NeuroData's MR Graphs package, ndmg (pronounced "nutmeg"), is a turn-key pipeline which combines structural, diffusion, and functional* MRI data to estimate multi-resolution connectomes reliably and scalably.
- Overview
- System Requirements
- Installation Guide
- Docker
- Demo
- Usage
- Example Datasets
- Documentation
- License
- Manuscript Reproduction
- Issues
The ndmg 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 derivatives are analyzed in our pre-print, available on BiorXiv. The pipelines are explained in the documentation.
The ndmg pipeline:
- was developed and tested primarily on Mac OSX, Ubuntu (12, 14, 16), and CentOS (5, 6);
- was developed in Python 2.7;
- 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, 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.
While ndmg is quite robust to Python package versions (with only few exceptions, mentioned in the installation guide), an example of possible versions (taken from the ndmg Docker Image with version v0.0.50) is shown below. Note: this list excludes many libraries which are standard with a Python distribution, and a complete list with all packages and versions can be produced by running pip freeze within the Docker container mentioned above.
awscli==1.11.128 , boto3==1.4.5 , botocore==1.5.91 , colorama==0.3.7 , dipy==0.12.0 , matplotlib==1.5.1 ,
networkx==1.11 , nibabel==2.1.0 , nilearn==0.3.1 , numpy==1.8.2 , Pillow==2.3.0 , plotly==1.12.9 ,
s3transfer==0.1.10 , scikit-image==0.13.0 , scikit-learn==0.18.2 , scipy==0.13.3 .
ndmg relies on FSL, Dipy, networkx, and nibabel, numpy scipy, scikit-learn, scikit-image, nilearn. You should install FSL through the instructions on their website, then follow install other Python dependencies with the following:
pip install ndmg
The only known packages which require a specific version are plotly and networkx, due to backwards-compatability breaking changes.
Finally, you can install ndmg either from pip or Github as shown below. Installation shouldn't take more than a few minutes, but depends on your internet connection.
pip install ndmg
git clone https://github.com/neurodata/ndmg
cd ndmg
python setup.py install
Currently, functional processing lives only in a development branch, so if you wish to have functional processing as well you can intervene between the 2nd and 3rd lines above, and install the package as follows:
git clone https://github.com/neurodata/ndmg
cd ndmg
git checkout m3r-release
python setup.py install
ndmg is available through Dockerhub, and can be run directly with the following container (and any additional options you may require for Docker, such as volume mounting):
docker run -ti --entrypoint /bin/bash bids/ndmg
You can run our entire end-to-end pipeline in approximately 3 minutes on downsampled data with the following command:
ndmg_demo_dwi
The connectome produced may not have neurological significance, as the data has been significantly downsampled, but this test should ensure that all of the pieces of the code and driver script execute properly. The expected output from the demo is shown below. Files will be downloaded and output data generated in /tmp/small_demo/ and /tmp/small_demo/outputs/, respectively. If the graph properties summarized at the end of the execution below match those observed with your installation, the demo ran successfully.
Getting test data...
Archive: /tmp/ndmg_demo.zip
creating: ndmg_demo/
inflating: ndmg_demo/MNI152NLin6_res-4x4x4_T1w.nii.gz
inflating: ndmg_demo/MNI152NLin6_res-4x4x4_T1w_brain.nii.gz
creating: ndmg_demo/sub-0025864/
creating: ndmg_demo/sub-0025864/ses-1/
creating: ndmg_demo/sub-0025864/ses-1/func/
inflating: ndmg_demo/sub-0025864/ses-1/func/sub-0025864_ses-1_bold.nii.gz
creating: ndmg_demo/sub-0025864/ses-1/dwi/
inflating: ndmg_demo/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.bvec
inflating: ndmg_demo/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.bval
inflating: ndmg_demo/sub-0025864/ses-1/dwi/sub-0025864_ses-1_dwi.nii.gz
creating: ndmg_demo/sub-0025864/ses-1/anat/
inflating: ndmg_demo/sub-0025864/ses-1/anat/sub-0025864_ses-1_T1w.nii.gz
inflating: ndmg_demo/desikan-res-4x4x4.nii.gz
inflating: ndmg_demo/HarvardOxford_variant-thr25_res-4x4x4_lvmask.nii.gz
inflating: ndmg_demo/MNI152NLin6_res-4x4x4_T1w_brainmask.nii.gz
Creating output directory: /tmp/ndmg_demo/outputs
Creating output temp directory: /tmp/ndmg_demo/outputs/tmp
This pipeline will produce the following derivatives...
DWI volume registered to atlas: /tmp/ndmg_demo/outputs/reg/dwi/sub-0025864_ses-1_dwi_aligned.nii.gz
Diffusion tensors in atlas space: /tmp/ndmg_demo/outputs/tensors/sub-0025864_ses-1_dwi_tensors.npz
Fiber streamlines in atlas space: /tmp/ndmg_demo/outputs/fibers/sub-0025864_ses-1_dwi_fibers.npz
Graphs of streamlines downsampled to given labels: /tmp/ndmg_demo/outputs/graphs/desikan-res-4x4x4/sub-0025864_ses-1_dwi_desikan-res-4x4x4.gpickle
Generating gradient table...
B-values shape (15,)
min 0.000000
max 1000.000000
B-vectors shape (15, 3)
min -0.978756
max 0.941755
None
Aligning volumes...
Executing: eddy_correct /tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_dwi_t1.nii.gz /tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_dwi_t1_t2.nii.gz 0
Executing: epi_reg --epi=/tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_dwi_t1_t2.nii.gz --t1=/tmp/ndmg_demo/sub-0025864/ses-1/anat/sub-0025864_ses-1_T1w.nii.gz --t1brain=/tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_T1w_ss.nii.gz --out=/tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_dwi_t1_ta.nii.gz
Executing: flirt -in /tmp/ndmg_demo/sub-0025864/ses-1/anat/sub-0025864_ses-1_T1w.nii.gz -ref /tmp/ndmg_demo/MNI152NLin6_res-4x4x4_T1w.nii.gz -omat /tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_T1w_MNI152NLin6_res-4x4x4_T1w_xfm.mat -dof 12 -bins 256 -cost mutualinfo -searchrx -180 180 -searchry -180 180 -searchrz -180 180
Executing: flirt -in /tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_dwi_t1_ta.nii.gz -ref /tmp/ndmg_demo/MNI152NLin6_res-4x4x4_T1w.nii.gz -out /tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_dwi_t1_ta2.nii.gz -init /tmp/ndmg_demo/outputs/tmp/sub-0025864_ses-1_T1w_MNI152NLin6_res-4x4x4_T1w_xfm.mat -interp trilinear -applyxfm
Beginning tractography...
Generating graph for desikan-res-4x4x4 parcellation...
{'source': 'http://m2g.io', 'ecount': 0, 'vcount': 70, 'date': 'Fri Jan 19 00:15:32 2018', 'region': 'brain', 'sensor': 'dwi', 'name': "Generated by NeuroData's MRI Graphs (ndmg)"}
# of Streamlines: 5772
0
288
576
864
1152
1440
1728
2016
2304
2592
2880
3168
3456
3744
4032
4320
4608
4896
5184
5472
5760
Graph Summary:
Name: Generated by NeuroData's MRI Graphs (ndmg)
Type: Graph
Number of nodes: 70
Number of edges: 887
Average degree: 25.3429
Execution took: 0:02:56.343901
Complete!
Parcellation: desikan-res-4x4x4
Computing: NNZ
Sample Mean: 887.00
Computing: Degree Sequence
Subject Means: 25.34
Computing: Edge Weight Sequence
Subject Means: 68.99
Computing: Clustering Coefficient Sequence
Subject Means: 0.75
Computing: Max Local Statistic Sequence
Subject Means: 24948.83
Computing: Eigen Value Sequence
Subject Maxes: 1.32
Computing: Betweenness Centrality Sequence
Subject Means: 0.01
Computing: Mean Connectome
This is the format of your plot grid:
[ (1,1) x1,y1 ] [ (1,2) x2,y2 ] [ (1,3) x3,y3 ] [ (1,4) x4,y4 ]
[ (2,1) x5,y5 ] [ (2,2) x6,y6 ] [ (2,3) x7,y7 ] [ (2,4) x8,y8 ]
Path to qc fig: /tmp/ndmg_demo/outputs/qa/graphs/desikan-res-4x4x4/desikan-res-4x4x4_plot.html
The ndmg pipeline can be used to generate connectomes as a command-line utility on BIDS datasets with the following:
ndmg_bids /input/bids/dataset /output/directory participant
Note that more options are available which can be helpful if running on the Amazon cloud, which can be found and documented by running ndmg_bids -h. If you do not have a BIDS organized dataset, you an use a slightly more complicated interface which is made available and is documented with ndmg_pipeline -h.
If running with the Docker container shown above, the entrypoint is already set to ndmg_bids, so the pipeline can be run directly from the host-system command line as follows:
docker run -ti -v /path/to/local/data:/data bids/ndmg /data/ /data/outputs participant
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.
Check out some resources on our website, or our function reference for more information about ndmg.
This project is covered under the Apache 2.0 License.
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!