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Abstract：
Many different independently published neuroanatomical parcellation schemes (brain maps, nomenclatures, or atlases) can exist for a particular species, although one scheme (a standard scheme) is typically chosen for mapping neuroanatomical data in a particular study. This is problematic for building connection matrices (connectomes) because the terms used to name structures in different parcellation schemes differ widely and interrelationships are seldom defined. Therefore, data sets cannot be compared across studies that have been mapped on different neuroanatomical atlases without a reliable translation method. Because resliceable 3D brain models for relating systematically and topographically different parcellation schemes are still in the first phases of development, it is necessary to rely on qualitative comparisons between regions and tracts that are either inserted directly by neuroanatomists or trained annotators, or are extracted or inferred by collators from the available literature. To address these challenges, we developed a publicly available neuroinformatics system, the Brain Architecture Knowledge Management System (BAMS; http://brancusi.usc.edu/bkms). The structure and functionality of BAMS is briefly reviewed here, as an exemplar for constructing interrelated connectomes at different levels of the mammalian central nervous system organization. Next, the latest version of BAMS rat macroconnectome is presented because it is significantly more populated with the number of inserted connectivity reports exceeding a benchmark value (50,000), and because it is based on a different classification scheme. Finally, we discuss a general methodology and strategy for producing global connection matrices, starting with rigorous mapping of data, then inserting and annotating it, and ending with online generation of large-scale connection matrices.
摘要：
许多不同的独立发表的神经解剖学分割方案(大脑图，命名法，或地图集)可以存在于特定物种，尽管在特定研究中通常选择一种方案（标准方案）来映射神经解剖学数据。这对于构建连接矩阵（连接体）是有问题的，因为在不同的分割方案中用于命名结构的术语差异很大，并且很少定义相互关系。因此,在没有可靠的翻译方法的情况下，数据集无法在映射到不同神经解剖学图谱的研究中进行比较。因为用于关联系统和地形上不同的分割方案的可重新复制的3D大脑模型仍处于开发的第一阶段，有必要依靠神经解剖学或训练有素的注释器直接插入的区域和区域之间的定性比较，或由校对者从现有文献中提取或推断。为了应对这些挑战，我们开发了一个公开的神经信息系统，大脑体系结构知识管理系统(BAMS；http://brancusi。usc.edu/bkms)。本文简要介绍了BAMS的结构和功能,作为在哺乳动物中枢神经系统组织的不同水平上构建相互关联的连接体的范例。接下来,提出了最新版本的BAMS大鼠宏连接体，因为它明显更多的插入连接报告的数量超过基准值（50,000），因为它基于不同的分类方案。最后,我们讨论了生成全球连接矩阵的一般方法和策略，从严格的数据映射开始，然后插入并注释它，并以在线生成大规模连接矩阵结束。
