Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)

Jonathan Bona, Aaron S. Kemp, Carli Cox, Tracy S. Nolan, Lakshmi Pillai, Aparna Das, James E. Galvin, Linda Larson-Prior, Tuhin Virmani, Fred Prior

Research output: Contribution to journalArticlepeer-review

Abstract

Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings). These unique capabilities ensure greater reproducibility of scientific findings across large-scale research projects. The current investigation was conducted with three collaborating teams who are using ARIES in a project focusing on neurodegeneration. Datasets included magnetic resonance imaging (MRI) data as well as non-imaging data obtained from a variety of assessments designed to measure neurocognitive functions (performance scores on neuropsychological tests). We integrate and manage these data with semantic representations based on axiomatically rich biomedical ontologies. These instantiate a knowledge graph that combines the data from the study cohorts into a shared semantic representation that explicitly accounts for relations among the entities that the data are about. This knowledge graph is stored in a triple-store database that supports reasoning over and querying these integrated data. Semantic integration of the non-imaging data using background information encoded in biomedical domain ontologies has served as a key feature-engineering step, allowing us to combine disparate data and apply analyses to explore associations, for instance, between hippocampal volumes and measures of cognitive functions derived from various assessment instruments.

Original languageEnglish (US)
Article number649970
JournalFrontiers in Artificial Intelligence
Volume4
DOIs
StatePublished - Feb 10 2022
Externally publishedYes

Keywords

  • imaging informatics
  • knowledge representation
  • neuroinformatics
  • ontologies (artificial intelligence)
  • semantic web

ASJC Scopus subject areas

  • Artificial Intelligence

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