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Grant: Operating Grant: Methodological Innovations for Neuroimaging Datasets

Université Laval

$212,868.00

AD ontological architecture for neuroimaging datawarehouse: merging and establishing anatomical normative data from 17 datasets

EF 2013-14

Résumé
No single neuroimaging dataset is sufficiently large to provide a comprehensive understanding of the AD disease process, propose accurate diagnostic and prognostic, and evaluate response to therapy. Yet, federating databases remains an arduous task, as we cannot rely on any domain-specific interoperability framework. We propose an ontology-based strategy to alleviate this situation. We will use ontologies to merge 17 neuroimaging-centric datasets, process them identically, and establish population norms for anatomical structures and metabolic information. We will implement the system locally, and disseminate the ontology nationally and internationally.
Programme
Operating Grant: Methodological Innovations for Neuroimaging Datasets
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
CIHR Funding Decisions Database

Tel que publié

Id
283588
Name
Duchesne Simon
Region
Québec
Country
Canada
Orgtype
Institution Paid; Research Institution
Instname2
Aging
Theme2
Biomedical
Prcname2
Methodological Innovations for Neuroimaging Datasets
Projecttitle
AD ontological architecture for neuroimaging datawarehouse: merging and establishing anatomical normative data from 17 datasets
Programtype2
Operating Grants
Orgnameinp2
Université Laval
Cihramount2
$212,868
Cihrequipment2
$10,000
Programname2
Operating Grant: Methodological Innovations for Neuroimaging Datasets
Keyworddelim
Alzheimer'S Disease; Anatomical Structures; Biomarkers; Datawarehouse; Domain Ontology; Metabolic Information; Multimodality; Ontological Processing; Population Norms; Related Disorders
Abstract
No single neuroimaging dataset is sufficiently large to provide a comprehensive understanding of the AD disease process, propose accurate diagnostic and prognostic, and evaluate response to therapy. Yet, federating databases remains an arduous task, as we cannot rely on any domain-specific interoperability framework. We propose an ontology-based strategy to alleviate this situation. We will use ontologies to merge 17 neuroimaging-centric datasets, process them identically, and establish population norms for anatomical structures and metabolic information. We will implement the system locally, and disseminate the ontology nationally and internationally.
Competitiondate
201304
Approvedterm2
3 yrs 0 mth
Pinamesdelim
Duchesne, Simon
Approvedterm
3 ans 0 mois
Cihrcontribution2
~ $212,868~ $10,000~