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The pipelines are explained and derivatives analyzed in our pre-print, available on [BiorXiv](https://www.biorxiv.org/content/early/2017/09/16/188706).\n\n# System Requirements\n\nThe **m2g** pipeline:\n\n- was developed and tested primarily on Mac OSX, Ubuntu (16, 18, 20), and CentOS (5, 6);\n- made to work on Python 3.7;\n- is wrapped in a [Docker container](https://hub.docker.com/r/neurodata/m2g);\n- has install instructions via a Dockerfile;\n- requires no non-standard hardware to run;\n- has key features built upon FSL, AFNI, Dipy, Nibabel, Nilearn, Networkx, Numpy, Scipy, Scikit-Learn, and others;\n- takes approximately 1-core, < 16-GB of RAM, and 1 hour to run for most datasets.\n\n# Installation Guide\n\n## Docker\n\nWhile 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:\n\n**m2g** is available through Dockerhub, and the most recent docker image can be pulled using:\n\n docker pull neurodata/m2g:latest\n\nThe 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):\n\n docker run -ti --entrypoint /bin/bash neurodata/m2g:latest\n\n**m2g** docker containers can also be made from m2g's Dockerfile.\n\n git clone https://github.com/neurodata/m2g.git\n cd m2g\n docker build -t .\n\nWhere \"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](https://docs.docker.com/engine/reference/commandline/image_build/).\nCreating the Docker image should take several minutes if this is the first time you have used this docker file.\nIn order to create a docker container from the docker image and access it, use the following command to both create and enter the container:\n\n docker run -it --entrypoint /bin/bash m2g:uniquelabel\n\n## Local Installation [COMING SOON]\nDue to the versioning required for CPAC, along with `m2g-d`, we are currently working on streamlining the installation of `m2g`. Stay tuned for updates.\n\n\n\n# Usage\n\nThe **m2g** pipeline can be used to generate connectomes as a command-line utility on [BIDS datasets](http://bids.neuroimaging.io) with the following:\n\n m2g --pipeline /input/bids/dataset /output/directory\n\nNote 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`.\n\n\n## Docker Container Usage\n\nIf 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:\n\n docker run -ti -v /path/to/local/data:/data neurodata/m2g /data/ /data/outputs\n\nThis 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`.\n\nAlso 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.\n\n\n# Tutorial\n\n## Structural Connectome Pipeline (`m2g-d`)\n\nOnce you have the pipeline up and running, you can run the structural connectome pipeline with:\n \n m2g --pipeline dwi \n \nWe 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):\n\n m2g --pipeline dwi --seeds 1 --parcellation Desikan \n\nYou can set a particular scan and session as well (recommended for batch scripts):\n\n m2g --pipeline dwi --seeds 1 --parcellation Desikan --participant_label --session_label \n\n## Functional Connectome Pipeline (`m2g-f`)\n\nOnce you have the pipeline up and running, you can run the functional connectome pipeline with:\n \n m2g --pipeline func \n \nWe 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):\n\n m2g --pipeline func --seeds 1 --parcellation Desikan \n\nYou can set a particular scan and session as well (recommended for batch scripts):\n\n m2g --pipeline func --seeds 1 --parcellation Desikan --participant_label --session_label \n\n\n## Running both `m2g-d` and `m2g-f`\nBoth pipelines can be run by setting the `pipeline` parameter to `both`:\n \n m2g --pipeline both \n\n# Outputs\n\n## Diffusion Pipeline\nThe 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`.\n\n```\nFile labels that may appear on output files, these denote additional actions m2g may have done:\nRAS = File was originally in RAS orientation, so no reorientation was necessary\nreor_RAS = File has been reoriented into RAS+ orientation\nnores = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing\nres = The file has been resliced to the desired voxel size specified by the user\n\n/output\n /anat_d\n \n /preproc\n t1w_aligned_mni.nii.gz = preprocessed t1w_brain anatomical image in mni space\n t1w_brain.nii.gz = t1w anatomical image with only the brain\n t1w_seg_mixeltype.nii.gz = mixeltype image of t1w image (denotes where there are more than one tissue type in each voxel)\n t1w_seg_pve_0.nii.gz = probability map of Cerebrospinal fluid for original t1w image\n t1w_seg_pve_1.nii.gz = probability map of grey matter for original t1w image\n t1w_seg_pve_2.nii.gz = probability map of white matter for original t1w image\n t1w_seg_pveseg.nii.gz = t1w image mapping wm, gm, ventricle, and csf areas\n t1w_wm_thr.nii.gz = binary white matter mask for resliced t1w image\n\n /registered\n t1w_corpuscallosum.nii.gz = atlas corpus callosum mask in t1w space\n t1w_corpuscallosum_dwi.nii.gz = atlas corpus callosum in dwi space\n t1w_csf_mask_dwi.nii.gz = t1w csf mask in dwi space\n t1w_gm_in_dwi.nii.gz = t1w grey matter probability map in dwi space\n t1w_in_dwi.nii.gz = t1w in dwi space\n t1w_wm_gm_int_in_dwi.nii.gz = t1w white matter-grey matter interfact in dwi space\n t1w_wm_gm_int_in_dwi_bin.nii.gz = binary mask of t12_2m_gm_int_in_dwi.nii.gz\n t1w_wm_in_dwi.nii.gz = atlas white matter probability map in dwi space\n\n /dwi\n /fiber\n Streamline track file(s)\n\n /preproc (files created during the preprocessing of the dwi data)\n #_B0.nii.gz = B0 image (there can be multiple B0 images per dwi file, # is the numerical location of each B0 image)\n bval.bval = original b-values for dwi image\n bvec.bvec = original b-vectors for dwi image\n bvecs_reor.bvecs = bvec_scaled.bvec data reoriented to RAS+ orientation\n bvec_scaled.bvec = b-vectors normalized to be of unit length, only non-zero b-values are changed\n eddy_corrected_data.nii.gz = eddy corrected dwi image\n eddy_corrected_data.ecclog = eddy correction log output\n eddy_corrected_data_reor_RAS.nii.gz = eddy corrected dwi image reoriented to RAS orientation\n eddy_corrected_data_reor_RAS_res.nii.gz = eddy corrected image reoriented to RAS orientation and resliced to desired voxel resolution\n nodif_B0.nii.gz = mean of all B0 images\n nodif_B0_bet.nii.gz = nodif_B0 image with all non-brain matter removed\n nodif_B0_bet_mask.nii.gz = mask of nodif_B0_bet.nii.gz brain\n tensor_fa.nii.gz = tensor image fractional anisotropy map\n \n /tensor\n Contains the rgb tensor file(s) for the dwi data if tractography is being done in MNI space\n\n /connectomes_d\n Location of connectome(s) created by the pipeline, with a directory given to each atlas you use for your analysis\n\n /qa_d\n /graphs_plotting\n Png file of an adjacency matrix made from the connectome\n /reg\n _atlas_2_nodif_B0_bet.png = overlay of registered atlas on top of anatomical image\n qa_fast.png = overlay of white/grey matter and csf regions on top of anatomical image\n t1w_aligned_mni_2_MNI152_T1__brain.png = overlay of registered anatomical image on top of MNI152 anatomical reference image\n t1w_corpuscallosum_dwi_2_nodif_B0_bet.png = corpus callosum region highlighted on registered anatomical image\n t1w_csf_mask_dwi_2_nodif_B0_bet.png = overlay of csf mask on top of registered anatomical image\n t1w_gm_in_dwi_2_nodif_B0_bet.png = overlay of grey matter mask on top of registered anatomical image\n t1w_in_dwi_2_nodif_B0_bet.png = overlay of dwi image on top of anatomical image registered to dwi space\n t1w_vent_mask_dwi_2_nodif_B0_bet.png = display of ventrical masks\n t1w_wm_in_dwi_2_nodif_B0_bet.png = overlay of white matter mask on top of registered anatomical image\n /skull_strip\n qa_skullstrip___{__T1w_reor_RAS_res.png = overlay of skullstripped anatomical image on top of original anatomical image\n \n /tmp_d\n /reg_a (Intermediate files created during the processing of the anatomical data)\n mni2t1w_warp.nii.gz = nonlinear warp coefficients/fields for mni to t1w space\n t1w_csf_mask_dwi_bin.nii.gz = binary mask of t1w_csf_mask_dwi.nii.gz\n t1w_gm_in_dwi_bin.nii.gz = binary mask of t12_gm_in_dwi.nii.gz\n t1w_vent_csf_in_dwi.nii.gz = t1w ventricle+csf mask in dwi space\n t1w_vent_mask_dwi.nii.gz = atlas ventricle mask in dwi space\n t1w_wm_edge.nii.gz = mask of the outer border of the resliced t1w white matter\n t1w_wm_in_dwi_bin.nii.gz = binary mask of t12_wm_in_dwi.nii.gz\n vent_mask_mni.nii.gz = altas ventricle mask in mni space using roi_2_mni_mat\n vent_mask_t1w.nii.gz = atlas ventricle mask in t1w space\n warp_t12mni.nii.gz = nonlinear warp coefficients/fields for t1w to mni space\n\n /reg_m (Intermediate files created during the processing of the diffusion data)\n dwi2t1w_bbr_xfm.mat = affine transform matrix of t1w_wm_edge.nii.gz to t1w space\n dwi2t1w_xfm.mat = inverse transform matrix of t1w2dwi_xfm.mat\n roi_2_mni.mat = affine transform matrix of selected atlas to mni space\n t1w2dwi_bbr_xfm.mat = inverse transform matrix of dwi2t1w_bbr_xfm.mat\n t1w2dwi_xfm.mat = affine transform matrix of t1w_brain.nii.gz to nodif_B0.nii.gz space\n 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\n xfm_mni2t1w_init.mat = inverse transform matrix of xfm_t1w2mni_init.mat\n xfm_t1w2mni_init.mat = affine transform matrix of preprocessed t1w_brain to mni space\n```\n\n## Functional Pipeline\nThe 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`.\n\n\n```\nFile labels that may appear on output files, these denote additional actions m2g may have done:\nRAS = File was originally in RAS orientation, so no reorientation was necessary\nreor_RAS = File has been reoriented into RAS+ orientation\nnores = File originally had the desired voxel size specified by the user (default 2mmx2mmx2mm), resulting in no reslicing\nres = The file has been resliced to the desired voxel size specified by the user\n\n/output\n /anat_f\n /anatomical_brain\n __T1w_resample_calc.nii.gz = resampled and skullstripped brain from anatomical image\n /anatomical_brain_mask\n __T1w_resample_skullstrip_calc.nii.gz = mask of resampled and skullstripped brain from anatomical image\n /anatomical_csf_mask\n segment_seg_0_maths_maths.nii.gz = mask of csf area of anatomical image\n /anatomical_gm_mask\n segment_seg_1_maths_maths.nii.gz = mask of grey matter area of anatomical image\n /anatomical_reorient\n __T1w_resample.nii.gz = reorientated and resampled anatomical image\n /anatomical_to_mni_nonlinear_xfm\n __T1w_resample_fieldwarp.nii.gz = fieldwarp for registering the anatomical image to MNI space\n /anatomical_to_standard\n __T1w_resample_calc_warp.nii.gz = anatomical image registered to MNI space\n /anatomical_wm_mask\n segment_seg_2_maths_maths.nii.gz = mask of white matter area of anatomical image\n /seg_mixeltype\n segment_mixeltype.nii.gz = mixeltype representation of anatomical image\n /seg_partial_volume_files\n segment_pve_0.nii.gz = mask of grey matter regions of anatomical image\n segment_pve_1.nii.gz = mask of grey matter/white matter boundary of anatomical image\n segment_pve_2.nii.gz = mask of white matter regions of anatomical image\n /seg_partial_volume_map\n segment_pveseg.nii.gz = partial volume map of anatomical image\n /seg_probability_maps\n segment_prob_0.nii.gz = probability map of anatomical image for grey matter\n segment_prob_1.nii.gz = probability map of grey/white matter boundary\n segment_prob_2.nii.gz = probability map of anatomical image for white matter\n /connectomes_f\n /\n _{__func__abs_edgelist.csv = edgelist file for estimated connectome where the absolute value of the correlation is given\n _{__func__edgelist.csv = edgelist file for estimated connectome\n /func\n /preproc\n /coordinate_transformation\n __task-rest_bold_calc_tshift_resample.aff12.1D = \n /frame_wise_displacement_jenkinson\n FD_J.1D = vector containing Jenkinson measurement of framewise displacement for each frame of the functional image file\n /frame_wise_displacement_power\n FD.1D = vector containing power of framewise displacement for each frame of the functional image file\n /functional_brain_mask\n __task-rest_bold_calc_tshift_resample_volreg_mask.nii.gz = brain mask for the functional image\n /functional_brain_mask_to_standard\n __task-rest_bold_calc_tshift_rasample_volreg_mask_warp.nii.gz = functional brain mask registered to MNI152 space\n /functional_freq_filtered\n bandpassed_demeaned_filtered.nii.gz = frequency filtered functional file\n /functional_nuisance_regressors\n nuisance_regressors.1D\n /functional_nuisance_residuals\n residuals.nii.gz\n /functional_preprocessed\n __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\n /functional_preprocessed_mask\n __task-rest_bold_calc_tshift_resample_volreg_calc_maths_maths.nii.gz = mask for image contained in /functional_preprocessed\n /motion_correct\n __task-rest_bold_calc_tshift_resample_volreg.nii.gz = motion corrected functional image file resampled to voxel dimensions specified by user\n /motion_correct_to_standard_smooth\n /_fwhm_4\n __task-rest_bold_calc_tshift_resample_volreg_warp_maths.nii.gz\n /motion_params\n motion_parameters.txt = statistical measurements of motion correction performed on functional image\n /raw_functional\n __task-rest_bold.nii.gz = unaltered input functional image\n /slice_time_corrected\n __task-rest_bold_calc_tshift.nii.gz = slice time corrected functional image\n /register\n /functional_to_anat_linear_xfm\n __task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.mat = \n /functional_to_standard\n bandpassed_demeaned_filtered_warp.nii.gz = bandpassed and demeaned filtered warp map for registering the functional image to MNI space\n /max_displacement\n max_displacement.1D\n /mean_functional\n __task-rest_bold_calc_tshift_resample_volreg_calc_tstat.nii.gz = mean functional image from all volumes\n /mean_functional_in_anat\n __task-rest_bold_calc_tshift_resample_volreg_calc_tstat_flirt.nii.gz = mean functional image registered to the anatomical image\n /mean_functional_to_standard\n __task-rest_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz = mean functional image registered to MNI space\n /movement_parameters\n __task-rest_bold_calc_tshift_resample.1D = movement parameters applied to each volumen of functional image\n /power_params\n pow_params.txt = different measurements on the power of functional images\n /roi_timeseries\n /\n roi_stats.csv = mean voxel intensity for each region of interest at each time point, used to calculate functional connectome\n roi_stats.npz = pickeled version of roi_stats.csv\n\n /log_f\n callback.log = nipype log for modules made for pipeline\n cpac_data_config_.yml = file containing functional and anatomical image directory location\n cpac_individual_timing_m2g.csv = record of the elapsed time from the run of m2g-f\n cpac_pipeline_config_.yml = copy of CPAC configuration file\n functional_pipeline_settings.yaml = record of CPACP pipeline parameter settings\n pypeline.lock = intermediate file created for pipeline running\n pypeline.log = nipype log with record of everything printed to terminal\n subject_info__.pkl = pickle file of functional and anatomical file information\n\n /qa_f\n /carpet\n carpet_seg.png\n /csf_gm_wm_a\n montage_csf_gm_wm_a.png = axial view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image\n /csf_gm_wm_s\n montage_csf_gm_wm_s.png = sagittal view of mask of csf/grey matter/white matter regions overlaid on top of anatomical image\n /mean_func_with_mni_edge_a\n MNI_edge_on_mean_func_mni_a.png = axial view of outline of MNI reference anatimical image overlaid on top of mean functional image\n /mean_func_with_mni_edge_s\n MNI_edge_on_mean_func_mni_s.png = sagittal view of outline of MNI reference anatimical image overlaid on top of mean functional image\n /mean_func_with_t1_edge_a\n 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\n /mean_func_with_t1_edge_s\n 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\n /mni_normalized_anatomical_a\n mni_anat_a.png = axial view of anatomical image registered to MNI image\n /mni_normalized_anatomical_s\n mni_anat_s.png = sagittal view of anatomical image registered to MNI image\n /movement_rot_plot\n motion_rot_plot.png = movement rotation plot for rotation correction of functional image\n /movement_trans_plot\n motion_trans_plot.png = movement translational plot for translation correction of functional image\n /skullstrip_vis_a\n skull_vis_a.png = axial view of original anatomical image overlaid on top of skullstripped anatomical image\n /skullstrip_vis_s\n skull_vis_s.png = sagittal view of original anatomical image overlaid on top of skullstripped anatomical imag\n /snr_a\n snr_a.png = axial view of signal to noise ratio for functional image\n /snr_hist\n snr_hist_plot.png = signal to noise ratio intensity plot\n /snr_s\n snr_s.png = sagittal view of signal to noise ratio for functional image\n /snr_val\n average_snr_file.txt = single value of average signal to noise ratio for functional image\n \n```\n\n## Working with S3 Datasets\n\n**m2g** has the ability to work on datasets stored on [Amazon's Simple Storage Service](https://aws.amazon.com/s3/), 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](https://github.com/neurodata/m2g/blob/deploy/tutorials/Batch.ipynb).\n\n## Example Datasets\n\nDerivatives have been produced on a variety of datasets, all of which are made available on [our website](http://m2g.io). Each of these datsets is available for access and download from their respective sources. Alternatively, example datasets on the [BIDS website](http://bids.neuroimaging.io) which contain diffusion data can be used and have been tested; `ds114`, for example.\n\n## Documentation\n\nCheck out some [resources](http://m2g.io) on our website, or our [function reference](https://ndmg.neurodata.io/) for more information about **m2g**.\n\n## License\n\nThis project is covered under the [Apache 2.0 License](https://github.com/neurodata/m2g/blob/m2g/LICENSE.txt).\n\n## Manuscript Reproduction\n\nThe figures produced in our manuscript linked in the [Overview](#overview) are all generated from code contained within Jupyter notebooks and made available at our [paper's Github repository](https://github.com/neurodata/ndmg-paper).\n\n## Issues\n\nIf 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. 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neurodata/m2g

Tags for neurodata/m2g