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一种用于定量评估新型神经影像学结果的网络通信工具箱。

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results.

作者信息

Kong Ru, Spreng R Nathan, Xue Aihuiping, Betzel Richard F, Cohen Jessica R, Damoiseaux Jessica S, De Brigard Felipe, Eickhoff Simon B, Fornito Alex, Gratton Caterina, Gordon Evan M, Holmes Avram J, Laird Angela R, Larson-Prior Linda, Nickerson Lisa D, Pinho Ana Luísa, Razi Adeel, Sadaghiani Sepideh, Shine James M, Yendiki Anastasia, Yeo B T Thomas, Uddin Lucina Q

机构信息

Centre for Translational MR Research and Centre for Sleep & Cognition, National University of Singapore, Singapore, Singapore.

Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.

出版信息

Nat Commun. 2025 Mar 25;16(1):2930. doi: 10.1038/s41467-025-58176-9.

Abstract

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

摘要

大脑可被分解为大规模功能网络,但这些网络的具体空间拓扑结构以及用于描述它们的名称在不同研究中存在差异。这种不一致阻碍了该领域研究结果的解释和整合。我们开发了网络对应工具箱(NCT),以便研究人员能够检查并报告其新的神经成像结果与多个广泛使用的功能性脑图谱之间的空间对应关系。我们提供了几个示例演示,以说明研究人员如何使用NCT来报告他们自己的发现。NCT提供了一种便捷的方法,通过自旋测试排列计算迪西系数,以确定用户定义的图谱与现有图谱标签之间对应关系的大小和统计显著性。采用NCT将使网络神经科学研究人员更容易以标准化方式报告他们的发现,从而有助于提高可重复性,并促进不同研究之间的比较以产生跨学科见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c231/11937327/44689face6d8/41467_2025_58176_Fig1_HTML.jpg

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