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

Author

Mara ten Kate, Alberto Redolfi, Enrico Peira, Isabelle Bos, Stephanie J. Vos, Rik Vandenberghe, Silvy Gabel, Jolien Schaeverbeke, Philip Scheltens, Olivier Blin, Jill C. Richardson, Regis Bordet, Anders Wallin, Carl Eckerstrom, José Luis Molinuevo, Sebastiaan Engelborghs, Christine Van Broeckhoven, Pablo Martinez-Lage, Julius Popp, Magdalini Tsolaki, Frans R. J. Verhey, Alison L. Baird, Cristina Legido-Quigley, Lars Bertram, Valerija Dobricic, Henrik Zetterberg, Simon Lovestone, Johannes Streffer, Silvia Bianchetti, Gerald P. Novak, Jerome Revillard, Mark F. Gordon, Zhiyong Xie, Viktor Wottschel, Giovanni Frisoni, Pieter Jelle Visser, and Frederik Barkhof

Subjects
Alzheimer’s disease, Mild cognitive impairment, Biomarkers, Magnetic resonance imaging, Amyloid, Machine learning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

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.

Published
2018
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2. MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

Author

Christine Van Broeckhoven, Isabelle Bos, Pablo Martinez-Lage, Frans R.J. Verhey, Frederik Barkhof, Alison L. Baird, Magdalini Tsolaki, Régis Bordet, Jolien Schaeverbeke, Sebastiaan Engelborghs, Pieter Jelle Visser, Olivier Blin, Zhiyong Xie, Mark Forrest Gordon, Silvy Gabel, Mara ten Kate, Carl Eckerström, Rik Vandenberghe, Jérôme Revillard, Julius Popp, Cristina Legido-Quigley, Alberto Redolfi, Giovanni B. Frisoni, Simon Lovestone, Gerald Novak, Lars Bertram, Jill C. Richardson, Johannes Streffer, Silvia Bianchetti, José Luis Molinuevo, Anders Wallin, Enrico Peira, Viktor Wottschel, Valerija Dobricic, Philip Scheltens, Henrik Zetterberg, Stephanie J.B. Vos, Radiology and nuclear medicine, Amsterdam Neuroscience - Neurodegeneration, Neurology, Clinical sciences, Pathologic Biochemistry and Physiology, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Psychiatrie & Neuropsychologie, Promovendi MHN, MUMC+: MA Med Staf Spec Psychiatrie (9), VU University Medical Center [Amsterdam], Centro San Giovanni di Dio, Fatebenefratelli, Brescia (IRCCS), Università degli Studi di Brescia = University of Brescia (UniBs), Maastricht University [Maastricht], University Hospitals Leuven [Leuven], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Hôpital de la Timone [CHU - APHM] (TIMONE), CIC-CPCET, GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Troubles cognitifs dégénératifs et vasculaires - U 1171 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CHU Lille, University of Gothenburg (GU), Pasqual Maragall Foundation, University of Antwerp (UA), Hospital Network Antwerp Middelheim and Hoge Beuken, Center for Research and Advanced Therapies CITA-Alzheimer Foundation [San Sebastián], Lausanne University Hospital, Geneva University Hospital (HUG), Aristotle University of Thessaloniki, University of Oxford, King‘s College London, Universität zu Lübeck = University of Lübeck [Lübeck], Imperial College London, University of Oslo (UiO), UCL, Institute of Neurology [London], UK Dementia Research Institute (UK DRI), University College of London [London] (UCL), Sahlgrenska University Hospital [Gothenburg], UCB Pharma S.A.[Braine-l'Alleud], UCB Pharma [Brussels], Janssen Research & Development, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, Pfizer Global Research and Development [Cambridge, MA, USA], Vrije Universiteit Medical Centre (VUMC), Vrije Universiteit Amsterdam [Amsterdam] (VU), Université de Genève = University of Geneva (UNIGE), and Institutes of Neurology and Healthcare Engineering, UCL, London

Subjects
0301 basic medicine, Apolipoprotein E, Oncology, Male, MILD COGNITIVE IMPAIRMENT, Neurology, Support vector machine, European Medical Information Framework for Alzheimer's Disease, Apolipoprotein E4, lcsh:RC346-429, ddc:616.89, 0302 clinical medicine, Biomarker discovery, Medicine(all), medicine.diagnostic_test, DEMENTIA, Brain, Cognition, Alzheimer's disease, Middle Aged, European Medical Information Framework for Alzheimer’s Disease, 3. Good health, ALZHEIMERS-DISEASE, Female, Life Sciences & Biomedicine, PROJECT, Alzheimer’s disease, medicine.medical_specialty, Amyloid, Aged, Alzheimer Disease/diagnostic imaging, Alzheimer Disease/genetics, Alzheimer Disease/pathology, Amyloid beta-Peptides/cerebrospinal fluid, Amyloid beta-Peptides/metabolism, Apolipoprotein E4/genetics, Biomarkers, Brain/diagnostic imaging, Brain/pathology, Cognitive Dysfunction/diagnostic imaging, Cognitive Dysfunction/pathology, Humans, Magnetic Resonance Imaging, ROC Curve, Support Vector Machine, Machine learning, Magnetic resonance imaging, Mild cognitive impairment, Cognitive Neuroscience, Clinical Neurology, ATROPHY, lcsh:RC321-571, 03 medical and health sciences, Atrophy, Alzheimer Disease, Internal medicine, BETA DEPOSITION, mental disorders, medicine, Dementia, Cognitive Dysfunction, support vector machine, Biology, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, lcsh:Neurology. Diseases of the nervous system, SELECTION BIAS, DECLINE, Science & Technology, Amyloid beta-Peptides, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Research, Neurosciences, medicine.disease, PREVENTION, MACHINE, 030104 developmental biology, Neurology (clinical), Neurosciences & Neurology, Human medicine, business, 030217 neurology & neurosurgery
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.

Published
2018
Full Text
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