Gradients of brain organization: Smooth sailing from methods development to user community

Jessica Royer, Casey Paquola, Sofie L. Valk, Matthias Kirschner, Seok-Jun Hong, Bo-yong Park, Richard A. I. Bethlehem, Robert Leech, B. T. Thomas Yeo, Elizabeth Jefferies, Jonathan Smallwood, Daniel Margulies, Boris C. Bernhardt
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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.
大脑组织的梯度:从方法开发到用户社区的顺利进行
多模态神经成像为了解人类大脑的结构和功能提供了一个强大的活体窗口。最近在方法论和概念上的进步使人们能够研究大脑结构和功能的大尺度空间趋势或梯度之间的相互作用,提供了一个框架来确定跨多个尺度的大脑组织原理。社会各界对这些技术的强烈热情有助于它们被广泛采用和实施,以回答神经科学中的关键问题。在简要回顾了有关该框架的现有文献之后,这篇视角论文将着重介绍旨在使梯度方法更容易为社会所接受的务实步骤是如何将这些技术推向神经科学探索的前沿的。更具体地说,我们将强调数据共享、开源软件开发以及由不同的早期职业研究人员团队领导的专门研讨会是如何促进人们对梯度方法的兴趣的。为此,我们认为,脑梯度研究的日益兴盛是建立一个包容性社区的协调和持续努力的结果,可以作为神经信息学未来创新和概念进步的一个案例。最后,我们讨论了神经科学理论的不断完善、方法论的创新和现实世界的转化所面临的挑战,以保持我们在脑组织综合模型方面的集体进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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