Cross-sectional TBSS

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] [-g]

Cross-section TBSS pipeline. 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
 --no_randomise      The script exists before the statistical analysis step
  --mat design_mat      Design matrix file to be used by randomised
  --con design_con      Design contrast file to be used by randomised
  -o output_dir, --output_dir output_dir
                        Output directory where to save the results
  --output_pre prefix   Output result prefix (default='tbss_')
  -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:

• here
• here

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

• here
• here