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Reproducible Tract Profiles (draft)

RTP comprises a set of methods to manage and analyze diffusion weighted imaging (DWI) data for reproducible tractography. The tools take MRI data from the scanner and process them through a series of analysis implemented as Docker containers that are integrated into a modern neuroinformatics platform (Flywheel, Docker, Singularity). The platform guarantees that the entire pipeline can be re-executed, using the same data and computational parameters. We describe the DWI analysis tools that are used to identify the positions of a user-defined number of tracts and their diffusion profiles. The combination of these three components defines a system that transforms raw data into reproducible tract profiles for publication.

Although this repository is only for the tracking part, the whole solution is comprised of three main parts (each one implemented in a different container).

Structural preprocessing and ROI creation

This container runs Freesurfer, and a set of other components to create ROIs.

Input: T1 nifti image.

Output: Freesurfer's standard output + a folder with a set of ROIs. Most importantly, it will generate a file called fs.zip that is required as an input to RTP-pipeline. fs.zip can be created in this container, or if it is required (because we want to create the ROIs manually, for example) it can be created locally. The folder should contain a folder called fs/ in the base, and nothing else. In the fs folder there will be two things:

1. brainmask.nii.gz, aparc+aseg.nii.gz (required) and other optional files.
2. a folder called ROIs: in this folder all the binary nifti ROIs will be stored.

Main components of the container:

1. Freesurfer 6 dev version (will be updated to 7 when available) (add link and citation)
2. Hippocampal and Thalamic segmentation (add link and citation)
3. Neuropythy (add link and citation)
4. Cerebellum atlas (add link and citation)
5. Mori atlas ROIs (add link and citation)

RTP-preproc

This container does the dMRI data preprocessing.

Input: T1 from Freesurfer and the raw dMRI nifti images.

Output: Preprocessed and anatomically aligned dMRI images.

Main components:

1. mrTrix (add link and citation)
2. FSL (add link and citation)
3. ANTs (add link and citation)

RTP-pipeline

This container does the tracking, and obtains the metrics and the profiles. Check the How to use section for a description of inputs and outputs.

RTP-pipeline uses parts of these tools (depending on the selected options):

1. mrTrix 3 (add link and citation)
2. mrVista, mrDiffusion and AFQ (Vistalab's Vistasoft) (add link and citation)
3. Ensemble Tractography (ET) (add link and citation)
4. LiFE/SIFT2(TODO) (add link and citation)/(add link and citation)

More documentation

• Installation
• How to use
• Parameter recommendations: differences in acquisition sequences or subject populations require to use different parameters, in this page we collect the parameters and pipeline versions we used for better results.
• Reporting and citation In this wiki page we include examples of how to report and cite RTP and all the included tools, it will change depending on the selected tools.
• TO-DO list

Parameter-recommendations