[论文解读] Gradients of brain organization: Smooth sailing from methods development to user community

Jessica Royer, Casey Paquola+11 more|arXiv (Cornell University)|Feb 16, 2024

Neuroscience, Education and Cognitive Function被引用 6

一句话总结

本观点综述了脑组织梯度方法如何从方法学发展扩展到广泛的社区采用，推动因素包括数据共享、开源工具和包容性工作坊。

ABSTRACT

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends, or gradients, in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.

研究动机与目标

• 总结关于脑梯度及其跨尺度组织原则的现有文献。
• 突出使梯度方法对神经科学界可访问的务实步骤。
• 论证社区建设努力推动了梯度方法的采用及其影响。
• 讨论完善神经科学理论、方法与转化过程面临的挑战。
• 提出一个模型，认为协调且包容的社区实践能在神经信息学领域持续推进。

提出的方法

• 综合最近关于脑梯度及其在脑结构与功能之间的概念统一的文献。
• 强调数据共享和开源软件在方法传播中的作用。
• 强调社区活动（工作坊、多元化领导层）对方法采用的影响。
• 主张将协调的社区方法作为未来神经信息学创新的蓝图。

实验结果

研究问题

• RQ1 脑组织梯度方法如何在神经科学领域实现广泛采用？
• RQ2 哪些社区实践（数据共享、软件、工作坊）最有效地促进了这一采用？
• RQ3 在完善基于梯度的理论并将其转化为实际应用方面，仍存在哪些挑战？

主要发现

• 梯度方法受益于数据共享和开源软件，促进了其采用的加速。
• 专门的工作坊与由早期职业研究人员主导的包容性领导有助于建立充满活力的用户社区。
• 社区驱动的努力被呈现为未来神经信息学创新的成功范例。