{"id":13676395,"url":"https://github.com/bids-apps/MRtrix3_connectome","last_synced_at":"2025-04-29T07:32:06.093Z","repository":{"id":10788875,"uuid":"64950150","full_name":"bids-apps/MRtrix3_connectome","owner":"bids-apps","description":"Generate subject connectomes from raw BIDS data \u0026 perform inter-subject connection density normalisation, using  the MRtrix3 software package.","archived":false,"fork":false,"pushed_at":"2023-09-27T03:20:46.000Z","size":800,"stargazers_count":49,"open_issues_count":37,"forks_count":26,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-11-11T18:41:12.993Z","etag":null,"topics":["bids","bidsapp","diffusion-mri","mri"],"latest_commit_sha":null,"homepage":"http://www.mrtrix.org/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bids-apps.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-08-04T16:26:23.000Z","updated_at":"2024-08-16T19:21:48.000Z","dependencies_parsed_at":"2023-09-27T09:15:59.201Z","dependency_job_id":null,"html_url":"https://github.com/bids-apps/MRtrix3_connectome","commit_stats":null,"previous_names":[],"tags_count":15,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FMRtrix3_connectome","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FMRtrix3_connectome/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FMRtrix3_connectome/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FMRtrix3_connectome/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bids-apps","download_url":"https://codeload.github.com/bids-apps/MRtrix3_connectome/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251455885,"owners_count":21592256,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bids","bidsapp","diffusion-mri","mri"],"created_at":"2024-08-02T13:00:25.198Z","updated_at":"2025-04-29T07:32:05.521Z","avatar_url":"https://github.com/bids-apps.png","language":"Python","funding_links":[],"categories":["BIDS Apps","Python"],"sub_categories":["others"],"readme":"## Description\n\nThis BIDS App enables generation and subsequent group analysis of structural connectomes\ngenerated from diffusion MRI data. The analysis pipeline relies primarily on the *MRtrix3*\nsoftware package, and includes a number of state-of-the-art methods for image processing,\ntractography reconstruction, connectome generation and inter-subject connection density\nnormalisation.\n\n**NOTE**: App is still under development; script is not guaranteed to be operational\nfor all use cases.\n\n## Requirements\n\nDue to use of the Anatomically-Constrained Tractography (ACT) framework, correction of\nEPI susceptibility distortions is a prerequisite for this pipeline. Currently, this is\nonly possible within this pipeline through use of the FSL tool `topup`, which relies\non the presence of spin-echo EPI images with differences in phase encoding to estimate\nthe causative inhomogeneity field. In the absence of such data, this pipeline is not\ncurrently applicable; though recommendations for alternative mechanisms for such\ncorrection in the Issues page are welcome, and development of novel techniques for\nperforming this correction are additionally underway.\n\nWhile many common DICOM conversion software are capable of providing data characterising\nthe phase and slice encoding performed in the acquisition protocol, which are subsequently\nused by this pipeline to automate DWI data pre-processing, for some softwares and/or\nsome data (particularly those not acquired on a Siemens platform), such data may not be\npresent in the sidecar JSON files for files in the BIDS `dwi/` and `fmap/` directories.\nIn this circumstance, it will be necessary for users to manually enter the relevant\ninformation into these files in order for this script to be capable of processing the\ndata. Every JSON file in these two directories should contain the BIDS fields\n`PhaseEncodingDirection` and `TotalReadoutTime`. For DWI data, it is also preferable to\nprovide the `SliceEncodingDirection` and `SliceTiming` fields. More information on these\ndata can be found in the [BIDS documentation](http://bids.neuroimaging.io/).\n\n## Instructions\n\n### Multi-stage analysis pipeline\n\nThe tool consists of three explicit analysis levels:\n\n1. \"`preproc`\": This performs basic DWI (and if necessary T1-weighted image) pre-processing\n   based on the raw BIDS data input, writing the resulting pre-processed data to\n   sub-directory \"`MRtrix3_connectome-preproc`\" within the specified output directory.\n\n2. \"`participant`\": This operates upon the pre-processed DWI (and possibly T1-weighted image)\n   data generated by the \"`preproc`\" analysis level, performing CSD, streamlines reconstruction\n   and connectome construction. Results are written to sub-directory \"`MRtrix3_connectome-participant`\"\n   within the specified output directory.\n\n3. \"`group`\": This operates on the outputs of the \"`participant`\" analysis level, performing\n   appropriate inter-subject connection density normalisation as described in\n   [this manuscript](https://osf.io/c67kn/). Results are written to sub-directory\n   \"`MRtrix3_connectome-group`\" within the specified output directory.\n\n### Execution platforms\n\nThis script can be utilised in one of three ways:\n\n1. As a stand-alone *MRtrix3* script\n\n   The script ``mrtrix3_connectome.py`` can additionally be used *outside* of this Docker\n   container, as a stand-alone Python script build against the *MRtrix3* Python libraries.\n   Using the script in this way requires setting the ``PYTHONPATH`` environment variable to\n   include the path to the *MRtrix3* ``lib/`` directory where it is installed on your local\n   system, as described [here](https://community.mrtrix.org/t/the-mrtrix3-python-script-library/2243).\n   When used in this way, the command-line interface of the script will be more consistent\n   with the rest of *MRtrix3*. Note that this usage will require version `3.0.0` of *MRtrix3*\n   to be installed and configured appropriately on your local system.\n\n2. As a Docker container\n\n   The [bids/MRtrix3_connectome](https://hub.docker.com/r/bids/mrtrix3_connectome/) Docker\n   container enables users to generate structural connectomes from diffusion MRI data using\n   state-of-the-art techniques. The pipeline requires that data be organized in accordance\n   with the [BIDS specification](http://bids.neuroimaging.io).\n\n   In your terminal, type:\n   ```{bash}\n   $ docker pull bids/mrtrix3_connectome\n   ```\n\n   Using the \"`preproc`\"-level analysis as an exemplar, the tool is executed as e.g.:\n\n   ```{bash}\n   $ docker run -i --rm \\\n         -v /Users/yourname/data:/bids_dataset \\\n         -v /Users/yourname/output:/output \\\n         bids/mrtrix3_connectome \\\n         /bids_dataset /output preproc --participant_label 01\n   ```\n\n3. As a Singularity container\n\n   The *MRtrix3_connectome* BIDS App can also be built locally as a Singularity container.\n   This is particularly useful for subsequent utilisation on high-performance computing\n   hardware, as unlike Docker there are no super-user privileges or user group\n   memberships required for execution. I have also personally been able to utilise the\n   CUDA version of FSL's `eddy` command within this tool when running on a computing\n   cluster node with GPU capability (though this can require explicit configuration;\n   speak to your system administrator).\n\n   Within the location in which the *MRtrix3_connectome* source code has been cloned,\n   type:\n   ```{bash}\n   $ sudo singularity build MRtrix3_connectome.sif Singularity\n   ```\n\n   The resulting container file \"`MRtrix3_connectome.sif`\" can be run as a stand-alone\n   executable, as long as the system on which the file is executed has a version of\n   Singularity installed that is compatible with that of the system used to build the\n   container.\n\n## Documentation\n\nThe help page of the tool itself can be generated by executing the script without\nproviding any command-line options. The help page is additionally presented at the\nbottom of this README page for reference. Documentation regarding the underlying\n*MRtrix3* tools can be found in the official\n[*MRtrix3* documentation](http://mrtrix.readthedocs.org). Additional information\nmay be found in the [online *MRtrix3* community forum](http://community.mrtrix.org).\n\n## Error Reporting\n\nExperiencing problems? You can either post a private message to me on the\n[*MRtrix3* community forum](http://community.mrtrix.org/u/rsmith), or you can report it\ndirectly to the [GitHub issues list](https://github.com/BIDS-Apps/MRtrix3_connectome/issues).\nIn both cases, please include as much information as possible; this may include re-running\nthe script using the ``--debug`` option, which will provide additional information at the\nterminal, and preserve temporary files generated by the script within your target output\ndirectory, which can be forwarded to the developer.\n\n## Acknowledgements\n\nDevelopment of this tool was made possible through funding from the National Health\nand Medical Research Council (NHMRC) of Australia.\n\nThe developer acknowledges the facilities and scientific and technical assistance of\nthe National Imaging Facility, a National Collaborative Research Infrastructure Strategy\n(NCRIS) capability, at the Florey Institute of Neuroscience and Mental Health.\n\nThe Florey Institute of Neuroscience and Mental Health acknowledges support from the\nVictorian Government and in particular the funding from the Operational Infrastructure\nSupport Grant.\n\nRobert Smith is supported by fellowship funding from the National Imaging Facility (NIF),\nan Australian Government National Collaborative Research Infrastructure Strategy (NCRIS)\ncapability.\n\n## Citation\n\nWhen using this pipeline, please use the following snippet to acknowledge the relevant\nwork (amend as appropriate depending on options used):\n\nStructural connectomes were generated using the *MRtrix3_connectome* BIDS App (Smith\net al., 2019), which operates principally using tools provided in the *MRtrix3*\nsoftware package (Tournier et al., 2019; http://mrtrix.org). This included: DWI\ndenoising (Veraart et al., 2016), Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson et al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF USING EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison et al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n2015); brain extraction (Smith, 2002 OR Iglesias et al., 2011), T1 tissue segmentation\n(Zhang et al., 2001; Smith, 2002; Patenaude et al., 2011; Smith et al., 2012); spherical\ndeconvolution (Tournier et al., 2004; Jeurissen et al., 2014); probabilistic tractography\n(Tournier et al., 2010) utilizing Anatomically-Constrained Tractography (Smith et al.,\n2012) and dynamic seeding (Smith et al., 2015b); SIFT2 (Smith et al., 2015b); T1\nparcellation ((((Avants et al., 2008 AND Tustison et al., 2013) OR Andersson et al.,\n2010) AND (Tzourio-Mazoyer et al., 2002 OR Yeo et al., 2011 OR Craddock et al., 2012\n2011) OR Fan et al., 2016 OR (Zalesky et al., 2010 AND Perry et al., 2017))) OR\n2012) (Dale et al., 1999 AND (Desikan et al., 2006 OR Destrieux et al., 2010 OR\n2013) Glasser et al., 2016))); robust structural connectome construction (Smith et al.,\n2014) 2015a; Yeh et al., 2016); inter-subject connection density normalisation\n2015) (Smith et al., 2020).\n\n```\nSmith, R. E.; Connelly, A. MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. In Proc OHBM, 2019, W610\nAndersson, J. L.; Skare, S. \u0026 Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 2003, 20, 870-888\nAndersson, J. L. R.; Jenkinson, M. \u0026 Smith, S. Non-linear registration, aka spatial normalisation. FMRIB technical report, 2010, TR07JA2\nAndersson, J. L. \u0026 Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 2016, 125, 1063-1078\nAndersson, J. L. R. \u0026 Graham, M. S. \u0026 Zsoldos, E. \u0026 Sotiropoulos, S. N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage, 2016, 141, 556-572\nAndersson, J. L. R.; Graham, M. S.; Drobnjak, I.; Zhang, H.; Filippini, N. \u0026 Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage, 2017, 152, 450-466\nAndersson, J. L. R.; Graham, M. S.; Drobnjak, I.; Zhang, H. \u0026 Campbell, J. Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. NeuroImage, 2018, 171, 277-295\nAvants, B. B.; Epstein, C. L.; Grossman, M. \u0026 Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 2008, 12, 26-41\nBhushan, C.; Haldar, J. P.; Choi, S.; Joshi, A. A.; Shattuck, D. W. \u0026 Leahy, R. M. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. NeuroImage, 2015, 115, 269-280\nCraddock, R. C.; James, G. A.; Holtzheimer, P. E.; Hu, X. P.; Mayberg, H. S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 2012, 33(8), 1914-1928\nDale, A. M.; Fischl, B. \u0026 Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage, 1999, 9, 179-194\nDesikan, R. S.; Ségonne, F.; Fischl, B.; Quinn, B. T.; Dickerson, B. C.; Blacker, D.; Buckner, R. L.; Dale, A. M.; Maguire, R. P.; Hyman, B. T.; Albert, M. S. \u0026 Killiany, R. J. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 2006, 31, 968-980\nDestrieux, C.; Fischl, B.; Dale, A. \u0026 Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 2010, 53, 1-15\nFan, L.; Li, H.; Zhuo, J.; Zhang, Y.; Wang, J.; Chen, L.; Yang, Z.; Chu, C.; Xie, S.; Laird, A.R.; Fox, P.T.; Eickhoff, S.B.; Yu, C.; Jiang, T. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 2016, 26 (8), 3508-3526\nGlasser, M. F.; Coalson, T. S.; Robinson, E. C.; Hacker, C. D.; Harwell, J.; Yacoub, E.; Ugurbil, K.; Andersson, J.; Beckmann, C. F.; Jenkinson, M.; Smith, S. M. \u0026 Van Essen, D. C. A multi-modal parcellation of human cerebral cortex. Nature, 2016, 536, 171-178\nIglesias, J. E.; Liu, C. Y.; Thompson, P. M. \u0026 Tu, Z. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods. IEEE Transactions on Medical Imaging, 2011, 30, 1617-1634\nJeurissen, B; Tournier, J-D; Dhollander, T; Connelly, A \u0026 Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 2014, 103, 411-426\nKellner, E.; Dhital, B.; Kiselev, V. G.; Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 2006, 76(5), 1574-1581\nPatenaude, B.; Smith, S. M.; Kennedy, D. N. \u0026 Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 2011, 56, 907-922\nPerry, A.; Wen, W.; Kochan, N. A.; Thalamuthu, A.; Sachdev, P. S.; Breakspear, M. The independent influences of age and education on functional brain networks and cognition in healthy older adults. Human Brain Mapping, 2017, 38(10), 5094-5114\nSmith, S. M. Fast robust automated brain extraction. Human Brain Mapping, 2002, 17, 143-155\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026 Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 2012, 62, 1924-1938\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026 Connelly, A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage, 2015a, 104, 253-265\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026 Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 2015b, 119, 338-351\nSmith, R. E.; Raffelt, D.; Tournier, J.-D.; Connelly, A. Quantitative streamlines tractography: methods and inter-subject normalisation. Preprint, 2020, OSF, https://doi.org/10.31219/osf.io/c67kn\nTournier, J.-D.; Calamante, F., Gadian, D.G. \u0026 Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004, 23, 1176-1185\nTournier, J.-D.; Calamante, F. \u0026 Connelly, A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the International Society for Magnetic Resonance in Medicine, 2010, 1670\nTournier, J.-D.; Smith, R. E.; Raffelt, D. A.; Tabbara, R.; Dhollander, T.; Pietsch, M; Christiaens, D.; Jeurissen, B.; Y, C.-H.; Connelly, A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 2019, 202, 116137\nTustison, N.; Avants, B.; Cook, P.; Zheng, Y.; Egan, A.; Yushkevich, P. \u0026 Gee, J. N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 2010, 29, 1310-1320\nTustison, N.; Avants, B. Explicit B-spline regularization in diffeomorphic image registration. Frontiers in Neuroinformatics, 2013, 7, 39\nTzourio-Mazoyer, N.; Landeau, B.; Papathanassiou, D.; Crivello, F.; Etard, O.; Delcroix, N.; Mazoyer, B. \u0026 Joliot, M. Automated Anatomical Labeling of activations in SPM using a Macroscopic Anatomical Parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289\nVeraart, J.; Novikov, D. S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. \u0026 Fieremans, E. Denoising of diffusion MRI using random matrix theory. NeuroImage, 2016, 142, 394-406\nYeh, C.-H.; Smith, R. E.; Liang, X.; Calamante, F. \u0026 Connelly, A. Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. NeuroImage, 2016, 142, 150-162\nYeo, B.T.; Krienen, F.M.; Sepulcre, J.; Sabuncu, M.R.; Lashkari, D.; Hollinshead, M.; Roffman, J.L.; Smoller, J.W.; Zollei, L.; Polimeni, J.R.; Fischl, B.; Liu, H. \u0026 Buckner, R.L. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 2011, 106(3), 1125-1165\nZalesky, A.; Fornito, A.; Harding, I. H.; Cocchi, L.; Yücel, M.; Pantelis, C. \u0026 Bullmore, E. T. Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage, 2010, 50, 970-983\nZhang, Y.; Brady, M. \u0026 Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 2001, 20, 45-57\n```\n\n\n\n\n## Help page\n\nThe following help page can equivalently be generated by executing the tool without\nproviding any command-line arguments (within a container environment; note interface\nis slightly different if run natively).\n\n---\n\n## Synopsis\n\nGenerate structural connectomes based on diffusion-weighted and T1-weighted image data using state-of-the-art reconstruction tools, particularly those provided in *MRtrix3*\n\n### Usage\n\n    mrtrix3_connectome.py bids_dir output_dir analysis_level [ options ]\n\n-  *bids_dir*: The directory with the input dataset formatted according to the BIDS standard.\n\n-  *output_dir*: The directory where the output files should be stored.\n\n-  *analysis_level*: Level of analysis that will be performed; options are: preproc, participant, group.\n\n### Description\n\nWhile preproc-level analysis only requires data within the BIDS directory, participant-level analysis requires that the output directory be pre-populated with the results from preproc-level processing; similarly, group-level analysis requires that the output directory be pre-populated with the results from participant-level analysis.\n\nThe operations performed by each of the three levels of analysis are as follows:\n\n\"preproc\": DWI: Denoising; Gibbs ringing removal; motion, eddy current and EPI distortion correction and outlier detection \u0026 replacement; brain masking, bias field correction and intensity normalisation; rigid-body registration \u0026 transformation to T1-weighted image. T1-weighted image: bias field correction; brain masking.\n\n\"participant\": DWI: Response function estimation; FOD estimation. T1-weighted image (if -parcellation is not none): Tissue segmentation; grey matter parcellation. Combined (if -parcellation is not none, or -streamlines is provided): Whole-brain streamlines tractography; SIFT2; connectome construction.\n\n\"group\": Generation of FA-based population template; warping of template-based white matter mask to subject spaces; calculation of group mean white matter response function; scaling of connectomes based on white matter b=0 intensity, response function used during participant-level analysis, and SIFT model proportioinality coefficient; generation of group mean connectome.\n\nThe label(s) provided to the -participant_label and -session_label options correspond(s) to sub-\u003cparticipant_label\u003e and ses-\u003csession_label\u003e from the BIDS spec (so they do _not_ include \"sub-\" or \"ses-\"). Multiple participants / sessions can be specified with a space-separated list.\n\nFor both preproc-level and participant-level analyses, if no specific participants or sessions are nominated by the user (or the user explicitly specifies multiple participants / sessions), the script will process each of these in series. It is additionally possible for the user to invoke multiple instances of this script in order to process multiple subjects at once in parallel, ensuring that no single participant / session is being processed in parallel, and that preproc-level output data are written fully before commencing participant-level analysis.\n\nThe -output_verbosity option principally affects the participant-level analysis, modulating how many derivative files are written to the output directory. Permitted values are from 1 to 4: 1 writes only those files requisite for group-level analysis; 2 additionally writes files typically useful for post-hoc analysis (the default); 3 additionally generates files for enhanced connectome visualisation and copies the entire whole-brain tractogram; 4 additionally generates a full copy of the script scratch directory (with all intermediate files retained) to the output directory (and this applies to all analysis levels)\n\nIf running participant-level analysis using the script as a standalone tool rather than inside the provided container, data pertaining to atlas parcellations can no longer be guaranteed to be stored at a specific location on the filesystem. In this case, the user will most likely need to manually specify the location where the corresponding parcellation is stored using the -atlas_path option.\n\n### Options\n\n+ **--output_verbosity**\u003cbr\u003eThe verbosity of script output (number from 1 to 4).\n\n#### Options that are relevant to participant-level analysis\n\n+ **--parcellation**\u003cbr\u003eThe choice of connectome parcellation scheme (compulsory for participant-level analysis); options are: aal, aal2, brainnetome246fs, brainnetome246mni, craddock200, craddock400, desikan, destrieux, hcpmmp1, none, perry512, yeo7fs, yeo7mni, yeo17fs, yeo17mni.\n\n+ **--streamlines**\u003cbr\u003eThe number of streamlines to generate for each subject (will be determined heuristically if not explicitly set).\n\n+ **--template_reg software**\u003cbr\u003eThe choice of registration software for mapping subject to template space; options are: ants, fsl.\n\n#### Options that are relevant to both preproc-level and participant-level analyses\n\n+ **--t1w_preproc path**\u003cbr\u003eProvide a path by which pre-processed T1-weighted image data may be found for the processed participant(s) / session(s)\n\n#### Options specific to the batch processing of participant data\n\n+ **--participant_label**\u003cbr\u003eThe label(s) of the participant(s) that should be analyzed.\n\n+ **--session_label**\u003cbr\u003eThe session(s) within each participant that should be analyzed.\n\n#### Standard options\n\n+ **-d/--debug**\u003cbr\u003edisplay debugging messages.\n\n+ **-h/--help**\u003cbr\u003edisplay this information page and exit.\n\n+ **-n/--n_cpus number**\u003cbr\u003euse this number of threads in multi-threaded applications (set to 0 to disable multi-threading).\n\n+ **--scratch /path/to/scratch/**\u003cbr\u003emanually specify the path in which to generate the scratch directory.\n\n+ **--skip-bids-validator**\u003cbr\u003eSkip BIDS validation\n\n+ **-v/--version**\u003cbr\u003edisplay version information and exit.\n\n---\n\n**Author:** Robert E. Smith (robert.smith@florey.edu.au)\n\n**Copyright:** Copyright (c) 2016-2020 The Florey Institute of Neuroscience\nand Mental Health.\n\nThis Source Code Form is subject to the terms of the Mozilla Public\nLicense, v. 2.0. If a copy of the MPL was not distributed with this\nfile, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nCovered Software is provided under this License on an \"as is\"\nbasis, without warranty of any kind, either expressed, implied, or\nstatutory, including, without limitation, warranties that the\nCovered Software is free of defects, merchantable, fit for a\nparticular purpose or non-infringing.\nSee the Mozilla Public License v. 2.0 for more details.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbids-apps%2FMRtrix3_connectome","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbids-apps%2FMRtrix3_connectome","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbids-apps%2FMRtrix3_connectome/lists"}