Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.
PLoS One. 2009 Sep 29;4(9):e7200. doi: 10.1371/journal.pone.0007200.
Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info.
许多神经科学报告引用了大脑的离散宏观解剖区域，这些区域是根据大脑图谱或分割协议划定的。然而，目前还没有广泛接受的标准来分割皮质和皮质下结构，或者为产生的区域分配标签，许多程序正在被积极使用。以前协调神经解剖命名法的尝试在很大程度上是定性的，主要集中在术语的词库或简单语义映射的开发上。在这里，我们采取了一种截然不同的方法，不考虑区域的名称，而是将它们的定义作为空间实体进行比较，以努力在不同图谱定义的解剖实体之间提供更精确的定量映射。我们开发了一种分析框架来研究这个大脑图谱一致性问题，并将这些方法应用于比较神经影像学界使用的八种不同的标记方法。这些分析产生了条件概率，使不同图谱之间的区域能够进行映射，这些概率也构成了基于图形的方法提取区域集之间高阶关系的输入，以及评估同一大脑的不同分割之间全局相似性的过程的输入。在全局尺度上，总体结果表明，现有的分割方案之间存在相当大的不一致性，对于某些图谱对，一致性水平低于随机水平。在更精细的层面上，这项研究揭示了定义区域集之间的空间关系，这些关系并不明显；对于那些面临基于这些不同解剖模型的结果进行比较的研究人员来说，这些关系具有很高的潜在兴趣，特别是在没有基于坐标的数据的情况下。揭示的空间重叠模式的复杂性表明，试图使用严格的定性和/或分类方法协调解剖分割和命名法存在问题。本研究的详细结果可通过 http://obart.info 上的一个交互式网站获得。
