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- ten Kate, M., et al.
(författare)
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MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
- 2018
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Ingår i: Alzheimers Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 10
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Tidskriftsartikel (refereegranskat)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) 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.
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- Caroli, A., et al.
(författare)
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Mild cognitive impairment with suspected nonamyloid pathology (SNAP) Prediction of progression
- 2015
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Ingår i: Neurology. - : Ovid Technologies (Wolters Kluwer Health). - 0028-3878 .- 1526-632X. ; 84:5, s. 508-515
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Tidskriftsartikel (refereegranskat)abstract
- Objectives:The aim of this study was to investigate predictors of progressive cognitive deterioration in patients with suspected non-Alzheimer disease pathology (SNAP) and mild cognitive impairment (MCI).Methods:We measured markers of amyloid pathology (CSF -amyloid 42) and neurodegeneration (hippocampal volume on MRI and cortical metabolism on [F-18]-fluorodeoxyglucose-PET) in 201 patients with MCI clinically followed for up to 6 years to detect progressive cognitive deterioration. We categorized patients with MCI as A+/A- and N+/N- based on presence/absence of amyloid pathology and neurodegeneration. SNAPs were A-N+ cases.Results:The proportion of progressors was 11% (8/41), 34% (14/41), 56% (19/34), and 71% (60/85) in A-N-, A+N-, SNAP, and A+N+, respectively; the proportion of APOE epsilon 4 carriers was 29%, 70%, 31%, and 71%, respectively, with the SNAP group featuring a significantly different proportion than both A+N- and A+N+ groups (p 0.005). Hypometabolism in SNAP patients was comparable to A+N+ patients (p = 0.154), while hippocampal atrophy was more severe in SNAP patients (p = 0.002). Compared with A-N-, SNAP and A+N+ patients had significant risk of progressive cognitive deterioration (hazard ratio = 2.7 and 3.8, p = 0.016 and p < 0.001), while A+N- patients did not (hazard ratio = 1.13, p = 0.771). In A+N- and A+N+ groups, none of the biomarkers predicted time to progression. In the SNAP group, lower time to progression was correlated with greater hypometabolism (r = 0.42, p = 0.073).Conclusions:Our findings support the notion that patients with SNAP MCI feature a specific risk progression profile.
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