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

机译：淀粉样蛋白病理的MRI预测因子：EMIF-AD多峰生物标志物发现研究的结果

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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 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.
机译：随着研究重点转向痴呆前期阿尔茨海默氏病（AD），迫切需要可靠的，非侵入性的生物标志物来预测淀粉样蛋白的病理。这项研究的目的是评估从结构性MRI轻松获得的指标，结合人口统计学数据，认知数据和载脂蛋白E（APOE）ε4基因型，是否可以用于通过机器学习分类来预测淀粉样蛋白病变。我们检查了810名具有结构性MRI数据和来自欧洲医学信息框架的阿尔茨海默氏病多峰生物标记物发现研究的淀粉样蛋白标记物的受试者，包括具有正常认知能力的受试者（CN，n？=？337，年龄？66.5？±？7.2，50％的女性，27％的淀粉样蛋白阳性），轻度认知障碍（MCI，n = 375），年龄69.1±7.5、53％的女性，63％淀粉样蛋白阳性）和AD痴呆（n = 98，年龄67.0）。 ±？7.7，女性48％，淀粉样蛋白阳性97％）。目视评估结构MRI扫描，并使用Freesurfer获得皮层下体积，皮层厚度和表面积测量值。我们首先使用混合模型评估了MRI测量与淀粉样蛋白病理之间的单变量关联。接下来，我们使用人口统计，认知，MRI和APOEε4信息开发并测试了自动分类器，以预测淀粉样蛋白病理。使用具有嵌套10倍交叉验证的支持向量机（SVM）来识别一组最佳区分淀粉样蛋白阳性和淀粉样蛋白阴性受试者的标记。在单变量关联中，淀粉样蛋白病理与CN和MCI的AD签名区域皮层下体积较小和皮质较薄相关。多变量SVM分类器在MCI中提供的曲线下面积（AUC）为0.81≤±0.07，在CN中提供的AUC为0.74≤±0.08。在CN中，分类器的选定功能包括APOEε4，年龄，记忆评分和一些MRI测量值，例如颞和海马区的海马，杏仁核和伏隔体积以及皮层厚度。在MCI中，添加了影像测量后，包括人口统计信息和APOEε4信息在内的分类器并未得到改善。淀粉样蛋白病理与CN和MCI中结构MRI测量的变化有关。基于临床，影像和APOEε4数据的自动分类器可以中等程度的准确性识别淀粉样蛋白病理的存在。这些结果可用于临床试验中以预先筛选抗淀粉样蛋白疗法的受试者。

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