. Create a 4D (x,y,z + time) file with all your maps:

Insert all your FA in template space in one folder and run: *fslmerge -t [output] .nii.gz gz

Create then apply mask from all subjects

fslmaths all_FA.nii.gz -max 0 -Tmin -bin mean_mask -odt char

fslmaths all_FA.nii.gz -mas mean_mask all_FA.nii.gz

Create a mean of the FA over-time and create white matter mask (thresholded at FA>0.20):

fslmaths all_FA -Tmean mean_FA

fslmaths mean_FA -thr 0.20 -bin WM_mask

Apply your mask to your smoothed maps:

fslmaths all_FA_smooth -mas mean_mask all_FA_smooth

At this point you should have a FA map 'all_FA.nii.gz' for all subjects.

Create linear regression script (that applies model voxel-by-voxel) using the LM.m function in MATLAB by using:

LM (file_ID1, raw_image_subject, mask)

e.g., LM (covariates.txt, all_FA_smooth.nii, WM_mask.nii)

Resulting files will be the FDR-corrected p-value and tstat maps for each variable.

Example is for first behavioral variable 'score_1'

3dcalc -ascore_1_FDR.nii-expr 'a*1' -prefixscore_1_FDR_corrected.nii.gz

3dcalc -a score_1_tStat.nii -expr 'a*1' -prefix score_1_tStat_corrected.nii.gz

Split scores tStat into positive and negative (this provides you with positive and negative correlations) :

Fslmathsscore_1_tStat_corrected.nii.gz -thr 0 positive_score1.nii.gz

Fslmathsscore_1_tStat_corrected.nii.gz -uthr 0 negative_score1.nii.gz

*For negative images, need to multiply by -1

Fslmaths negative_score1.nii.gz -mul -1 negative_score1.nii.gz

Get FDR-corrected p-value clusters that are less than 0.01 and that are at least 100 voxels in size

3dClusterize -nosum -1Dformat -inset score_1_FDR_corrected.nii.gz -idat 0 -ithr 0 -NN 2 -clust_nvox 100 -1sided RIGHT_TAIL 0.99 -pref_map Clust_mask_motor.nii.gz

fslmaths Clust_mask_motor.nii.gz -bin Clust_mask_motor.nii.gz

Coregister the template to MNI:

antsRegistrationSyN.sh -d 3 -m /Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/group_template/templatetemplate.nii.gz -f /usr/local/fsl/data/standard/MNI152_T1_1mm_brain.nii.gz -o /Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI -t s

Check that ROIs are Binarized (use nearest neighbor command when the images are binarized). Behavioral variables are 'score_1' and 'motor'

Transform clusters to MNI:

antsApplyTransforms -d 3 -i Clust_mask_motor.nii.gz -o Clust_mask_motor_MNI.nii.gz -r/usr/local/fsl/data/standard/MNI152_T1_1mm_brain.nii.gz -t [/Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI1Warp.nii.gz,0]  -t [/Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI0GenericAffine.mat,0] -n NearestNeighbor

Transform scores tstat (positive and negative) to MNI space:

antsApplyTransforms -d 3 -ipositive_score1.nii.gz-opositive_score1_MNI.nii.gz -r/usr/local/fsl/data/standard/MNI152_T1_1mm_brain.nii.gz -t [/Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI1Warp.nii.gz,0]  -t [/Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI0GenericAffine.mat,0]

antsApplyTransforms -d 3 -inegative_score1.nii.gz-onegative_score1_MNI.nii.gz -r/usr/local/fsl/data/standard/MNI152_T1_1mm_brain.nii.gz -t [/Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI1Warp.nii.gz,0]  -t [/Users/syschaef/Desktop/4214_DTI/MRtrix_preproc/template_to_MNI/templatetemplate_to_MNI0GenericAffine.mat,0]

Mask the tstat scores with significant cluster files:

Positive:

fslmathspositive_score1_MNI.nii.gz -mas Clust_mask_motor_MNI.nii.gzpositive_score1masked_MNI.nii.gz fslmathspositive_score1masked_MNI.nii.gz -bin Clust_mask_motor_positive_MNI.nii.gz z

Negative :

fslmathsnegative_score1_MNI.nii.gz -mas Clust_mask_motor_MNI.nii.gznegative_score1masked_MNI.nii.gz fslmathsnegative_score1masked_MNI.nii.gz -bin Clust_mask_motor_negative_MNI.nii.gz z

Custom code extracted significant cluster locations from the JHU white matter atlas, but use the atlas of your choice and identify clusters by using ' fslmaths ' (see fslwiki for tutorial)