MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
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 wit...
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| Published in: | Alzheimer's research & therapy Vol. 10; no. 1; pp. 100 - 12 |
|---|---|
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
27.09.2018
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1758-9193, 1758-9193 |
| Online Access: | Get full text |
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| 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. |
|---|---|
| AbstractList | 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.BACKGROUNDWith 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.METHODSWe 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.RESULTSIn 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.CONCLUSIONSAmyloid 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. 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. 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) [epsilon]4 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 [+ or -] 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 [+ or -] 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 [+ or -] 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 [epsilon]4 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 [+ or -] 0.07 in MCI and an AUC of 0.74 [+ or -] 0.08 in CN. In CN, selected features for the classifier included APOE [epsilon]4, 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 [epsilon]4 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 [epsilon]4 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. Keywords: Alzheimer's disease, Mild cognitive impairment, Biomarkers, Magnetic resonance imaging, Amyloid, Machine learning, Support vector machine, European Medical Information Framework for Alzheimer's Disease 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. 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. 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) [epsilon]4 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 [+ or -] 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 [+ or -] 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 [+ or -] 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 [epsilon]4 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 [+ or -] 0.07 in MCI and an AUC of 0.74 [+ or -] 0.08 in CN. In CN, selected features for the classifier included APOE [epsilon]4, 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 [epsilon]4 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 [epsilon]4 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. 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. 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) epsilon 4 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 +/- 72, 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 epsilon 4 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 +/- O. 07 in MCI and an AUC of 0.74 +/- 0.08 in CN. In CN, selected features for the classifier included APOE epsilon 4, 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 epsilon 4 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 epsilon 4 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. |
| ArticleNumber | 100 |
| Audience | Academic |
| Author | Richardson, Jill C. Streffer, Johannes Engelborghs, Sebastiaan Frisoni, Giovanni ten Kate, Mara Tsolaki, Magdalini Wallin, Anders Legido-Quigley, Cristina Redolfi, Alberto Bos, Isabelle Bianchetti, Silvia Dobricic, Valerija Baird, Alison L. Revillard, Jerome Barkhof, Frederik Blin, Olivier Bordet, Regis Schaeverbeke, Jolien Zetterberg, Henrik Vos, Stephanie J. Bertram, Lars Novak, Gerald P. Eckerstrom, Carl Verhey, Frans R. J. Xie, Zhiyong Gabel, Silvy Gordon, Mark F. Wottschel, Viktor Visser, Pieter Jelle Peira, Enrico Molinuevo, José Luis Van Broeckhoven, Christine Vandenberghe, Rik Martinez-Lage, Pablo Popp, Julius Lovestone, Simon Scheltens, Philip |
| Author_xml | – sequence: 1 givenname: Mara orcidid: 0000-0002-8290-8543 surname: ten Kate fullname: ten Kate, Mara email: m.tenkate1@vumc.nl organization: Alzheimer Center & Department of Neurology, VU University Medical Center – sequence: 2 givenname: Alberto surname: Redolfi fullname: Redolfi, Alberto organization: Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli – sequence: 3 givenname: Enrico surname: Peira fullname: Peira, Enrico organization: Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli – sequence: 4 givenname: Isabelle surname: Bos fullname: Bos, Isabelle organization: Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University – sequence: 5 givenname: Stephanie J. surname: Vos fullname: Vos, Stephanie J. organization: Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University – sequence: 6 givenname: Rik surname: Vandenberghe fullname: Vandenberghe, Rik organization: University Hospital Leuven, Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven – sequence: 7 givenname: Silvy surname: Gabel fullname: Gabel, Silvy organization: University Hospital Leuven, Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven – sequence: 8 givenname: Jolien surname: Schaeverbeke fullname: Schaeverbeke, Jolien organization: University Hospital Leuven, Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven – sequence: 9 givenname: Philip surname: Scheltens fullname: Scheltens, Philip organization: Alzheimer Center & Department of Neurology, VU University Medical Center – sequence: 10 givenname: Olivier surname: Blin fullname: Blin, Olivier organization: AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance – sequence: 11 givenname: Jill C. surname: Richardson fullname: Richardson, Jill C. organization: Neurosciences Therapeutic Area Unit, GlaxoSmithKline R&D – sequence: 12 givenname: Regis surname: Bordet fullname: Bordet, Regis organization: U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille – sequence: 13 givenname: Anders surname: Wallin fullname: Wallin, Anders organization: Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg – sequence: 14 givenname: Carl surname: Eckerstrom fullname: Eckerstrom, Carl organization: Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg – sequence: 15 givenname: José Luis surname: Molinuevo fullname: Molinuevo, José Luis organization: Barcelona βeta Brain Research Center, Pasqual Maragall Foundation – sequence: 16 givenname: Sebastiaan surname: Engelborghs fullname: Engelborghs, Sebastiaan organization: Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken – sequence: 17 givenname: Christine surname: Van Broeckhoven fullname: Van Broeckhoven, Christine organization: Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB, Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp – sequence: 18 givenname: Pablo surname: Martinez-Lage fullname: Martinez-Lage, Pablo organization: Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation – sequence: 19 givenname: Julius surname: Popp fullname: Popp, Julius organization: Department of Psychiatry, University Hospital of Lausanne, Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals – sequence: 20 givenname: Magdalini surname: Tsolaki fullname: Tsolaki, Magdalini organization: Memory and Dementia Center, 3rd Department of Neurology, “G Papanicolau” General Hospital, Aristotle University of Thessaloniki – sequence: 21 givenname: Frans R. J. surname: Verhey fullname: Verhey, Frans R. J. organization: Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University – sequence: 22 givenname: Alison L. surname: Baird fullname: Baird, Alison L. organization: University of Oxford – sequence: 23 givenname: Cristina surname: Legido-Quigley fullname: Legido-Quigley, Cristina organization: King’s College London – sequence: 24 givenname: Lars surname: Bertram fullname: Bertram, Lars organization: Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, School of Public Health, Imperial College London, Department of Psychology, University of Oslo – sequence: 25 givenname: Valerija surname: Dobricic fullname: Dobricic, Valerija organization: Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck – sequence: 26 givenname: Henrik surname: Zetterberg fullname: Zetterberg, Henrik organization: Department of Psychiatry and Neurochemistry, University of Gothenburg, Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, UK Dementia Research Institute at UCL, Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital – sequence: 27 givenname: Simon surname: Lovestone fullname: Lovestone, Simon organization: University of Oxford – sequence: 28 givenname: Johannes surname: Streffer fullname: Streffer, Johannes organization: Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, UCB Biopharma SPRL – sequence: 29 givenname: Silvia surname: Bianchetti fullname: Bianchetti, Silvia organization: Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli – sequence: 30 givenname: Gerald P. surname: Novak fullname: Novak, Gerald P. organization: Janssen Pharmaceutical Research and Development – sequence: 31 givenname: Jerome surname: Revillard fullname: Revillard, Jerome organization: MAAT – sequence: 32 givenname: Mark F. surname: Gordon fullname: Gordon, Mark F. organization: Teva Pharmaceuticals, Inc., Boehringer Ingelheim Pharmaceuticals, Inc – sequence: 33 givenname: Zhiyong surname: Xie fullname: Xie, Zhiyong organization: Worldwide Research and Development, Pfizer Inc – sequence: 34 givenname: Viktor surname: Wottschel fullname: Wottschel, Viktor organization: Department of Radiology and Nuclear Medicine, VUMC – sequence: 35 givenname: Giovanni surname: Frisoni fullname: Frisoni, Giovanni organization: Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, University of Geneva – sequence: 36 givenname: Pieter Jelle surname: Visser fullname: Visser, Pieter Jelle organization: Alzheimer Center & Department of Neurology, VU University Medical Center, Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University – sequence: 37 givenname: Frederik surname: Barkhof fullname: Barkhof, Frederik organization: Department of Radiology and Nuclear Medicine, VUMC, Institutes of Neurology and Healthcare Engineering, UCL |
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| Keywords | Alzheimer’s disease Magnetic resonance imaging European Medical Information Framework for Alzheimer’s Disease Mild cognitive impairment Machine learning Biomarkers Amyloid Support vector machine |
| Language | English |
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| PublicationTitle | Alzheimer's research & therapy |
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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... 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... 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... 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... 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... 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,... |
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| Title | MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study |
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