MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study.

Détails

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Etat: Serval
Version: de l'auteur
ID Serval
serval:BIB_BD2694DB4008
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study.
Périodique
Alzheimer's research & therapy
Auteur(s)
Ten Kate M., Redolfi A., Peira E., Bos I., Vos S.J., Vandenberghe R., Gabel S., Schaeverbeke J., Scheltens P., Blin O., Richardson J.C., Bordet R., Wallin A., Eckerstrom C., Molinuevo J.L., Engelborghs S., Van Broeckhoven C., Martinez-Lage P., Popp J., Tsolaki M., Verhey FRJ, Baird A.L., Legido-Quigley C., Bertram L., Dobricic V., Zetterberg H., Lovestone S., Streffer J., Bianchetti S., Novak G.P., Revillard J., Gordon M.F., Xie Z., Wottschel V., Frisoni G., Visser P.J., Barkhof F.
ISSN
1758-9193 (Electronic)
Statut éditorial
Publié
Date de publication
27/09/2018
Peer-reviewed
Oui
Volume
10
Numéro
1
Pages
100
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
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.
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.
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.
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.
Mots-clé
Alzheimer’s disease, Amyloid, Biomarkers, European Medical Information Framework for Alzheimer’s Disease, Machine learning, Magnetic resonance imaging, Mild cognitive impairment, Support vector machine
Pubmed
Web of science
Open Access
Oui
Création de la notice
11/10/2018 11:54
Dernière modification de la notice
09/05/2019 0:32
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