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dc.contributor.authorMehdipour Ghazi, Mostafa
dc.contributor.authorSelnes, Per
dc.contributor.authorReina, Santiago Timon
dc.contributor.authorTecelão, Sandra
dc.contributor.authorIngala, Silvia
dc.contributor.authorBjørnerud, Atle
dc.contributor.authorKirsebom, Bjørn-Eivind Seljelid
dc.contributor.authorFladby, Tormod
dc.contributor.authorNielsen, Mads
dc.date.accessioned2024-08-26T07:30:42Z
dc.date.available2024-08-26T07:30:42Z
dc.date.issued2024-02-26
dc.description.abstractIntroduction: Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer’s disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors.<p> <p>Methods: In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. <p>Results: Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models’ ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. <p>Discussion: These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.en_US
dc.identifier.citationMehdipour Ghazi, Selnes, Reina ST, Tecelão, Ingala, Bjørnerud, Kirsebom, Fladby, Nielsen. Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts. Frontiers in Aging Neuroscience. 2024;16en_US
dc.identifier.cristinIDFRIDAID 2261730
dc.identifier.doi10.3389/fnagi.2024.1345417
dc.identifier.issn1663-4365
dc.identifier.urihttps://hdl.handle.net/10037/34413
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.relation.journalFrontiers in Aging Neuroscience
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/643417/EU/ERA-NET for establishing synergies between the Joint Programming on Neurodegenerative Diseases Research and Horizon 2020/JPco-fuND/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/681043/EU/Coordination Action in support of the sustainability and globalisation of the Joint Programming Initiative on Neurodegenerative Diseases/JPsustaiND/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/825664/EU/ERA-NET to support the Joint Programming in Neurodegenerative Diseases strategic plan (JPND)/JPCOFUND2/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleComparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohortsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)