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

Alzheimer’s Research & Therapy

Olivier Blin¹  Philip Scheltens²  Mara ten Kate²  Pieter Jelle Visser²  Stephanie J. Vos³  Frans R. J. Verhey³  Isabelle Bos³  José Luis Molinuevo⁴  Pablo Martinez-Lage⁵  Henrik Zetterberg⁶  Julius Popp⁷  Frederik Barkhof⁸  Viktor Wottschel⁸  Gerald P. Novak⁹  Cristina Legido-Quigley^1,10  Enrico Peira^1,11  Alberto Redolfi^1,11  Silvia Bianchetti^1,11  Giovanni Frisoni^1,11  Lars Bertram^1,12  Valerija Dobricic^1,12  Jerome Revillard^1,13  Magdalini Tsolaki^1,14  Christine Van Broeckhoven^1,15  Jill C. Richardson^1,16  Johannes Streffer^1,17  Sebastiaan Engelborghs^1,17  Anders Wallin^1,18  Carl Eckerstrom^1,18  Mark F. Gordon^1,19  Regis Bordet^2,20  Rik Vandenberghe^2,21  Silvy Gabel^2,21  Jolien Schaeverbeke^2,21  Alison L. Baird^2,22  Simon Lovestone^2,22  Zhiyong Xie^2,23 

[1] AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance;Alzheimer Center & Department of Neurology, VU University Medical Center;Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University;Barcelona βeta Brain Research Center, Pasqual Maragall Foundation;Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation;Department of Psychiatry and Neurochemistry, University of Gothenburg;Department of Psychiatry, University Hospital of Lausanne;Department of Radiology and Nuclear Medicine, VUMC;Janssen Pharmaceutical Research and Development;King’s College London;Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli;Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck;MAAT;Memory and Dementia Center, 3rd Department of Neurology, “G Papanicolau” General Hospital, Aristotle University of Thessaloniki;Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB;Neurosciences Therapeutic Area Unit, GlaxoSmithKline R&D;Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp;Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg;Teva Pharmaceuticals, Inc.;U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille;University Hospital Leuven;University of Oxford;Worldwide Research and Development, Pfizer Inc;

关键词: Alzheimer’s disease;    Mild cognitive impairment;    Biomarkers;    Magnetic resonance imaging;    Amyloid;    Machine learning;   

DOI  :  10.1186/s13195-018-0428-1

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown