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
- Eickhoff S, Nichols TE, Van Horn JD, Turner JA. Sharing the wealth: neuroimaging data repositories. Neuroimage. 2016;124(Pt B):1-8.
- Hodge MR, Horton W, Brown T, et al. ConnectomeDB—sharing human brain connectivity data. Neuroimage. 2016;124:1102-7.
- 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.
