BIDS-Apps/MRtrix3_connectome

Name: MRtrix3_connectome

Owner: BIDS Apps

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

Created: 2016-08-04 16:26:23.0

Updated: 2018-05-24 11:54:35.0

Pushed: 2018-05-24 06:23:29.0

Homepage: http://www.mrtrix.org/

Size: 341

Language: Python

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README

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.

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 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.

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 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).

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rsson, 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
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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 ]

• 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

• –output_verbosity
  The verbosity of script output (number from 1 to 3); higher values result in more generated data being included in the output directory

• –parcellation
  The choice of connectome parcellation scheme (compulsory for participant-level analysis). Options are: aal,aal2,craddock200,craddock400,desikan,destrieux,hcpmmp1,perry512

• –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

• –template_reg
  The choice of registration software for mapping subject to template space. Options are: ants, fsl

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- 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)

• -s/–skip-bids-validator
  Skip BIDS validation

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

---

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/.

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:

cker pull bids/mrtrix3_connectome

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

cker 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.:

cker 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“.