Fantastic measures and how to repeat them: tractography-based test retest reproducibility of microstructural MRI

Project info

Title: Fantastic measures and how to repeat them: tractography-based test retest reproducibility of microstructural MRI

Project lead: Name (Twitter;Mattermost): Kristin Koller (@Kristin_Koller;@kristinkoller), Malwina Molendowska (@Molednowska_m), Elena Kleban (@LNAKleban), Chantal Tax (@ChantalTax), Dmitri Shastin (@Dmitri_Shastin), Veronica Dell-Acqua, Sila Genc (@drsilagenc), Pedro Luque Laguna (@P_Luque_Laguna)

Project collaborators: Name (Twitter;Mattermost): Kristin Koller (@Kristin_Koller;@kristinkoller), Malwina Molendowska (@Molednowska_m), Elena Kleban (@LNAKleban), Chantal Tax (@ChantalTax), Dmitri Shastin (@Dmitri_Shastin), Veronica Dell-Acqua, Sila Genc (@drsilagenc), Pedro Luque Laguna (@P_Luque_Laguna)

Registered Brainhack Global 2020 Event: Brainhack – Micro2Macro, Cardiff, United Kingdom #bhg:micro2macro_gbr_1

Project Description: The goal of our project is to investigate novel approaches to assess repeatability and reproducibility in MRI research. As ‘open science’ becomes integral practice, demonstrating that our MRI measurements are reliable is now easier due to publicly shared datasets. Previous demonstrations of reliability often focus on standard ‘tried and tested’ statistical measures to report repeatability such as intra-class correlation (ICC) coefficient and coefficients of variation (CV). Additionally, metric maps derived from different MRI sequences are often projected onto whole brain skeletons or masks derived from white matter pathways virtually dissected with diffusion tractography. We plan to challenge previous standard approaches by investigating new ways to measure repeatability, by its variability in tractograms beyond volumetric masks and in microstructural measures mapped onto tractograms.

A short example may be:

- Use of different tract properties (e.g. end-points, size, streamline number) to estimate repeatability - Investigating additional strategies (e.g. extra steps that may be critical such as a specific registration step, or data clean up step) that may improve statistical estimation of repeatability

We plan to use the MICRA dataset for this project. The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and computed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols in 6 healthy humans collected at 5 time points. For this project we are also making available tractography results. Access here https://osf.io/z3mkn/

The end result of this collaboration will be to produce and/or evaluate novel reliable methods for the quantification of reliability and reproducibility in tractography/microstructural MRI.

It is intended to pave the way towards a consensus opinion clarifying the concepts of reliability and reproducibility, evaluating on how best to approach the assessment of these qualities in future work and establishing the minimum requirements for their reporting.

Data to use: The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and processed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols. Additionally, for this project we make available tractography results. Access at https://osf.io/z3mkn/

Link to project repository/sources: https://osf.io/z3mkn/ Koller, K., Rudrapatna, S. U., Chamberland, M., Raven, E. P., Parker, G. D., Tax, C. M. W., … Jones, D. K. (2020). MICRA: Microstructural Image Compilation with Repeated Acquisitions. NeuroImage, 117406. https://doi.org/10.1016/j.neuroimage.2020.117406

Goals for Brainhack Global 2020: Expected deliverables of this project are two-fold: 1. Production of novel reliable methods for evaluation of tractography/microstructural MRI reproducibility/repeatability. 2. Reporting of quantitative outcomes.

Good first issues: 1. Tractography with MRTrix https://mrtrix.readthedocs.io/en/latest/reference/commands/tckgen.html 2. List of MRTrix commands to work on dissected tracts https://mrtrix.readthedocs.io/en/latest/reference/commands_list.html 3. https://www.rdocumentation.org/packages/psych/versions/2.0.12/topics/ICC

Skills: Pattern matching, statistics, handling tractograms and imaging data, programming

Tools/Software/Methods to Use: Below we list a couple of commonly used tools, but feel free to use any tool of choice.

MRtrix: https://www.mrtrix.org, FSL https://fsl.fmrib.ox.ac.uk/fsl/fslwiki, Shell BATMAN tutorial for tractography in MRTrix https://osf.io/fkyht/ Processing diffusion data in FSL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide Matlab, R, SPSS https://github.com/scilus/scilpy https://github.com/scilus/scilpy/tree/master/scripts

Communication channels:

https://mattermost.brainhack.org/ https://zoom.us/ (link will be posted in mattermost channel)

Project labels #coding_methods #statistics_method_development #MRI_tractography #validating_existing_measures #2_releases_existing #connectome #data_visualisation #diffusion #hypothesis_testing #MR_methodologies #reproducible_scientific_methods #statistical_modelling #tractography #FSL #MRtrix #comfortable #familiar #new_learners_welcome #R #Shell #Matlab #DWI #MRI #0_no_git_skills

Project Submission

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• Link to your project: could be a code repository, a shared document, etc.
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