Valcourt Caron, A. | bioRxiv Science

bioRxiv2022

High-frequency longitudinal white matter diffusion- & myelin-based MRI database: reliability and variability

Assessing the consistency of quantitative MRI measurements is critical for inclusion in longitudinal studies and clinical trials. Intraclass coefficient correlation and coefficient of variation were used to evaluate the different consistency aspects of diffusion- and myelinbased MRI measures. Multi-shell diffusion and inhomogeneous magnetization transfer datasets were collected from twenty healthy adults at a high-frequency of five MRI sessions. The consistency was evaluated across whole bundles and the track-profile along the bundles. The impact of the fiber populations on the consistency was also evaluated using the number of fiber orientations map. For whole and profile bundles, moderate to high reliability of diffusion and myelin measures were observed. We report higher reliability of measures for multiple fiber populations than single. The overall portrait of the most consistent measurements and bundles drawn from a wide range of MRI techniques presented here will be particularly useful for identifying reliable biomarkers capable of detecting, monitoring and predicting white matter changes in clinical applications and has the potential to inform patient-specific treatment strategies. Key pointsO_LIReliability and variability are excellent to good for DWI measurements, and good to moderate for MT measures for whole bundles and along the bundles. C_LIO_LIThe number of fiber populations affects the reliability and variability of the MRI measurements. C_LIO_LIThe reliability and variability of MRI measurements are also bundle dependent. C_LI

neuroscience↗

bioRxiv2021

mrHARDIflow : A pipeline tailored for the preprocessing and analysis of Multi-Resolution High Angular diffusion MRI and its application to a variability study of the PRIME-DE database

The validation of advanced methods in diffusion MRI requires finer acquisition resolutions, which is hard to acquire with decent Signal-to-Noise Ratio (SNR) in humans. The use of Non-Human Primates (NHP) and anaesthesia is key to unlock valid microstructural maps, but tools must be adapted and configured finely for them to work well. Here, we propose a novel processing pipeline implemented in Nextflow, designed for robustness and scalability, in a modular fashion to allow for maintainability and a high level of customization and parametrization, tailored for the analysis of diffusion data acquired on multiple spatial resolutions. Modules of processes and workflows were implemented upon cutting edge and state-of-the-art MRI processing technologies and diffusion modelling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD) and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND), a multi-tensor distribution estimator. Using our pipeline, we provide an in-depth study of the variability of diffusion models and measurements computed on 32 subjects from 3 sites of the PRIME-DE, a database containing anatomical (T1, T2), functional (fMRI) and diffusion (DWI) imaging of Non-Human Primate (NHP). Together, they offer images acquired over a range of different spatial resolutions, using single-shell and multi-shell b-value gradient samplings, on multiple scanner vendors, that present artifacts at different level of importance. We also perform a reproducibility study of DTI, CSD and DIAMOND measurements outputed by the pipeline, using the Aix-Marseilles site, to ensure our implementation has minimal impact on their variability. We observe very high reproducibility from a majority of diffusion measurements, only gamma distribution parameters computed on the DIAMOND model display a less reproducible behaviour. This should be taken into consideration when future applications are performed. We also show that even if promising, the PRIME-DE diffusion data exhibits a great level of variability and its usage should be done with care to prevent instilling uncertainty in statistical analyses.

neuroscience↗