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).

Journal: Frontiers in artificial intelligence
Published Date: Feb 10, 2022

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.

Authors

  • Jonathan Bona
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • Aaron S Kemp
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • Carli Cox
    Neurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • Tracy S Nolan
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • Lakshmi Pillai
    Department of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • Aparna Das
    Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • James E Galvin
    Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Miami, FL, United States.
  • Linda Larson-Prior
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
  • Tuhin Virmani
    Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States.
  • Fred Prior
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.

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