Run NeuroDock

Congratulations, you’ve come this far. You’ve installed Docker and NeuroDock, and are probably wondering how what else to do…

You’re done. Not even kidding! You can now start processing data with PyDesigner and NeuroDock. It’s almost as if FSL, MRtrix3 and PyDesigner commands are built natively into your OS - be it Linux, Mac OS, or even Windows!

Intro to Docker Run

Use the following form of docker run command to call all command made availble by NeuroDock:

$ docker run [OPTIONS] IMAGE [COMMAND] [ARG...]





docker options to use when running the container; common options are -it, -v, -d


image name to run; in this instance, this is dmri/neurodock


specify which NeuroDock commands to run; these can be FSL, MRtrix3 or PyDesigner commands


arguments for [COMMAND]

Users are encouraged to visit the Docker run reference documentation for more information on controlling the :code:`docker run ` command.

Practical Run

The section above convered a generic way to use the docker run command. For actual data analysis, we use the following options.

1. -it --rm to run docker in interative TTY mode. What this implies is that your NeuroDock command will run like any other OS commands such as ipconfig, watch, ls etc.

  1. -v to mount the file system or folder to processing

Bind Mount

The second flag. -v, makes visible the host’s local filesystem to a Docker container, which otherwise runs in a completely isolated system. By mounting a folder for NeuroDock, you are able to make it process data in said folder. The general guideline is to mount one subject folder at a time. It is advisable that users read through Docker’s bind mounts to understand how Docker containers handle storage.

The correct syntax for the -v flag is:


Suppose a subject folder bond_007 in need of processing is structured the following way:

├── nifti
│ ├── bond_dwi.bval
│ ├── bond_dwi.bvec
│ ├── bond_dwi.bval
│ ├── bond_dwi.json
│ ├── bond_topup.json
│ └── bond_topup.nii
└── processed (empty dir)

This subject needs to be processed PyDesigner read the input nifti files in the nifti directory, and saves the outputs in the processed directory. Since both nifti and processed folders belong to a common parent directory, the bond_007 directory can be mounted to give NeurDock access to both child directories simultaneously.

Here, the directory bond_007 is the [HOST PATH TO MOUNT], the directory that NeuroDock will not be able to see.

Next, we need to define where within the container this directory is mounted, [TARGET AT WHICH TO MOUNT]. You may simply mount this in the root NeuroDock directory at /data.

The flag to reflect this would then be:

-v /Users/sid/Desktop/bond_007:/data

This would make the contents of host directory bond_007 available in the NeuroDock at data. Say, for example, the nifti file bond_dwi.nii, is located in the host system at /Users/sid/Desktop/bond_007/nifti/bond_dwi.nii. If the above mounting scheme is used, the NeuroDock container will see this file in /data/nifti/bond_dwi.nii

This filesystem transformation is particularly important when writing scripts for automatic or batch processing of subject directories using the NeuroDock container.

Put it all together

Considering everything on this page, it becomes incredibly easy to process a subject using the NeuoDock container. Sticking to bond_007 example above, and combining everthing so far, one could process Mr. Bond’s DWI with the command:

$ docker run -it --rm -v /Users/sid/Desktop/bond_007:/data \
    dmri/neurodock pydesigner --standard \
    --output /data/processed \

This command runs the --standard PyDesigner pipeline on the input files /Users/sid/Desktop/bond_007/nifti/bond_dwi.nii and /Users/sid/Desktop/bond_007/nifti/bond_topup.nii, and saves all outputs into the directory Users/sid/Desktop/bond_007/processed