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脑组织结构梯度:从方法开发到用户社区的顺利推进。

Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community.

机构信息

Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.

Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany.

出版信息

Neuroinformatics. 2024 Oct;22(4):623-634. doi: 10.1007/s12021-024-09660-y. Epub 2024 Apr 3.

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.

摘要

多模态神经影像学为研究人类大脑的结构和功能提供了强大的活体窗口。最近的方法和概念上的进步使我们能够研究大脑结构和功能的大规模空间趋势(或梯度)之间的相互作用,为跨多个尺度统一大脑组织原则提供了一个框架。社区对这些技术的强烈热情是它们广泛采用和实施的关键,以回答神经科学中的关键问题。在简要回顾了关于该框架的当前文献后,本观点文章将重点介绍旨在使梯度方法更易于为社区所接受的务实步骤如何将这些技术推向神经科学研究的前沿。更具体地说,我们将强调梯度方法的兴趣是如何通过数据共享、开源软件开发以及由不同背景的早期职业研究人员领导的专门研讨会组织来激发的。为此,我们认为,对大脑梯度的日益兴奋是建立一个包容性社区的协调一致努力的结果,并且可以作为神经信息学未来创新和概念进步的一个例子。最后,我们讨论了持续改进神经科学理论、方法创新和实际应用以保持我们朝着大脑组织综合模型前进的集体进展所面临的挑战。

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