versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics Pub Date : 2023-08-10 eCollection Date: 2023-01-01 DOI:10.3389/fninf.2023.1191200
Alex Valcourt Caron, Amir Shmuel, Ziqi Hao, Maxime Descoteaux
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Abstract

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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versaFlow:一个适用于分辨率的扩散MRI处理的通用管道及其在研究PRIME-DE数据库可变性中的应用。
扩散加权成像(DWI)缺乏“金标准”,这使得验证变得繁琐。为了解决这一任务,研究使用了转化分析,将人类的研究结果与其他物种的研究结果进行对比。非人类灵长类动物(NHP)对此特别感兴趣,因为它们的细胞结构与人类密切相关。然而,用于处理和分析的工具必须经过调整和微调,才能在NHP图像上正常工作。在这里,我们提出了versaFlow,这是一个在Nextflow中实现的模块化管道,旨在实现健壮性和可扩展性。该管道适用于任何空间分辨率的体内NHP DWI;它允许可维护性和自定义。使用尖端和最先进的磁共振成像(MRI)处理技术和扩散建模算法,即扩散张量成像(DTI)、约束球面反褶积(CSD)和扩散室成像中各向异性微结构电子环境的分布(DIAMOND),来实现流程和工作流程。使用versaFlow,我们对来自灵长类动物数据交换(PRIME-DE)3个位点的32名受试者的扩散指标的可变性进行了深入研究,该数据交换包括NHP大脑的解剖T1加权(T1w)和T2加权(T2w)图像、功能MRI(fMRI)和DWI。该数据集包括在多个扫描仪供应商上使用单壳和多壳梯度采样在一系列分辨率下获取的图像。我们使用Aix Marseilles现场的数据对versaFlow的处理进行了再现性研究,以确保我们的实施对后续分析中观察到的变异性影响最小。我们报告了大多数指标的非常高的再现性;由于在我们使用的软件中缺乏强制执行随机数种子的机制,只有DIAMOND的伽马分布参数显示出较少的可再现行为。在执行未来的应用程序时,应考虑到这一点。我们表明,PRIME-DE扩散数据显示出很大程度的可变性,与人类研究中获得的结果相似或更大。应谨慎使用它,以防止在统计分析中灌输不确定性。这表明需要在采集协议中进行充分的协调,并开发能够管理由于扫描仪模型和/或供应商的差异而在成像中引起的可变性的稳健算法。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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