Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Quebec, Canada.
Nat Methods. 2022 Nov;19(11):1472-1479. doi: 10.1038/s41592-022-01625-w. Epub 2022 Oct 6.
Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Comparing experimentally generated maps to these reference maps facilitates cross-disciplinary scientific discovery. Although recent data sharing initiatives increase the accessibility of brain maps, data are often shared in disparate coordinate systems, precluding systematic and accurate comparisons. Here we introduce neuromaps, a toolbox for accessing, transforming and analyzing structural and functional brain annotations. We implement functionalities for generating high-quality transformations between four standard coordinate systems. The toolbox includes curated reference maps and biological ontologies of the human brain, such as molecular, microstructural, electrophysiological, developmental and functional ontologies. Robust quantitative assessment of map-to-map similarity is enabled via a suite of spatial autocorrelation-preserving null models. neuromaps combines open-access data with transparent functionality for standardizing and comparing brain maps, providing a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.
成像技术越来越多地被用于生成大脑结构和功能的高分辨率参考图谱。将实验生成的图谱与这些参考图谱进行比较有助于跨学科的科学发现。尽管最近的数据共享计划增加了脑图谱的可访问性,但数据通常在不同的坐标系中进行共享,从而无法进行系统和准确的比较。在这里,我们介绍了 neuromaps,这是一个用于访问、转换和分析结构和功能脑注释的工具包。我们实现了在四个标准坐标系之间生成高质量转换的功能。该工具包包括经过策展的人类大脑参考图谱和生物学本体,例如分子、微观结构、电生理学、发育和功能本体。通过一系列保留空间自相关的 null 模型,实现了对图谱到图谱相似性的稳健定量评估。neuromaps 将开放获取的数据与用于标准化和比较脑图谱的透明功能相结合,为全面的结构和功能注释富集分析提供了一个系统的工作流程,以分析人类大脑。