Publications

Jonathan P. Bona, Joseph Utecht, and Mathias Brochhausen.

American Medical Informatics Association 2019 (AMIA) Annual Symposium, Poster session, Washington, DC, November 16 – 20, 2019.

The Platform for Imaging in Precision Medicine initiative seeks to sustain and expand core capabilities of The Cancer Imaging Archive to support the evolving requirements of cancer Precision Medicine research. This poster highlights work on developing PRISM’s capabilities to integrate and manage clinical, patient demographic, and other non-image data accompanying image collections. We are integrating these data into a single semantic knowledge graph in a triple store database, and developing tools to explore these data.

Jonathan P. Bona, Mathias Brochhausen, and William R. Hogan.

BMC bioinformatics 20.21 (2019): 1-14. PMID: 31865907

BACKGROUND:

The Drug Ontology (DrOn) is a modular, extensible ontology of drug products, their ingredients, and their biological activity created to enable comparative effectiveness and health services researchers to query National Drug Codes (NDCs) that represent products by ingredient, by molecular disposition, by therapeutic disposition, and by physiological effect (e.g., diuretic). It is based on the RxNorm drug terminology maintained by the U.S. National Library of Medicine, and on the Chemical Entities of Biological Interest ontology. Both national drug codes (NDCs) and RxNorm unique concept identifiers (RXCUIS) can undergo changes over time that can obfuscate their meaning when these identifiers occur in historic data. We present a new approach to modeling these entities within DrOn that will allow users of DrOn working with historic prescription data to more easily and correctly interpret that data.

RESULTS:

We have implemented a full accounting of national drug codes and RxNorm unique concept identifiers as information content entities, and of the processes involved in managing their creation and changes. This includes an OWL file that implements and defines the classes necessary to model these entities. A separate file contains an instance-level prototype in OWL that demonstrates the feasibility of this approach to representing NDCs and RXCUIs and the processes of managing them by retrieving and representing several individual NDCs, both active and inactive, and the RXCUIs to which they are connected. We also demonstrate how historic information about these identifiers in DrOn can be easily retrieved using a simple SPARQL query.

CONCLUSIONS:

An accurate model of how these identifiers operate in reality is a valuable addition to DrOn that enhances its usefulness as a knowledge management resource for working with historic data.

Aaron S. Kemp, Jonathan P. Bona, Tracy S. Nola, Linda Larson-Prior, Tuhin Virmani, Lakshmi Pillai, Mathias Brochhausen, Lawrence Tarbox, and Fred Prior.

American Medical Informatics Association 2019 (AMIA) Annual Symposium, Poster session, Washington, DC, November 16 – 20, 2019.

Abstract:

Introduction
Neuroimaging is among the most active research domains for the creation and management of open-access data repositories1. Heavy emphasis has been placed on functional magnetic resonance imaging (fMRI) data for both disease specific collections and healthy brain function2. The Cancer Imaging Archive (TCIA) has been the National Cancer Institute’s principal imaging resource and has encouraged and supported open-science research by acquiring, curating, hosting and managing collections of multi-modal information3. The TCIA technology stack is currently being refactored into a more streamlined, easily maintained, containerized package which we have labeled PRISM: Platform for Imaging in Precision Medicine. One of the first applications of PRISM is the establishment of a neuroimaging research data management system at the University of Arkansas for Medical Sciences, which is known as the Arkansas Image Enterprise System (ARIES). As the first instantiation of the PRISM infrastructure, the ARIES project aims to explore the practical utility and usability of the full set of capabilities that this new platform provides. In particular, the integration of semantic representations of multi-modal data elements from a variety of disparate sources (e.g., imaging, behavioral, or cognitive assessments), across image processing stages (e.g., preprocessing steps, neuroanatomical segmentation schemes, analytic pipelines), as well as descriptions of the derived results would ensure greater reproducibility and comparability of scientific findings across large-scale neuroimaging research projects.

User Groups and Pilot Data
Pilot testing of the ARIES instantiation of PRISM is being conducted with three collaborating investigative teams who are using ARIES in a project designed to identify common pathways of neurodegeneration. The dataset for the pilot test includes neuroimaging measures (structural and functional MRI and EEG) as well as endophenotypic data obtained from a variety of assessments designed to measure neuro-motor integration (wearable body sensors, gait-assessment floor mat, digitized gloves, and handwriting/drawing assessments on a digitizing tablet) and neurocognitive functions (performance scores on standardized neuropsychological tests and cognitive activation tasks from functional imaging) in three unique study cohorts diagnosed with Parkinson’s disease (PD), Mild Cognitive Impairment (MCI), or Cancer-Related Cognitive Impairment (CRCI).

Semantic Integration Approach
To integrate and manage these data we are building semantic representations using axiomatically-rich ontologies. These will be used to instantiate a knowledge graph that combines the data from these unique study cohorts into a shared semantic representation that explicitly accounts for relations among these data. This amounts to providing queryable relationships across the source data sets. This knowledge graph is stored in a triple store database that supports reasoning over and querying these integrated data. We believe that these unigue capabilities will facilitate the discovery of important new linkages among endophenotypic expressions of disturbed neural functions and discrete neuroanatomical markers of neurodegeneration obtained from the derived neuroimaging results

Conclusion

Semantic integration of neuroinformatic processes in the ARIES pilot project demonstrates the capabilities of the PRISM infrastructure to effectively represent detailed neuroanatomical segmentation schemata and processing pipelines for image analyses, integrate a diverse set of multi-modal data elements, and provide detailed descriptions of the results obtained across the analytic processing stages and in relation to the supporting endophenotypic data. Such capabilities are essential to ensure greater reproducibility in large-scale neuroimaging research projects.

References

1. Eickhoff S, Nichols TE, Van Horn JD, Turner JA. Sharing the wealth: neuroimaging data repositories. Neuroimage. 2016;124(Pt B):1-8.
2. Hodge MR, Horton W, Brown T, et al. ConnectomeDB—sharing human brain connectivity data. Neuroimage. 2016;124:1102-7.
3. Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013;26(6):1045-57.