Polavaram Sridevi, Ascoli Giorgio A
Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
Brain Inform. 2017 Jun;4(2):123-134. doi: 10.1007/s40708-017-0062-x. Epub 2017 Mar 23.
Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.
神经元形态在不同动物物种、发育阶段、脑区和细胞类型之间以及内部都极为多样。这种多样性在功能上很重要，因为神经元结构强烈影响突触整合、放电动态和网络连接性。因此，轴突和树突分支的数字重建对于量化和模拟神经系统中的信息处理至关重要。NeuroMorpho.Org是一个已建立的数据库，包含全球数百个实验室共享的数以万计的数字重建神经元。每个神经元都根据已发表的参考文献和数据所有者提供的其他详细信息标注了特定的元数据。多年来，所表示的元数据概念数量随着可用数据的增加而增长。然而，到目前为止，缺乏标准化术语和结构合理的元数据模式限制了用户搜索的有效性。在这里，我们提出了一种新的NeuroMorpho.Org元数据组织方式，它基于一组相互关联的层次结构，重点关注动物物种、解剖区域和细胞类型的主要维度。我们已将NeuroMorpho.Org中的每个元数据术语全面映射到这个形式本体，明确解决了由同义词和同音词引起的所有歧义。利用这个一致的框架，我们引入了OntoSearch，这是一项强大的功能，它通过具有自动完成功能的直观基于字符串的用户界面，无缝地实现基于专家知识和逻辑推理的形态学数据检索。除了返回直接匹配搜索标准的数据外，OntoSearch还通过考虑不完整的元数据注释来识别一组可能的匹配项。
