Name: FibreDensityAndCrosssection
Owner: BIDS Apps
Description: null
Created: 2016-08-02 16:00:30.0
Updated: 2017-10-04 00:10:03.0
Pushed: 2017-02-06 07:11:45.0
Homepage: null
Size: 50
Language: Python
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This BIDS App enables group analysis of diffusion MRI data by performing a Fixel-Based Analysis (FBA) of Fibre Density, Fibre Cross-section and a combined measure (Fibre Density & Cross-section).
The analysis pipeline relies primarily on the MRtrix3 software package.
Full documentation for MRtrix3 is available online here. This pipeline performs pre-processing of diffusion MRI data according to the steps outlined here, and fixel-based analysis using the steps listed here.
For help and support please post a question on the MRtrix3 discussion.
When using this pipeline, please include the following paragraph to descibe the method used. Citations can be found in the attached bibtex file.
Pre-processing was performed by first denoising the diffusion-weighted images (Veraart et al. 2016), followed by eddy-current and motion correction (Andersson et al. 2016). Fibre orientation distributions (FOD) were computed using multi-tissue constrained spherical deconvolution (Jeurissen et al. 2014). Simultaneous bias field correction and intensity normalisation (across subjects) was performed as per Raffelt et al. 2017. All subjects were registered to a study-specific FOD template using an iterative update approach (Raffelt et al. 2011, Raffelt et al. 2012b). Three quantitative measures were computed for each white matter fixel: apparent Fibre Density (FD) (Raffelt et al. 2012), Fibre Cross-section (FC)(Raffelt et al. 2017) and also a combined measure Fibre Density and Cross-section (FDC)(Raffelt et al. 2017b).Statistical analysis was performed using connectivity-based fixel enhancement (CFE)(Raffelt et al. 2015). CFE exploits fixel-fixel connectivity information derived from whole-brain fibre tractography streamlines computed on the FOD template (Tournier et al. 2010). Following tractography, SIFT was used to reduce tractogram biases (Smith et al. 2013). We assigned family-wise error corrected p-values to each fixel using permutation testing of the CFE enhanced t-statistics (5000 permutations).
If your BIDS dataset has reverse phase encoded b=0 or DWI image pairs, please also add this sentence to the paragraph above (after the eddy-current correction): Suceptibility-induced distortions were corrected with reverse phase encoded pairs (Andersson et al. 2003).
This pipeline requires that data be organized in accordance with the BIDS spec.
To get your container ready to run type the following your terminal:
cker pull bids/fibredensityandcrosssection
Before starting, let's check out the help page
cker run -ti bids/fibredensityandcrosssection -h
PSIS
run.py [ options ] bids_dir output_dir analysis_level
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. Valid
choices are: [participant1, group1, participant2, group2,
participant3, group3, participant4, group4]. Multiple
participant level analyses can be run independently(in
parallel) using the same output_dir.
RIPTION
Perform group analysis of diffusion MRI data with a Fixel-Based Analysis
(FBA) of Fibre Density, Fibre Cross-section and a combined measure (Fibre
Density & Cross-section). The analysis pipeline relies primarily on the
MRtrix3 software package (www.mrtrix.org).
onal arguments
participant_label <space-separated list>
The label(s) of the participant(s) that should be analyzed. The label
corresponds to sub-<participant_label> from the BIDS spec (so it does not
include "sub-"). If this parameter is not provided all subjects should be
analyzed. Multiple participants can be specified with a space separated
list.
n_cpus INT
The number of CPU cores available on the compute node. Set to 0 to use the
maximum number of cores available
ons for this Fibre Density and Cross-section BIDS-App
ox_size FLOAT
define the voxel size (in mm) to be used during the upsampling step
(participant1 analysis level only)
roup_subset GROUP_SUBSET
Define a subset of participants to be used when generating the group-
average FOD template and response functions. The subset is to be supplied
as a comma separate list. Note the subset should be representable of your
entire population and not biased towards one particular group. For example
in a patient-control comparison, choose equal numbers of patients and
controls. Used in group1 and group2 analysis levels.
um_tracks INT
define the number of streamlines to be computed when performing
tractography on the FOD template. (group3 analysis level only)
um_tracks_sift INT
define the number of streamlines to remain after performing SIFT on the
tractogram(group3 analysis level only)
dard options
ontinue <TempDir> <LastFile>
Continue the script from a previous execution; must provide the temporary
directory path, and the name of the last successfully-generated file
orce
Force overwrite of output files if pre-existing
elp
Display help information for the script
uiet
Suppress all console output during script execution
erbose
Display additional information and progress for every command invoked
ebug
Display additional debugging information over and above the verbose output
OR
David Raffelt (david.raffelt@florey.edu.au)
To run the pipeline using docker
This fixel-based analysis pipeline has been broken up into several stages, each defined by the “analysis level” positional argument. Each level is labelled as either participant or group. Participant levels can be run on different subjects independently, while group level analysis is performed on all subjects within the group. The order in which the analysis should be run is participant1, group1, participant2, group2, participant3, group3, particpant4, group4.
In order to share data between our container and the rest of our machine, we need to mount a volume. Docker does this with the -v flag. Docker expects its input formatted as: -v path/to/local/data:/path/in/container.
For example, to run the first particpant level analysis on a single subject use:
er run -i --rm -v /path/to/local/data:/bids_input -v /path/to/local/output:/output bids/fibredensityandcrosssection /bids_input /output participant1 --participant_label 01
For example, to run the first particpant level analysis on a single subject use: