TY - JOUR
T1 - 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)
AU - Bona, Jonathan
AU - Kemp, Aaron S.
AU - Cox, Carli
AU - Nolan, Tracy S.
AU - Pillai, Lakshmi
AU - Das, Aparna
AU - Galvin, James E.
AU - Larson-Prior, Linda
AU - Virmani, Tuhin
AU - Prior, Fred
N1 - Funding Information:
This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Under this contract the University of Arkansas is funded by Leidos Biomedical Research subcontract 16X011. Funding was also provided by U24CA215109. This project is supported in part by the UAMS Translational Research Institute (TRI), grant UL1 TR003107 through the National Center for Advancing Translational Sciences of the National Institutes
Funding Information:
The authors wish to thank the creators of the Neuropsychological Testing Ontology, and in particular Alexander Diehl and Alexander Cox (University at Buffalo), for their input on our use of the NPT within ARIES.
Publisher Copyright:
Copyright © 2022 Bona, Kemp, Cox, Nolan, Pillai, Das, Galvin, Larson-Prior, Virmani and Prior.
PY - 2022/2/10
Y1 - 2022/2/10
N2 - 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.
AB - 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.
KW - imaging informatics
KW - knowledge representation
KW - neuroinformatics
KW - ontologies (artificial intelligence)
KW - semantic web
UR - http://www.scopus.com/inward/record.url?scp=85125291956&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125291956&partnerID=8YFLogxK
U2 - 10.3389/frai.2021.649970
DO - 10.3389/frai.2021.649970
M3 - Article
AN - SCOPUS:85125291956
VL - 4
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
SN - 2624-8212
M1 - 649970
ER -