ArticleFrontiers in artificial intelligence2021
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).
Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Lessons Learned From a Delayed-Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease.Annals of clinical and translational neurology · 2026Trial
- Sequential patterning of dynamic brain states distinguish Parkinson's disease patients with mild cognitive impairments.NeuroImage. Clinical · 2025Article
- Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease.Journal of clinical and translational science · 2024Article
- Development and implementation of the frog-in-maze game to study upper limb movement in people with Parkinson's disease.Scientific reports · 2023Article
- Development and validation of the early warning system scores ontology.Journal of biomedical semantics · 2023Article
- Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems.Frontiers in medicine · 2023Article
- The PRISM semantic cohort builder: a novel tool to search and access clinical data in TCIA imaging collections.Physics in medicine and biology · 2022Article
- Feasibility of telemedicine research visits in people with Parkinson's disease residing in medically underserved areas.Journal of clinical and translational science · 2022Article
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Authors and funding
10 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.