erramuzpe / mrtrix3_connectome

Generate subject connectomes from raw image data and perform inter-subject connection density normalisation, using tools provided in the *MRtrix3* software package.

Description

This BIDS App enables generation and subsequent group analysis of structural connectomes generated from diffusion MRI data. The analysis pipeline relies primarily on the MRtrix3 software package, and includes a number of state-of-the-art methods for image processing, tractography reconstruction, connectome generation and inter-subject connection density normalisation.

NOTE: App is still under development; script is not guaranteed to be operational for all use cases.

Documentation

Please use the official MRtrix3 documentation for reference. Additional information may be found in the online MRtrix3 community forum.

Error Reporting

Experiencing problems? You can post a message on the MRtrix3 community forum; or if you are confident that what you are experiencing is a genuine issue, you can report it directly to the GitHub issues list. In both cases, please include as much information as possible.

Acknowledgement

When using this pipeline, please use the following snippet to acknowledge the relevant work (amend as appropriate depending on options used):

Structural connectomes were generated using tools provided in the MRtrix3 software package (http://mrtrix.org). This included: DWI denoising (Veraart et al., 2016), Gibbs ringing removal (Kellner et al., 2016), pre-processing (Andersson et al., 2003; Andersson and Sotiropoulos, 2015; Andersson et al., 2016) and bias field correction (Tustison et al., 2010); inter-modal registration (Bhushan et al., 2015); T1 tissue segmentation (Zhang et al., 2001; Smith, 2002; Patenaude et al., 2011; Smith et al., 2012); spherical deconvolution (Tournier et al., 2004; Jeurissen et al., 2014); probabilistic tractography (Tournier et al., 2010) utilizing Anatomically-Constrained Tractography (Smith et al., 2012) and dynamic seeding (Smith et al., 2015); SIFT2 (Smith et al., 2015); T1 parcellation (Tzourio-Mazoyer et al., 2002 OR (Dale et al., 1999 AND (Desikan et al., 2006 OR Destrieux et al., 2010) ) OR Rohlfing et al., 2010 ); robust structural connectome construction (Yeh et al., 2016).

Andersson, J. L.; Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 2003, 20, 870-888
Andersson, J. L. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 2015, 125, 1063-1078
Andersson, J. L. R. & Graham, M. S. & Zsoldos, E. & 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
Bhushan, C.; Haldar, J. P.; Choi, S.; Joshi, A. A.; Shattuck, D. W. & Leahy, R. M. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. NeuroImage, 2015, 115, 269-280
Dale, A. M.; Fischl, B. & Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction NeuroImage, 1999, 9, 179-194
Desikan, 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. & 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
Destrieux, C.; Fischl, B.; Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature NeuroImage, 2010, 53, 1-15
Jeurissen, B; Tournier, J-D; Dhollander, T; Connelly, A & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data NeuroImage, 2014, 103, 411-426
Kellner, 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
Patenaude, B.; Smith, S. M.; Kennedy, D. N. & Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 2011, 56, 907-922
Rohlfing, T.; Zahr, N. M.; Sullivan, E. V. & Pfefferbaum, A. The SRI24 Multi-Channel Atlas of Normal Adult Human Brain Structure. Human Brain Mapping, 2010, 31, 798-819
Smith, S. M. Fast robust automated brain extraction. Human Brain Mapping, 2002, 17, 143-155
Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 2012, 62, 1924-1938
Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 2015, 119, 338-351
Tournier, J.-D.; Calamante, F., Gadian, D.G. & Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage,     2004, 23, 1176-1185
Tournier, J.-D.; Calamante, F. & 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
Tustison, N.; Avants, B.; Cook, P.; Zheng, Y.; Egan, A.; Yushkevich, P. & Gee, J. N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 2010, 29, 1310-1320
Tzourio-Mazoyer, N.; Landeau, B.; Papathanassiou, D.; Crivello, F.; Etard, O.; Delcroix, N.; Mazoyer, B. & 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
Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping using random matrix theory Magn. Res. Med., 2016, early view, doi:10.1002/mrm.26059
Yeh, C.H.; Smith, R.E.; Liang, X.; Calamante, F.; Connelly, A. Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. Neuroimage, 2016, doi: 10.1016/j.neuroimage.2016.05.047
Zhang, Y.; Brady, M. & 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

Synopsis

Generate structural connectomes based on diffusion-weighted and T1-weighted image data using state-of-the-art reconstruction tools, particularly those provided in MRtrix3

Usage

run.py bids_dir output_dir analysis_level [ options ]

• bids_dir: The directory with the input dataset formatted according to the BIDS standard.
• output_dir: The directory where the output files should be stored. If you are running group level analysis, this folder should be prepopulated with the results of the participant level analysis.
• analysis_level: Level of the analysis that will be performed. Multiple participant level analyses can be run independently (in parallel) using the same output_dir. Options are: participant, group

Options

Options that are relevant to participant-level analysis

• --parcellation
  The choice of connectome parcellation scheme (compulsory for participant-level analysis). Options are: aal, aal2, fs_2005, fs_2009

• --preprocessed
  Indicate that the subject DWI data have been preprocessed, and hence initial image processing steps will be skipped (also useful for testing)

• --streamlines
  The number of streamlines to generate for each subject

Options specific to the batch processing of subject data

• --participant_label
  The label(s) of the participant(s) that should be analyzed. The label(s) correspond(s) to sub-<participant_label> from the BIDS spec (so it does not include "sub-"). If this parameter is not provided, all subjects will be analyzed sequentially. Multiple participants can be specified with a space-separated list.

Standard options

• -d/--debug
  In the event of encountering an issue with the script, re-run with this flag set to provide more useful information to the developer

• -h/--help
  Display help information for the script

• -n/--n_cpus number
  Use this number of threads in MRtrix3 multi-threaded applications (0 disables multi-threading)

• -v/--version
  show program's version number and exit

---

Author: Robert E. Smith ([email protected])

Copyright: Copyright (c) 2008-2017 the MRtrix3 contributors.

This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, you can obtain one at http://mozilla.org/MPL/2.0/.

MRtrix is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

For more details, see http://www.mrtrix.org/.

Instructions

The bids/MRtrix3_connectome Docker container enables users to generate structural connectomes from diffusion MRI data using state-of-the-art techniques. The pipeline requires that data be organized in accordance with the BIDS specification.

In your terminal, type:

$ docker pull bids/mrtrix3_connectome

To run the script in participant level mode (for processing one subject only), use e.g.:

$ docker run -i --rm \
      -v /Users/yourname/data:/bids_dataset \
      -v /Users/yourname/outputs:/outputs \
      bids/mrtrix3_connectome \
      /bids_dataset /outputs participant --participant_label 01 --parcellation fs_2005

Following processing of all participants, the script can be run in group analysis mode using e.g.:

$ docker run -i --rm \
      -v /Users/yourname/data:/bids_dataset \
      -v /Users/yourname/outputs:/outputs \
      bids/mrtrix3_connectome \
      /bids_dataset /outputs group

If you wish to run this script on a computing cluster, we recommend the use of Singularity. Although built for Docker, this container can be converted using the docker2singularity tool.

The script run.py can additionally be used outside of this Docker container, as a stand-alone Python script build against the MRtrix3 Python libraries. Using the script in this way requires setting the PYTHONPATH environment variable to include the path to the MRtrix3 lib/ directory where it is installed on your local system, as described here. When used in this way, the command-line interface of the script will be more consistent with the rest of MRtrix3.