Evidence map›Paper›PMID 35224477›Full record

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).

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

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jonathan BonaDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Aaron S KempDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Carli CoxNeurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Tracy S NolanDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Lakshmi PillaiDepartment of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Aparna DasDepartment of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
James E GalvinDepartment of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Miami, FL, United States.
Linda Larson-PriorDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Tuhin VirmaniDepartment of Neurology, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Fred PriorDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.

Funding

Multicultural Community Dementia ScreeningR01AG071514 · NIA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI GALVIN, JAMES E · 2021 to 2025
$13.8M
NIA NIH HHS R01 AG071514
6 · The paper itself

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.

Indexed as

imaging informaticsknowledge representationneuroinformaticsontologies (artificial intelligence)semantic web

Identifiers

PMID35224477
PMCPMC8866818

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.