=Paper=
{{Paper
|id=Vol-2931/ICBO_2019_paper_55
|storemode=property
|title=Semantic Integration of Neuroinformatics Data in the Arkansas Image Enterprise System with Open Biomedical Ontologies
|pdfUrl=https://ceur-ws.org/Vol-2931/ICBO_2019_paper_55.pdf
|volume=Vol-2931
|authors=Jonathan P. Bona,Aaron S. Kemp,Tracy Nolan,Linda Larson-Prior,Aparna Das,Tuhin Virmani,Lakshmi Pillai,Mathias Brochhausen,Fred Prior
|dblpUrl=https://dblp.org/rec/conf/icbo/BonaKNLDVPBP19
}}
==Semantic Integration of Neuroinformatics Data in the Arkansas Image Enterprise System with Open Biomedical Ontologies==
https://ceur-ws.org/Vol-2931/ICBO_2019_paper_55.pdf
Semantic Integration of Neuroinformatics Data in the Arkansas Image Enterprise System
with Open Biomedical Ontologies
Jonathan P. Bonaa, Aaron S. Kempa,b, Tracy Nolana, Linda Larson-Priora,b,c, Aparna Dasb,
Tuhin Virmanic, Lakshmi Pillaic, Mathias Brochhausena, and Fred Priora
a
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR
b
Departnent of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR
c
Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR
Abstract diverse data sets that are about the same individuals. The result
is deidentified, curated datasets for third-party exploration
The Arkansas Image Enterprise System (ARIES) pilot project
brings together data from three collaborating investigative ARIES achieves semantic integration of diverse datasets by
teams in an effort to identify common pathways of using representations based on axiomatically-rich ontologies to
neurodegeneration. These datasets include images and image- construct a knowledge graph that combines instance data from
derived features, motor assessments, cognitive assessments, these unique study cohorts into a shared semantic representation.
clinical rating scales, demographics, and clinical data. ARIES This is stored in a triple store database that supports reasoning
achieves semantic integration of its diverse datasets using over and querying the integrated data.
representations based on axiomatically-rich ontologies. We represent the entities that ARIES data collections are about
Keywords: using OBO Foundry resources, taking advantage of the
consistent representational strategy underlying these resources.
Neuroinformatics, semantic integration, imaging
This poster reports on ongoing work on our pilot test case
linking image-derived measures of hippocampal volumes to a
The Arkansas Image Enterprise System (ARIES) is an diverse set of cognitive assessment results. We use the
instantiation of the Platform for Imaging in Precision Medicine Neuropsychological Testing Ontology (3), among others, to
(PRISM) (1). PRISM containerizes The Cancer Image Archive represent, combine, and explore study subjects’ cognitive
(TCIA) (2) technology stack, streamlining its deployment, and assessment results with imaging and image derived measures.
incorporating new tools to organize and analyze data. PRISM’s
development improves TCIA's ability to manage and analyze
integrated datasets. ARIES is both a testbed for PRISM and an Acknowledgements
institutional research data management resource. It demonstrates This work has been funded in whole or in part with federal funds
the capabilities of the PRISM infrastructure’s semantic from the National Cancer Institute, National Institutes of Health
representation approach by integrating diverse data relevant to under Contract No. HHSN261200800001E, subcontract 16X011
research questions about neurodegenerative disease. and grants: U01CA187013, U24CA215109, 3U24CA215109-
The ARIES pilot project combines data from three collaborating 02S1.
investigative teams in an effort to identify common pathways of
neurodegeneration. These data come from three unique study References
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1. Bona JP, et al. Ontology-Enhanced Representations of Non-
Cognitive Impairment (MCI), or Cancer-Related Cognitive
image Data in The Cancer Imaging Archive. Proceedings of the
Impairment (CRCI). The datasets include images and image-
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derived features, motor assessments, cognitive assessments,
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metadata using a shared representation that accounts for both Journal of digital imaging. 2013;26(6):1045-57.
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3. Cox AP, et al. Measuring cognitive functions: Hurdles in the
source data sets. This approach removes obstacles to 1) working
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with different source representations for the same type of
Proceedings of the International Conference on Biomedical
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Ontology. CEUR Workshop Proceedings, Montreal, Canada,
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