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dc.contributor.authorten Kate, Mara
dc.contributor.authorRedolfi, Alberto
dc.contributor.authorPeira, Enrico
dc.contributor.authorBos, Isabelle
dc.contributor.authorVos, Stephanie J
dc.contributor.authorVandenberghe, Rik
dc.contributor.authorGabel, Silvy
dc.contributor.authorSchaeverbeke, Jolien
dc.contributor.authorScheltens, Philip
dc.contributor.authorBlin, Olivier
dc.contributor.authorRichardson, Jill C
dc.contributor.authorBordet, Regis
dc.contributor.authorWallin, Anders
dc.contributor.authorEckerstrom, Carl
dc.contributor.authorMolinuevo, José L
dc.contributor.authorEngelborghs, Sebastiaan
dc.contributor.authorVan Broeckhoven, Christine
dc.contributor.authorMartinez-Lage, Pablo
dc.contributor.authorPopp, Julius
dc.contributor.authorTsolaki, Magdalini
dc.contributor.authorVerhey, Frans R J
dc.contributor.authorBaird, Alison L
dc.contributor.authorLegido-Quigley, Cristina
dc.contributor.authorBertram, Lars
dc.contributor.authorDobricic, Valerija
dc.contributor.authorZetterberg, Henrik
dc.contributor.authorLovestone, Simon
dc.contributor.authorStreffer, Johannes
dc.contributor.authorBianchetti, Silvia
dc.contributor.authorNovak, Gerald P
dc.contributor.authorRevillard, Jerome
dc.contributor.authorGordon, Mark F
dc.contributor.authorXie, Zhiyong
dc.contributor.authorWottschel, Viktor
dc.contributor.authorFrisoni, Giovanni
dc.contributor.authorVisser, Pieter J
dc.contributor.authorBarkhof, Frederik
dc.date.accessioned2018-10-02T05:02:19Z
dc.date.available2018-10-02T05:02:19Z
dc.date.issued2018
dc.identifier.citationAlzheimer's Research & Therapy. 2018 Sep 27;10(1):100
dc.identifier.urihttp://hdl.handle.net/10852/65022
dc.description.abstractBackground With the shift of research focus towards the pre-dementia stage of Alzheimer’s disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. Methods We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. Results In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. Conclusions Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.
dc.language.isoeng
dc.rightsThe Author(s); licensee BioMed Central Ltd.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleMRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
dc.typeJournal article
dc.date.updated2018-10-02T05:02:20Z
dc.creator.authorten Kate, Mara
dc.creator.authorRedolfi, Alberto
dc.creator.authorPeira, Enrico
dc.creator.authorBos, Isabelle
dc.creator.authorVos, Stephanie J
dc.creator.authorVandenberghe, Rik
dc.creator.authorGabel, Silvy
dc.creator.authorSchaeverbeke, Jolien
dc.creator.authorScheltens, Philip
dc.creator.authorBlin, Olivier
dc.creator.authorRichardson, Jill C
dc.creator.authorBordet, Regis
dc.creator.authorWallin, Anders
dc.creator.authorEckerstrom, Carl
dc.creator.authorMolinuevo, José L
dc.creator.authorEngelborghs, Sebastiaan
dc.creator.authorVan Broeckhoven, Christine
dc.creator.authorMartinez-Lage, Pablo
dc.creator.authorPopp, Julius
dc.creator.authorTsolaki, Magdalini
dc.creator.authorVerhey, Frans R J
dc.creator.authorBaird, Alison L
dc.creator.authorLegido-Quigley, Cristina
dc.creator.authorBertram, Lars
dc.creator.authorDobricic, Valerija
dc.creator.authorZetterberg, Henrik
dc.creator.authorLovestone, Simon
dc.creator.authorStreffer, Johannes
dc.creator.authorBianchetti, Silvia
dc.creator.authorNovak, Gerald P
dc.creator.authorRevillard, Jerome
dc.creator.authorGordon, Mark F
dc.creator.authorXie, Zhiyong
dc.creator.authorWottschel, Viktor
dc.creator.authorFrisoni, Giovanni
dc.creator.authorVisser, Pieter J
dc.creator.authorBarkhof, Frederik
dc.identifier.doihttps://doi.org/10.1186/s13195-018-0428-1
dc.identifier.urnURN:NBN:no-67553
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/65022/1/13195_2018_Article_428.pdf
dc.type.versionPublishedVersion
cristin.articleid100


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