Journal article
MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study.
- Ten Kate M Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands. m.tenkate1@vumc.nl.
- Redolfi A Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
- Peira E Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
- Bos I Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
- Vos SJ Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
- Vandenberghe R University Hospital Leuven, Leuven, Belgium.
- Gabel S University Hospital Leuven, Leuven, Belgium.
- Schaeverbeke J University Hospital Leuven, Leuven, Belgium.
- Scheltens P Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands.
- Blin O AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance, Marseille, France.
- Richardson JC Neurosciences Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, UK.
- Bordet R U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille, Lille, France.
- Wallin A Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.
- Eckerstrom C Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.
- Molinuevo JL Barcelona βeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
- Engelborghs S Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
- Van Broeckhoven C Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB, Antwerp, Belgium.
- Martinez-Lage P Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain.
- Popp J Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland.
- Tsolaki M Memory and Dementia Center, 3rd Department of Neurology, "G Papanicolau" General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.
- Verhey FRJ Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
- Baird AL University of Oxford, Oxford, UK.
- Legido-Quigley C King's College London, London, UK.
- Bertram L Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany.
- Dobricic V Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany.
- Zetterberg H Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden.
- Lovestone S University of Oxford, Oxford, UK.
- Streffer J Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
- Bianchetti S Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
- Novak GP Janssen Pharmaceutical Research and Development, Titusville, NJ, USA.
- Revillard J MAAT, Archamps, France.
- Gordon MF Teva Pharmaceuticals, Inc., Malvern, PA, USA.
- Xie Z Worldwide Research and Development, Pfizer Inc, Cambridge, MA, USA.
- Wottschel V Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands.
- Frisoni G Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
- Visser PJ Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands.
- Barkhof F Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands.
- 2018-09-29
Published in:
- Alzheimer's research & therapy. - 2018
Alzheimer’s disease
Amyloid
Biomarkers
European Medical Information Framework for Alzheimer’s Disease
Machine learning
Magnetic resonance imaging
Mild cognitive impairment
Support vector machine
Aged
Alzheimer Disease
Amyloid beta-Peptides
Apolipoprotein E4
Biomarkers
Brain
Cognitive Dysfunction
Female
Humans
Magnetic Resonance Imaging
Male
Middle Aged
ROC Curve
Support Vector Machine
English
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.
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1186/s13195-018-0428-1
- PMID 30261928
- Persistent URL
- https://sonar.ch/global/documents/100440
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