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Downloads shield DOI Code Climate DockerHub OpenNeuro

NeuroData's MR Graphs package, m2g, is a turn-key pipeline which uses structural and diffusion MRI data to estimate multi-resolution connectomes reliably and scalably.



The m2g pipeline has been developed as a one-click solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on BiorXiv.

System Requirements

The m2g pipeline:

  • was developed and tested primarily on Mac OSX, Ubuntu (12, 14, 16, 18), and CentOS (5, 6);
  • made to work on Python 3.6;
  • is wrapped in a Docker container;
  • has install instructions via a Dockerfile;
  • requires no non-standard hardware to run;
  • has key features built upon FSL, AFNI, Dipy, Nibabel, Nilearn, Networkx, Numpy, Scipy, Scikit-Learn, and others;
  • takes approximately 1-core, 8-GB of RAM, and 1 hour to run for most datasets.

Installation Guide

m2g relies on FSL, AFNI, Dipy, networkx, and nibabel, numpy scipy, scikit-learn, scikit-image, nilearn. You should install FSL and AFNI through the instructions on their website, then install other Python dependencies as well as the package itself with the following:

git clone
cd m2g
pip install -r requirements.txt
pip install .

For the most up-to-date version of m2g, you can use the staging branch. This version is updated regularly, but may be less stable:

git clone
cd m2g
git checkout staging
pip install -r requirements.txt
pip install .

You can also install m2g from pip as shown below. Installation shouldn't take more than a few minutes, but depends on your internet connection. Note that to install the most up-to-date version of the pipeline, we currently recommend installing from github.

Install from pip

pip install m2g


m2g is available through Dockerhub, and the most recent docker image can be pulled using:

docker pull neurodata/m2g:latest

The image can then be used to create a container and run directly with the following command (and any additional options you may require for Docker, such as volume mounting):

docker run -ti --entrypoint /bin/bash neurodata/m2g:latest

m2g docker containers can also be made from m2g's Dockerfile.

git clone
cd m2g
docker build -t <imagename:uniquelabel> .

Where "uniquelabel" can be whatever you wish to call this Docker image (for example, m2g:latest). Additional information about building Docker images can be found here. Creating the Docker image should take several minutes if this is the first time you have used this docker file. In order to create a docker container from the docker image and access it, use the following command to both create and enter the container:

docker run -it --entrypoint /bin/bash m2g:uniquelabel


Once you have the pipeline up and running, you can run it with:

m2g <input_directory> <output_directory>

We recommend specifying an atlas and lowering the default seed density on test runs (although, for real runs, we recommend using the default seeding -- lowering seeding simply decreases runtime):

m2g --seeds 1 --parcellation desikan <input_directory> <output_directory>

You can set a particular scan and session as well (recommended for batch scripts):

m2g --seeds 1 --parcellation desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>

For more detailed instructions, tutorials on the m2g pipeline can be found in m2g/tutorials


The organization of the output files generated by the m2g pipeline are shown below. If you only care about the connectome edgelists (m2g's fundamental output), you can find them in /output/dwi/roi-connectomes.

File labels that may appear on output files, these denote additional actions m2g may have done:
RAS = File was originally in RAS orientation, so no reorientation was necessary
reor_RAS = File has been reoriented into RAS+ orientation
nores = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing
res = The file has been resliced to the desired voxel size specified by the user

               Files created during the preprocessing of the anatomical data
                   t1w_aligned_mni.nii.gz = preprocessed t1w_brain anatomical image in mni space
                   t1w_brain.nii.gz = t1w anatomical image with only the brain
                   t1w_seg_mixeltype.nii.gz = mixeltype image of t1w image (denotes where there are more than one tissue type in each voxel)
                   t1w_seg_pve_0.nii.gz = probability map of Cerebrospinal fluid for original t1w image
                   t1w_seg_pve_1.nii.gz = probability map of grey matter for original t1w image
                   t1w_seg_pve_2.nii.gz = probability map of white matter for original t1w image
                   t1w_seg_pveseg.nii.gz = t1w image mapping wm, gm, ventricle, and csf areas
                   t1w_wm_thr.nii.gz = binary white matter mask for resliced t1w image

               Files created during the registration process
                   t1w_corpuscallosum.nii.gz = atlas corpus callosum mask in t1w space
                   t1w_corpuscallosum_dwi.nii.gz = atlas corpus callosum in dwi space
                   t1w_csf_mask_dwi.nii.gz = t1w csf mask in dwi space
                   t1w_gm_in_dwi.nii.gz = t1w grey matter probability map in dwi space
                   t1w_in_dwi.nii.gz = t1w in dwi space
                   t1w_wm_gm_int_in_dwi.nii.gz = t1w white matter-grey matter interfact in dwi space
                   t1w_wm_gm_int_in_dwi_bin.nii.gz = binary mask of t12_2m_gm_int_in_dwi.nii.gz
                   t1w_wm_in_dwi.nii.gz = atlas white matter probability map in dwi space

               Streamline track file(s)
               Files created during the preprocessing of the dwi data
                    #_B0.nii.gz = B0 image (there can be multiple B0 images per dwi file, # is the numerical location of each B0 image)
                    bval.bval = original b-values for dwi image
                    bvec.bvec = original b-vectors for dwi image
                    bvecs_reor.bvecs = bvec_scaled.bvec data reoriented to RAS+ orientation
                    bvec_scaled.bvec = b-vectors normalized to be of unit length, only non-zero b-values are changed
                    eddy_corrected_data.nii.gz = eddy corrected dwi image
                    eddy_corrected_data.ecclog = eddy correction log output
                    eddy_corrected_data_reor_RAS.nii.gz = eddy corrected dwi image reoriented to RAS orientation
                    eddy_corrected_data_reor_RAS_res.nii.gz = eddy corrected image reoriented to RAS orientation and resliced to desired voxel resolution
                    nodif_B0.nii.gz = mean of all B0 images
                    nodif_B0_bet.nii.gz = nodif_B0 image with all non-brain matter removed
                    nodif_B0_bet_mask.nii.gz = mask of nodif_B0_bet.nii.gz brain
                    tensor_fa.nii.gz = tensor image fractional anisotropy map
               Location of connectome(s) created by the pipeline, with a directory given to each atlas you use for your analysis
               Contains the rgb tensor file(s) for the dwi data if tractography is being done in MNI space
               Png file of an adjacency matrix made from the connectome
               Intermediate files created during the processing of the anatomical data
                    mni2t1w_warp.nii.gz = nonlinear warp coefficients/fields for mni to t1w space
                    t1w_csf_mask_dwi_bin.nii.gz = binary mask of t1w_csf_mask_dwi.nii.gz
                    t1w_gm_in_dwi_bin.nii.gz = binary mask of t12_gm_in_dwi.nii.gz
                    t1w_vent_csf_in_dwi.nii.gz = t1w ventricle+csf mask in dwi space
                    t1w_vent_mask_dwi.nii.gz = atlas ventricle mask in dwi space
                    t1w_wm_edge.nii.gz = mask of the outer border of the resliced t1w white matter
                    t1w_wm_in_dwi_bin.nii.gz = binary mask of t12_wm_in_dwi.nii.gz
                    vent_mask_mni.nii.gz = altas ventricle mask in mni space using roi_2_mni_mat
                    vent_mask_t1w.nii.gz = atlas ventricle mask in t1w space
                    warp_t12mni.nii.gz = nonlinear warp coefficients/fields for t1w to mni space

               Intermediate files created during the processing of the diffusion data
                    dwi2t1w_bbr_xfm.mat = affine transform matrix of t1w_wm_edge.nii.gz to t1w space
                    dwi2t1w_xfm.mat = inverse transform matrix of t1w2dwi_xfm.mat
                    roi_2_mni.mat = affine transform matrix of selected atlas to mni space
                    t1w2dwi_bbr_xfm.mat = inverse transform matrix of dwi2t1w_bbr_xfm.mat
                    t1w2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz space
                    t1wtissue2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz, using t1w2dwi_bbr_xfm.mat or t1w2dwi_xfm.mat as a starting point
                    xfm_mni2t1w_init.mat = inverse transform matrix of xfm_t1w2mni_init.mat
                    xfm_t1w2mni_init.mat = affine transform matrix of preprocessed t1w_brain to mni space

Other files may end up in the output folders, depending on what settings or atlases you choose to use. Using MNI space for tractography or setting --clean to True will result in fewer files.


The m2g pipeline can be used to generate connectomes as a command-line utility on BIDS datasets with the following:

m2g /input/bids/dataset /output/directory

Note that more options are available which can be helpful if running on the Amazon cloud, which can be found and documented by running m2g -h. If running with the Docker container shown above, the entrypoint is already set to m2g, so the pipeline can be run directly from the host-system command line as follows:

docker run -ti -v /path/to/local/data:/data neurodata/m2g /data/ /data/outputs

This will run m2g on the local data and save the output files to the directory /path/to/local/data/outputs. Note that if you have created the docker image from github, replace neurodata/m2g with imagename:uniquelabel.

Also note that currently, running m2g on a single bids-formatted dataset directory only runs a single scan. To run the entire dataset, we recommend parallelizing on a high-performance cluster or using m2g's s3 integration.

Working with S3 Datasets

m2g has the ability to work on datasets stored on Amazon's Simple Storage Service, assuming they are in BIDS format. Doing so requires you to set your AWS credentials and read the related s3 bucket documentation. You can find a guide here.

Example Datasets

Derivatives have been produced on a variety of datasets, all of which are made available on our website. Each of these datsets is available for access and download from their respective sources. Alternatively, example datasets on the BIDS website which contain diffusion data can be used and have been tested; ds114, for example.

For some downsampled test data, see neuroparc


Check out some resources on our website, or our function reference for more information about m2g.


This project is covered under the Apache 2.0 License.

Manuscript Reproduction

The figures produced in our manuscript linked in the Overview are all generated from code contained within Jupyter notebooks and made available at our paper's Github repository.


If you're having trouble, notice a bug, or want to contribute (such as a fix to the bug you may have just found) feel free to open a git issue or pull request. Enjoy!

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