Moreau Tristan, Gibaud Bernard
Medicis, UMR 1099 LTSI, INSERM, University of Rennes 1 Rennes, France.
Front Neuroinform. 2015 Apr 10;9:9. doi: 10.3389/fninf.2015.00009. eCollection 2015.
Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge. The aim of this study was to create a neuroanatomical ontology, called "Human Connectomics Ontology" (HCO), in order to represent macroscopic gray matter regions connected with fiber bundles assessed by diffusion tractography and to annotate MRI connectomics datasets acquired in the living human brain. First a neuroanatomical "view" called NEURO-DL-FMA was extracted from the reference ontology Foundational Model of Anatomy (FMA) in order to construct a gross anatomy ontology of the brain. HCO extends NEURO-DL-FMA by introducing entities (such as "MR_Node" and "MR_Route") and object properties (such as "tracto_connects") pertaining to MR connectivity. The Web Ontology Language Description Logics (OWL DL) formalism was used in order to enable reasoning with common reasoning engines. Moreover, an experimental work was achieved in order to demonstrate how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets. Indeed, neuroimaging datasets of five healthy subjects were annotated with terms of the HCO and a multi-level analysis of the connectivity patterns assessed by diffusion tractography of the right medial Brodmann Area 6 was achieved using a set of queries. This approach can facilitate comparison of data across scales, modalities and species.
不同的非侵入性神经成像模态以及人类连接组学数据集的多层次分析产生了大量异质数据，这些数据难以整合为统一的表示形式。生物医学本体可以为领域知识提供合适的整合框架，以及促进跨尺度、模态和物种的信息检索、数据共享和数据比较的工具。特别是，迫切需要填补神经生物学与活体人类连接组学之间的差距，以便更好地考虑磁共振成像（MRI）中突出的现实情况，并将其与现有的脑知识联系起来。本研究的目的是创建一个神经解剖学本体，称为“人类连接组学本体”（HCO），以表示与通过扩散张量成像评估的纤维束相连的宏观灰质区域，并注释在活体人类大脑中获取的MRI连接组学数据集。首先，从参考本体解剖学基础模型（FMA）中提取一个名为NEURO-DL-FMA的神经解剖学“视图”，以构建大脑的大体解剖学本体。HCO通过引入与MR连通性相关的实体（如“MR_节点”和“MR_路径”）和对象属性（如“tracto_connects”）来扩展NEURO-DL-FMA。使用网络本体语言描述逻辑（OWL DL）形式主义，以便能够使用通用推理引擎进行推理。此外，还开展了一项实验工作，以证明HCO如何能够有效地用于解决有关活体MRI连接组学数据集的复杂查询。实际上，对五名健康受试者的神经成像数据集用HCO术语进行了注释，并使用一组查询对右侧内侧布罗德曼区6通过扩散张量成像评估的连通性模式进行了多层次分析。这种方法可以促进跨尺度、模态和物种的数据比较。
