ArticleJournal of Alzheimer's disease : JAD2017
Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.
Article in Journal of Alzheimer's disease : JAD, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 17 citations in OpenAlex.
- Development of an early alert model for pandemic situations in Germany.Scientific reports · 2023Article
- A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2021Article
- Challenges of Integrative Disease Modeling in Alzheimer's Disease.Frontiers in molecular biosciences · 2019Review
- Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.International journal of molecular sciences · 2015Review
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.
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Registered trials
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