Difference between revisions of "Cross-sectional TBSS"

Revision as of 15:54, 24 August 2015

The nipype script to perform cross-sectional TBSS is called perform_dti_tbss_cross_sectional.py

This pipeline is to replicate the work presented in the following paper: Keihaninejad, S., Ryan, N. S., Malone, I. B., Modat, M., Cash, D., Ridgway, G. R., Zhang, H., et al. (2012). The importance of group-wise registration in tract based spatial statistics study of neurodegeneration: a simulation study in Alzheimer's disease. PloS one, 7(11), e45996. doi:10.1371/journal.pone.0045996

The script help returns:

usage: perform_dti_tbss_cross_sectional.py [-h] --input_img input_img
                                           [input_img ...] [--mat design_mat]
                                           [--con design_con] [-o output_dir]
                                           [--output_pre prefix]
                                           [--no_randomise] [-g]

Cross-section TBSS pipeline (as described in 10.1371/journal.pone.0045996).
The required input are the tensor images and the design matrix and contrast.
The input images can be specified using the --input_img argument. The design
matrix (--mat) and contrast (--con) can be generated as described on this
page: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM with the Glm_gui executable.
Note that the file order has to match the design matrix. The user also need to
specify an output directory (--output_dir) where all the result files will be
saved. Using the --g argument you can generate the pipeline graph without
running the actual pipeline.

optional arguments:
  -h, --help            show this help message and exit
  --input_img input_img [input_img ...]
                        List of Nifti file(s) to include in the analysis
  --mat design_mat      Design matrix file to be used by randomised on the
                        skeletonised FA maps. Required to run the statistical
                        analysis
  --con design_con      Design contrast file to be used by randomised on the
                        skeletonised FA maps. Required to run the statistical
                        analysis
  -o output_dir, --output_dir output_dir
                        Output directory where to save the results
  --output_pre prefix   Output result prefix (default='tbss_')
  --no_randomise        Do not perform the randomise test on the skeletonised
                        FA maps (permutation test)
  -g, --graph           Print a graph describing the node connections and exit

As an input, the script expects several DTI images, e.g. /folder/DTI_img0.nii.gz, /folder/DTI_img1.nii.gz, ..., /folder/DTI_imgN.nii.gz. The script also expect the design matrix and design contrast file generated as described in [1] with the Glm_gui executable.

The generated files will be store in the folder specified by the --output_dir option.

By default, all generated files will have the tbss_ prefix. This prefix can be altered using the --output_pre option.

A typical example application can be:

python perform_dti_tbss_cross_sectional.py \
   --input_img /folder/DTI_img0.nii.gz /folder/DTI_img1.nii.gz [...] /folder/DTI_imgN.nii.gz \
   --output_dir /folder/path/output_folder \
   --output_pre new_prefix_ \
   --mat /folder/path/design_matrix.mat \
   --con /folder/path/design_contrast.con

Note that if the --no_randomise option is specified the --mat and --con options do not required to be specified. In this case, a typical command can be:

python perform_dti_tbss_cross_sectional.py \
   --input_img /folder/DTI_img0.nii.gz /folder/DTI_img1.nii.gz [...] /folder/DTI_imgN.nii.gz \
   --output_dir /folder/path/output_folder \
   --output_pre new_prefix_ \
   --no_randomise

The output, if the design contrast and matrix are specified, encompass:

• <prefix>mean_fa.nii.gz -- Mean FA map generated from the input DTI using a groupwise approach with DTI-TK
• <prefix>fa_skeleton.nii.gz -- Binary skeleton of the mean FA map
• <prefix>all_fa_skeletonised.nii.gz -- 4D image containing all FA maps masked using the FA skeleton
• <prefix>all_md_skeletonised.nii.gz -- 4D image containing all MD maps masked using the FA skeleton
• <prefix>all_rd_skeletonised.nii.gz -- 4D image containing all RD maps masked using the FA skeleton
• <prefix>all_ad_skeletonised.nii.gz -- 4D image containing all AD maps masked using the FA skeleton
• TBSS outputs

If the --no_randomise option is used, the outputs are:

• <prefix>mean_fa.nii.gz -- Mean FA map generated from the input DTI using a groupwise approach with DTI-TK
• <prefix>fa_skeleton.nii.gz -- Binary skeleton of the mean FA map
• <prefix>all_fa_skeletonised.nii.gz -- 4D image containing all FA maps masked using the FA skeleton
• <prefix>all_md_skeletonised.nii.gz -- 4D image containing all MD maps masked using the FA skeleton
• <prefix>all_rd_skeletonised.nii.gz -- 4D image containing all RD maps masked using the FA skeleton
• <prefix>all_ad_skeletonised.nii.gz -- 4D image containing all AD maps masked using the FA skeleton