QuantConn

This code was used to process Diffusion-Weighted Images (DWI) for the QuantConn Challenge. QuantConn contributors:

Nancy R. Newlin, Vanderbilt University, Nashville, TN

Neda Jahanshad, Keck School of Medicine of USC, Los Angeles, CA

Kurt Schilling, Vanderbilt University, Nashville, TN

Daniel Moyer, Vanderbilt University, Nashville, TN

Eleftherios Garyfallidis, Indiana University Bloomington, Bloomington, IN

Bennett A. Landman, Vanderbilt University, Nashville, TN

Serge Koudoro, Indiana University Bloomington, Bloomington, IN

Bramsh Chandio, Keck School of Medicine of USC, Los Angeles, CA

http://cmic.cs.ucl.ac.uk/cdmri/challenge.html

Installation

Pull the github repo. All libraries needed for processing will be set up with singularity build (mrtrix, fsl, freesurfer, dipy, nibabel, python, etc)

Usage

Build the singularity.

sudo singularity build --sandbox Prototype NancysSingularity Run the singularity. You will need to bind directories for (1) Diffusion data, (2) Freesurfer output, and (3) output directory.

sudo singularity shell --bind /path/to/dwi/:/DIFFUSION/,/path/to/freesurfer/:/FREESURFER/,/path/for/outputs/:/OUTPUTS/ --writable test1

Expected contents of /path/to/freesurfer/

• The full output from running freesurfer on a T1 from the subject. (Has mri/ directory)
• Example freesurfer command for linux: recon-all -i ${T1wImage} -subjid ${Name of subject specific dir} -sd ${Location of freesurfer installation} -all

Expected contents of /path/to/dwi/

• We expect the names to have "dwi" and extnsions ".nii.gz", ".bvec", and ".bval"

Overview of processing

When running singularity run ..., /CODE/main.sh is executed. This script runs the following processing:

• Tensor fitting
• b0 extraction
• Extract Desikan-Killany ROIs from aparc+aseg.nii.gz (freesurfer output)
• Registration between freesurfer parcellation and diffusion b0
• Estimate response functions
• Get fiber orientation distribution functions (fODFs)
• Get 5tt mask
• Get GMWM boundary
• Tractography
• Connectomics
• Compute graph measures from connectomes

Citations

MRTrix:

• Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C. H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202, 116137. https://doi.org/10.1016/J.NEUROIMAGE.2019.116137

DiPy:

• Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., & Nimmo-Smith, I. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 8(FEB). https://doi.org/10.3389/FNINF.2014.00008/ABSTRACT

Brain Connectivity Toolbox:

• Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/J.NEUROIMAGE.2009.10.003

Streamline Count Invariant adjustment to complex network measures:

• Newlin, N. R., Rheault, F., Schilling, K. G., & Landman, B. A. (2023). Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/JMRI.28631

FSL:

• Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/J.NEUROIMAGE.2011.09.015