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.
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
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
TotalReadoutTime. For DWI data, it is also preferable to
SliceTiming fields. More information on these
data can be found in the BIDS documentation.
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.
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.; 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 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
Generate structural connectomes based on diffusion-weighted and T1-weighted image data using state-of-the-art reconstruction tools, particularly those provided in MRtrix3
mrtrix3_connectome.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 that are relevant to participant-level analysis
The verbosity of script output (number from 1 to 3); higher values result in more generated data being included in the output directory
The choice of connectome parcellation scheme (compulsory for participant-level analysis). Options are: aal,aal2,craddock200,craddock400,desikan,destrieux,hcpmmp1,perry512
Indicate that the subject DWI data have been preprocessed, and hence initial image processing steps will be skipped (also useful for testing)
The number of streamlines to generate for each subject
The choice of registration software for mapping subject to template space. Options are: ants, fsl
Options specific to the batch processing of subject data
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.
In the event of encountering an issue with the script, re-run with this flag set to provide more useful information to the developer
Display help information for the script
Use this number of threads in MRtrix3 multi-threaded applications (0 disables multi-threading)
Skip BIDS validation
show program's version number and exit
Author: Robert E. Smith (firstname.lastname@example.org)
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/.
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 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
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