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.
- Overview
- System Requirements
- Installation Guide
- Docker
- Tutorial
- Outputs
- Usage
- Working with S3 Datasets
- Example Datasets
- Documentation
- License
- Manuscript Reproduction
- Issues
The m2g pipeline has been developed as a beginner-friendly 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.
The m2g pipeline:
- was developed and tested primarily on Mac OSX, Ubuntu (16, 18, 20), and CentOS (5, 6);
- made to work on Python 3.7;
- 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, AFNI, Dipy, Nibabel, Nilearn, Networkx, Numpy, Scipy, Scikit-Learn, and others;
- takes approximately 1-core, < 16-GB of RAM, and 1 hour to run for most datasets.
While you can install m2g from pip using the command pip install m2g, as there are several dependencies needed for both m2g and CPAC, it is highly recommended to use m2g through a docker container:
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 https://github.com/neurodata/m2g.git
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
Due to the versioning required for CPAC, along with m2g-d, we are currently working on streamlining the installation of m2g. Stay tuned for updates.
The m2g pipeline can be used to generate connectomes as a command-line utility on BIDS datasets with the following:
m2g --pipeline <pipe> /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.
Once you have the pipeline up and running, you can run the structural connectome pipeline with:
m2g --pipeline dwi <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 --pipeline dwi --seeds 1 --parcellation Desikan <input_directory> <output_directory>
You can set a particular scan and session as well (recommended for batch scripts):
m2g --pipeline dwi --seeds 1 --parcellation Desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>
Once you have the pipeline up and running, you can run the functional connectome pipeline with:
m2g --pipeline func <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 --pipeline func --seeds 1 --parcellation Desikan <input_directory> <output_directory>
You can set a particular scan and session as well (recommended for batch scripts):
m2g --pipeline func --seeds 1 --parcellation Desikan --participant_label <label> --session_label <label> <input_directory> <output_directory>
Both pipelines can be run by setting the pipeline parameter to both:
m2g --pipeline both <input_directory> <output_directory>
The organization of the output files generated by the m2g-d pipeline are shown below. If you only care about the connectome edgelists (m2g's fundamental output), you can find them in /output/connectomes_d.
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
/output
/anat_d
/preproc
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
/registered
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
/dwi
/fiber
Streamline track file(s)
/preproc (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
/tensor
Contains the rgb tensor file(s) for the dwi data if tractography is being done in MNI space
/connectomes_d
Location of connectome(s) created by the pipeline, with a directory given to each atlas you use for your analysis
/qa_d
/graphs_plotting
Png file of an adjacency matrix made from the connectome
/reg
<atlas>_atlas_2_nodif_B0_bet.png = overlay of registered atlas on top of anatomical image
qa_fast.png = overlay of white/grey matter and csf regions on top of anatomical image
t1w_aligned_mni_2_MNI152_T1_<vox>_brain.png = overlay of registered anatomical image on top of MNI152 anatomical reference image
t1w_corpuscallosum_dwi_2_nodif_B0_bet.png = corpus callosum region highlighted on registered anatomical image
t1w_csf_mask_dwi_2_nodif_B0_bet.png = overlay of csf mask on top of registered anatomical image
t1w_gm_in_dwi_2_nodif_B0_bet.png = overlay of grey matter mask on top of registered anatomical image
t1w_in_dwi_2_nodif_B0_bet.png = overlay of dwi image on top of anatomical image registered to dwi space
t1w_vent_mask_dwi_2_nodif_B0_bet.png = display of ventrical masks
t1w_wm_in_dwi_2_nodif_B0_bet.png = overlay of white matter mask on top of registered anatomical image
/skull_strip
qa_skullstrip__<sub>_<ses>_T1w_reor_RAS_res.png = overlay of skullstripped anatomical image on top of original anatomical image
/tmp_d
/reg_a (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
/reg_m (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
The organization of the output files generated by the m2g-f pipeline are shown below. If you only care about the connectome edgelists (m2g's fundamental output), you can find them in /output/connectomes_f.
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
/output
/anat_f
/anatomical_brain
<subject>_<session>_T1w_resample_calc.nii.gz = resampled and skullstripped brain from anatomical image
/anatomical_brain_mask
<subject>_<session>_T1w_resample_skullstrip_calc.nii.gz = mask of resampled and skullstripped brain from anatomical image
/anatomical_csf_mask
segment_seg_0_maths_maths.nii.gz = mask of csf area of anatomical image
/anatomical_gm_mask
segment_seg_1_maths_maths.nii.gz = mask of grey matter area of anatomical image
/anatomical_reorient
<subject>_<session>_T1w_resample.nii.gz = reorientated and resampled anatomical image
/anatomical_to_mni_nonlinear_xfm
<subject>_<session>_T1w_resample_fieldwarp.nii.gz = fieldwarp for registering the anatomical image to MNI space
/anatomical_to_standard
<subject>_<session>_T1w_resample_calc_warp.nii.gz = anatomical image registered to MNI space
/anatomical_wm_mask
segment_seg_2_maths_maths.nii.gz = mask of white matter area of anatomical image
/seg_mixeltype
segment_mixeltype.nii.gz = mixeltype representation of anatomical image
/seg_partial_volume_files
segment_pve_0.nii.gz = mask of grey matter regions of anatomical image
segment_pve_1.nii.gz = mask of grey matter/white matter boundary of anatomical image
segment_pve_2.nii.gz = mask of white matter regions of anatomical image
/seg_partial_volume_map
segment_pveseg.nii.gz = partial volume map of anatomical image
/seg_probability_maps
segment_prob_0.nii.gz = probability map of anatomical image for grey matter
segment_prob_1.nii.gz = probability map of grey/white matter boundary
segment_prob_2.nii.gz = probability map of anatomical image for white matter
/connectomes_f
/<atlas>
<sub>_<ses>_func_<atlas>_abs_edgelist.csv = edgelist file for estimated connectome where the absolute value of the correlation is given
<sub>_<ses>_func_<atlas>_edgelist.csv = edgelist file for estimated connectome
/func
/preproc
/coordinate_transformation
<subject>_<session>_task-rest_bold_calc_tshift_resample.aff12.1D =
/frame_wise_displacement_jenkinson
FD_J.1D = vector containing Jenkinson measurement of framewise displacement for each frame of the functional image file
/frame_wise_displacement_power
FD.1D = vector containing power of framewise displacement for each frame of the functional image file
/functional_brain_mask
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_mask.nii.gz = brain mask for the functional image
/functional_brain_mask_to_standard
<subject>_<session>_task-rest_bold_calc_tshift_rasample_volreg_mask_warp.nii.gz = functional brain mask registered to MNI152 space
/functional_freq_filtered
bandpassed_demeaned_filtered.nii.gz = frequency filtered functional file
/functional_nuisance_regressors
nuisance_regressors.1D
/functional_nuisance_residuals
residuals.nii.gz
/functional_preprocessed
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_maths.nii.gz = skullstripped brain from motion corrected functional image file resampled to voxel dimensions specified by user
/functional_preprocessed_mask
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_maths_maths.nii.gz = mask for image contained in /functional_preprocessed
/motion_correct
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg.nii.gz = motion corrected functional image file resampled to voxel dimensions specified by user
/motion_correct_to_standard_smooth
/_fwhm_4
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_warp_maths.nii.gz
/motion_params
motion_parameters.txt = statistical measurements of motion correction performed on functional image
/raw_functional
<subject>_<session>_task-rest_bold.nii.gz = unaltered input functional image
/slice_time_corrected
<subject>_<session>_task-rest_bold_calc_tshift.nii.gz = slice time corrected functional image
/register
/functional_to_anat_linear_xfm
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.mat =
/functional_to_standard
bandpassed_demeaned_filtered_warp.nii.gz = bandpassed and demeaned filtered warp map for registering the functional image to MNI space
/max_displacement
max_displacement.1D
/mean_functional
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat.nii.gz = mean functional image from all volumes
/mean_functional_in_anat
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.nii.gz = mean functional image registered to the anatomical image
/mean_functional_to_standard
<subject>_<session>_task-rest_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz = mean functional image registered to MNI space
/movement_parameters
<subject>_<session>_task-rest_bold_calc_tshift_resample.1D = movement parameters applied to each volumen of functional image
/power_params
pow_params.txt = different measurements on the power of functional images
/roi_timeseries
/<atlas>
roi_stats.csv = mean voxel intensity for each region of interest at each time point, used to calculate functional connectome
roi_stats.npz = pickeled version of roi_stats.csv
/log_f
callback.log = nipype log for modules made for pipeline
cpac_data_config_<date>.yml = file containing functional and anatomical image directory location
cpac_individual_timing_m2g.csv = record of the elapsed time from the run of m2g-f
cpac_pipeline_config_<date>.yml = copy of CPAC configuration file
functional_pipeline_settings.yaml = record of CPACP pipeline parameter settings
pypeline.lock = intermediate file created for pipeline running
pypeline.log = nipype log with record of everything printed to terminal
subject_info_<subject>_<session>.pkl = pickle file of functional and anatomical file information
/qa_f
/carpet
carpet_seg.png
/csf_gm_wm_a
montage_csf_gm_wm_a.png = axial view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image
/csf_gm_wm_s
montage_csf_gm_wm_s.png = sagittal view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image
/mean_func_with_mni_edge_a
MNI_edge_on_mean_func_mni_a.png = axial view of outline of MNI reference anatimical image overlaid on top of mean functional image
/mean_func_with_mni_edge_s
MNI_edge_on_mean_func_mni_s.png = sagittal view of outline of MNI reference anatimical image overlaid on top of mean functional image
/mean_func_with_t1_edge_a
t1_edge_on_mean_func_in_t1_a.png = axial view of outline of anatomical image overlaid on top of mean functional image registered to the anatomical image
/mean_func_with_t1_edge_s
t1_edge_on_mean_func_in_t1_s.png = sagittal view of outline of anatomical image overlaid on top of mean functional image registered to the anatomical image
/mni_normalized_anatomical_a
mni_anat_a.png = axial view of anatomical image registered to MNI image
/mni_normalized_anatomical_s
mni_anat_s.png = sagittal view of anatomical image registered to MNI image
/movement_rot_plot
motion_rot_plot.png = movement rotation plot for rotation correction of functional image
/movement_trans_plot
motion_trans_plot.png = movement translational plot for translation correction of functional image
/skullstrip_vis_a
skull_vis_a.png = axial view of original anatomical image overlaid on top of skullstripped anatomical image
/skullstrip_vis_s
skull_vis_s.png = sagittal view of original anatomical image overlaid on top of skullstripped anatomical imag
/snr_a
snr_a.png = axial view of signal to noise ratio for functional image
/snr_hist
snr_hist_plot.png = signal to noise ratio intensity plot
/snr_s
snr_s.png = sagittal view of signal to noise ratio for functional image
/snr_val
average_snr_file.txt = single value of average signal to noise ratio for functional image
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.
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 m2g.
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!