Abstract
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.
Highlights
The human brain is constituted of a vast amount of interconnected neurons forming structural circuits which transmit information
An experimental work was achieved in order to show how the Human Connectomics Ontology” (HCO) could be effectively used to address complex queries concerning in vivo Magnetic Resonance Imaging (MRI) connectomics datasets: a multi-level analysis of the connectivity pattern of the right medial Brodmann Area 6 (BA6) reconstructed by diffusion tractography was achieved, using a set of queries on annotated neuroimaging datasets of five healthy subjects
The HCO was based on NEURO-Description Logics (DL)-Foundational Model of Anatomy (FMA) and aimed at representing nodes connected with fiber bundles assessed by diffusion tractography
Summary
The human brain is constituted of a vast amount of interconnected neurons forming structural circuits which transmit information. After reconstruction of anatomical or functional connectivities from MRI (Jbabdi and Johansen-Berg, 2011), in vivo neuroimaging data can be modeled and analyzed using connectomics in order to produce brain networks at macroscopic scale (∼ 1 cm or greater) (Hagmann et al, 2007; Zalesky et al, 2011 ; Sporns, 2013). There is a great diversity in methodological approaches, especially no consensus currently exists on how to best define nodes for charting in vivo human connectome, i.e., subdividing the brain into macroscopic regions in an anatomofunctional coherent way (Craddock et al, 2013 ; Fortino et al, 2013). A real need exists of new neuroinformatics tools for in vivo human connectomics that allow different levels of granularity of multi-modal connectivity data to be described, shared, integrated and compared
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