Title :

Mri predictors of amyloid pathology: results from the emif-ad multimodal biomarker discovery study

Author(s) :

Ten Kate, Mara [Auteur]
Redolfi, Alberto [Auteur]
Peira, Enrico [Auteur]
Bos, Isabelle [Auteur]
Vos, Stéphanie J. B. [Auteur]
Vandenberghe, Rik [Auteur]
Gabel, Silvy [Auteur]
Schaeverbeke, Jolien [Auteur]
Scheltens, Philip [Auteur]
Blin, Olivier [Auteur]
Richardson, Jill C. [Auteur]
Bordet, Regis [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Wallin, Anders [Auteur]
Eckerstrom, Carl [Auteur]
Molinuevo, Jose Luis [Auteur]
Engelborghs, Sebastiaan [Auteur]
Van Broeckhoven, Christine [Auteur]
Martinez-Lage, Pablo [Auteur]
Popp, Julius [Auteur]
Tsolaki, Magda [Auteur]
Verhey, Frans R. J. [Auteur]
Baird, Alison L. [Auteur]
Legido-Quigley, Cristina [Auteur]
Bertram, Lars [Auteur]
Dobricic, Valerija [Auteur]
Zetterberg, Henrik [Auteur]
Lovestone, Simon [Auteur]
Streffer, Johannes [Auteur]
Bianchetti, Silvia [Auteur]
Novak, Gerald P. [Auteur]
Revillard, Jerome [Auteur]
Gordon, Mark F. [Auteur]
Xie, Zhiyong [Auteur]
Wottschel, Viktor [Auteur]
Frisoni, Giovanni B. [Auteur]
Visser, Pieter Jelle [Auteur]
Barkhof, Frederik [Auteur]

Journal title :

Alzheimer's research & therapy

Abbreviated title :

Alzheimers Res Ther

Volume number :

10

Pages :

100

Publication date :

2018-09-27

ISSN :

1758-9193

English keyword(s) :

European Medical Information Framework for Alzheimer''s Disease
Alzheimer''s disease
Mild cognitive impairment
Biomarkers
Magnetic resonance imaging
Amyloid
Machine learning
Support vector machine

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CHU Lille
CNRS
Inserm
Université de Lille

Collections :

• Lille Neurosciences & Cognition (LilNCog) - U 1172

Research team(s) :

Troubles cognitifs dégénératifs et vasculaires

Submission date :

2019-11-27T14:33:04Z