Work flow example for voxel wise mediation analyses

Voxel-wise Mediation Analyses

1) Preprocess a 4D volume. For instance using TBSS.

2) Load 4D volume into tfce_mediation

tfce_mediation step0-voxel -i 4dvolume.nii.gz mask.nii.gz

Explanation: Load the 4D volume and its binarized mask into tfce_mediation

Inputs:

• 4Dvolume.nii.gz (3D NiFti volumes for all subjects. e.g. for TBSS output use all_FA_skeletonised.nii.gz)
• mask.nii.gz (binarized brain makes. e.g. for TBSS output use mean_FA_skeleton_mask.nii.gz)

Outputs:

• python_temp/*.npy (Numpy memory mapped objects used for all voxel analyses/actions)

3) Remove the effect of covariates on regressors for mediation analysis

tm_tools regressor-tools -i predictor_variable.csv covariates.csv -r -s
tm_tools regressor-tools -i dependent_variable.csv covariates.csv -r -s

Explanation: For mediation analyses using TFCE_mediation, the effect of covariates should be removed from all regressors of interest. ‘regressor-tools -r’ returns the residuals after regressing out the effect of the covariates. In the next step (voxel-step1-????), the covariates will regressed out from the 4D_image.

Inputs:

• ?????_variable.csv (dummy-coded regressors of interest)
• covariates.csv (dummy-coded regressors of no interest)

Outputs:

• ?????_resids.csv (residuals of the variable of interest)
• covariates_std_dm.csv (unit variance and demean covariates file)

4) Mediation analysis

tfce_mediation step1-voxel-mediation -i predictor_resids.csv dependent_resids.csv -c covariates_std_dm.csv -m M

Explanation: Mediation analyses using TFCE_mediation in which the ‘predictor_variable’ is the independent variable, the 4d_image is the mediator, and the ‘dependent_variable’ is the dependent variable.

Inputs:

• ?????_resids.csv (residuals of the variable of interest)
• covariates_std_dm.csv (unit variance and demean covariates file)

Outputs:

• output_med_M/SobelZ_M.nii.gz (the untransformed Sobel Z-statistics of the indirect effect)
• output_med_M/SobelZ_M_TFCE.nii.gz (the TFCE transformed Z-statistics)
• output_med_M/maxTFCE_contrast_value.csv (the maximum TFCE values of the statistics image)

Note, different mediation models can be performed.

5) Permuation Testing (Randomization)

tfce_mediation step2-randomise-parallel --voxel -n 10000 -m M -p 8

Explanation: Permutation testing using parallel processing. This script is a wrapper to make n=200 permution chunks, and parallelizing processing of the voxel-mediation-randomise scipt for each chunk (in this case). i.e., each chunk to a different processor.

Input:

• M (mediation type)
• 10000 (total number of permutations)
• 8 (number of cores to used with GNU parallel)

Output:

• output_med_M/perm_SobelZ/perm_Zstat_TFCE_maxVoxel.csv (the maximum TFCE value among all voxels of the entire cortex for each permutation. It is used to correct for family-wise error)

6) Apply family-wise error rate correction

cd output_med_M
tfce_mediation voxel-calculate-fwep -i  SobelZ_M_TFCE.nii.gz  perm_SobelZ/perm_Zstat_TFCE_maxVoxel.csv

Explanation: Calculate 1-P(FWE) voxel image from max TFCE values from randomisation.

Input:

• SobelZ_M_TFCE.nii.gz (TFCE transformed Z-statistic voxel image)
• perm_Zstat_TFCE_maxVoxel.csv (List with maximum TFCE values)

Output:

• SobelZ_M_TFCE_FWEcorrP.nii.gz (1-P(FWE) corrected image)

7) View results

Use your favourite viewer. e.g., tmi_viewer, fslview, mricron, MRIcroGL, etc.