Raw neuroimaging data require further processing before they can be used for scientific or clinical research. Traditionally, this could be accomplished with a single powerful computer. However, much greater computing power is required to analyze the large open-access cohorts that are increasingly being released to the community. And processing pipelines are inconsistently scripted, which can hinder reproducibility efforts. This creates a barrier for labs lacking access to sufficient resources or technological support, potentially excluding them from neuroimaging research. A paper by Hayashi and colleagues in Nature Methods offers a solution. They present https://brainlife.io, a freely available, web-based platform for secure neuroimaging data access, processing, visualization and analysis. It leverages ‘opportunistic computing’, which pools processing power from commercial and academic clouds, making it accessible to scientists worldwide. This is a step towards lowering the barriers for entry into big data neuroimaging research.

Original reference: Nat. Methods https://doi.org/10.1038/s41592-024-02237-2 (2024)

原始神经影像数据需要进一步处理才能用于科学或临床研究。传统上，这可以通过一台功能强大的计算机来完成。然而，需要更强大的计算能力来分析越来越多地向社区发布的大型开放获取群体。而且处理管道的脚本不一致，这可能会阻碍再现性工作。这给缺乏足够资源或技术支持的实验室造成了障碍，可能将它们排除在神经影像研究之外。 Hayashi 及其同事在《自然方法》上发表的一篇论文提供了一个解决方案。他们推出了 https://brainlife.io，这是一个免费的、基于网络的平台，用于安全的神经影像数据访问、处理、可视化和分析。它利用“机会计算”，汇集商业和学术云的处理能力，使全世界的科学家都可以使用它。这是降低大数据神经影像研究进入门槛的一步。

原文参考： Nat.方法https://doi.org/10.1038/s41592-024-02237-2 (2024)