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Article Dans Une Revue Alzheimer's Research and Therapy Année : 2018

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

1 VU University Medical Center [Amsterdam]
2 IRCCS - Centro San Giovanni di Dio, Fatebenefratelli, Brescia
3 Maastricht University [Maastricht]
4 University Hospitals Leuven [Leuven]
5 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
6 TIMONE - Hôpital de la Timone [CHU - APHM]
7 CIC-CPCET
8 GSK - GlaxoSmithKline [Stevenage, UK]
9 TCDV - Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046
10 CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille]
11 GU - Göteborgs Universitet = University of Gothenburg
12 Pasqual Maragall Foundation
13 UA - University of Antwerp
14 Hospital Network Antwerp Middelheim and Hoge Beuken
15 Center for Research and Advanced Therapies CITA-Alzheimer Foundation [San Sebastián]
16 CHUV - Centre Hospitalier Universitaire Vaudois = Lausanne University Hospital [Lausanne]
17 HUG - Geneva University Hospital
18 Aristotle University of Thessaloniki
19 University of Oxford
20 King‘s College London
21 Universität zu Lübeck = University of Lübeck [Lübeck]
22 Imperial College London
23 UiO - University of Oslo
24 UCL Queen Square Institute of Neurology
25 UK DRI - UK Dementia Research Institute
26 Sahlgrenska University Hospital [Gothenburg]
27 UCB Pharma S.A.[Braine-l'Alleud]
28 Janssen Research & Development
29 Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield
30 Pfizer Global Research and Development [Cambridge, MA, USA]
31 VUMC - Vrije Universiteit Medical Centre
32 UNIGE - Université de Genève = University of Geneva
33 Institutes of Neurology and Healthcare Engineering, UCL, London
Jerome Revillard
  • Fonction : Auteur

Résumé

Background: 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.

Dates et versions

hal-03598100 , version 1 (04-03-2022)

Identifiants

Citer

Mara ten Kate, Alberto Redolfi, Enrico Peira, Isabelle Bos, Stephanie Vos, et al.. MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimer's Research and Therapy, 2018, 10 (1), pp.100. ⟨10.1186/s13195-018-0428-1⟩. ⟨hal-03598100⟩
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