Frontiers in Artificial Intelligence (Feb 2022)

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

DOI
https://doi.org/10.3389/frai.2021.649970
Journal volume & issue
Vol. 4

Abstract

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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.

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