Imagine you’re a mechanic and you have to tweak different car parts to make them work perfectly together. That’s exactly what scientists are doing with the new pipeline called versaFlow! It’s like a versatile toolbox that helps researchers process and analyze Diffusion Weighted Imaging (DWI) data from Non-Human Primates (NHP) in a more effective way. Just as NHPs share similar features with humans, the pipeline adjusts and fine-tunes the tools to work seamlessly on NHP images. Using advanced Magnetic Resonance Imaging (MRI) techniques like Diffusion Tensor Imaging (DTI) and Constrained Spherical Deconvolution (CSD), as well as cutting-edge algorithms like DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND), versaFlow provides a modular and scalable solution for DWI analysis. The researchers used versaFlow to explore the variability of diffusion metrics in a primate database called PRIME-DE, which contains brain images acquired at different resolutions, using various scanning techniques, and from multiple vendors. They found that the diffusion data in PRIME-DE exhibits a level of variability similar to or even greater than what is observed in human studies. This suggests the need for standardization in data acquisition protocols and the development of robust algorithms that can handle differences between scanners. Curious to learn more about this powerful pipeline? Dive into the full article!

The lack of “gold standards” in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site’s data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

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