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.

Requirements

Due to use of the Anatomically-Constrained Tractography (ACT) framework, correction of EPI susceptibility distortions is a prerequisite for this pipeline. Currently, this is only possible within this pipeline through use of the FSL tool topup, which relies on the presence of spin-echo EPI images with differences in phase encoding to estimate the causative inhomogeneity field. In the absence of such data, this pipeline is not currently applicable; though recommendations for alternative mechanisms for such correction in the Issues page are welcome, and development of novel techniques for performing this correction are additionally underway.

While many common DICOM conversion software are capable of providing data characterising the phase and slice encoding performed in the acquisition protocol, which are subsequently used by this pipeline to automate DWI data pre-processing, for some softwares and/or some data (particularly those not acquired on a Siemens platform), such data may not be present in the sidecar JSON files for files in the BIDS dwi/ and fmap/ directories. In this circumstance, it will be necessary for users to manually enter the relevant information into these files in order for this script to be capable of processing the data. Every JSON file in these two directories should contain the BIDS fields PhaseEncodingDirection and TotalReadoutTime. For DWI data, it is also preferable to provide the SliceEncodingDirection and SliceTiming fields. More information on these data can be found in the BIDS documentation.

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 query the help page of the tool:

$ docker run -i --rm 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/output:/output \
      bids/mrtrix3_connectome \
      /bids_dataset /output participant --participant_label 01 --parcellation desikan

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/output:/output \
      bids/mrtrix3_connectome \
      /bids_dataset /output 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 mrtrix3_connectome.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. Note however that this script may make use of MRtrix3 features or bug fixes that have not yet been merged into the master branch; in this case, it may be necessary to install the same version of MRtrix3 as that installed within "Dockerfile".

Documentation

The help page of the tool itself can be generated by executing the script without providing any command-line options. The help page is additionally presented at the bottom of this README page for reference. Documentation regarding the underlying MRtrix3 tools can be found in the official MRtrix3 documentation. Additional information may be found in the online MRtrix3 community forum.

Error Reporting

Experiencing problems? You can either post a private message to me on the MRtrix3 community forum, or you can report it directly to the GitHub issues list. In both cases, please include as much information as possible; this may include re-running the script using the --debug option, which will provide additional information at the terminal, and preserve temporary files generated by the script within your target output directory, which can be forwarded to the developer.

Acknowledgements

Development of this tool was made possible through funding from the National Health and Medical Research Council (NHMRC) of Australia.

The developer acknowledges the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at the Florey Institute of Neuroscience and Mental Health.

The Florey Institute of Neuroscience and Mental Health acknowledges support from the Victorian Government and in particular the funding from the Operational Infrastructure Support Grant.

Citation

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 principally 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 OR Zhang et al., 2001); inter-modal registration (Bhushan et al., 2015); brain extraction (Smith, 2002 OR Iglesias et al., 2011), 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., 2015b); SIFT2 (Smith et al., 2015b); T1 parcellation (((Avants et al., 2008 OR Andersson et al., 2010) AND (Tzourio-Mazoyer et al., 2002 OR Craddock et al., 2012 OR (Zalesky et al., 2010 AND Perry et al., 2017))) OR (Dale et al., 1999 AND (Desikan et al., 2006 OR Destrieux et al., 2010 OR Glasser et al., 2016))); robust structural connectome construction (Smith et al., 2015a; 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. R.; Jenkinson, M. & Smith, S. Non-linear registration, aka spatial normalisation. FMRIB technical report, 2010, TR07JA2
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
Avants, B. B.; Epstein, C. L.; Grossman, M. & 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
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
Craddock, 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
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
Glasser, 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. & Van Essen, D. C. A multi-modal parcellation of human cerebral cortex. Nature, 2016, 536, 171-178
Iglesias, J. E.; Liu, C. Y.; Thompson, P. M. & Tu, Z. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods. IEEE Transactions on Medical Imaging, 2011, 30, 1617-1634
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
Perry, 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
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. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage, 2015a, 104, 253-265
Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 2015b, 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.; Novikov, D. S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. & Fieremans, E. Denoising of diffusion MRI using random matrix theory. NeuroImage, 2016, 142, 394-406
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, 142, 150-162
Zalesky, A.; Fornito, A.; Harding, I. H.; Cocchi, L.; Yücel, M.; Pantelis, C. & Bullmore, E. T. Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage, 2010, 50, 970-983
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

Help page

The following help page can equivalently be generated by executing the tool without providing any command-line arguments.

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

mrtrix3_connectome.py bids_dir output_dir analysis_level [ options ]

Options

Options that are relevant to participant-level analysis

Options specific to the batch processing of subject data

Standard options

Author: Robert E. Smith (robert.smith@florey.edu.au)

Copyright: Copyright (c) 2008-2018 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/.