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

[KristinKoller]

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

Submission checklist

Once the issue is submitted, please check items in this list as you add under ‘Additional project info’

• Link to your project: could be a code repository, a shared document, etc.
• Goals for Brainhack Global 2020: describe what you want to achieve during this brainhack.
• Flesh out at least 2 “good first issues”: those are tasks that do not require any prior knowledge about your project, could be defined as issues in a GitHub repository, or in a shared document.
• Skills: list skills that would be particularly suitable for your project. We ask you to include at least one non-coding skill. Use the issue labels for this purpose.
• Chat channel: A link to a chat channel that will be used during the Brainhack Global 2020 event. This can be an existing channel or a new one. We recommend using the Brainhack space on Mattermost.

Optionally, you can also include information about:

• Number of participants required.
• Twitter-sized summary of your project pitch.
• Provide an image of your project for the Brainhack Global 2020 website.

We would like to think about how you will credit and onboard new members to your project. If you’d like to share your thoughts with future project participants, you can include information about:

• Specify how you will acknowledge contributions (e.g. listing members on a contributing page).
• Provide links to onboarding documents if you have some: