Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease
Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease
复制标题
DOI：
10.1016/j.neurobiolaging.2019.11.005
复制
发表时间：
2020-03-01
影响因子：
4.2
通讯作者：
Aizenstein, Howard J.
中科院分区：
文献类型：
--
作者：
Ly, Maria;Yu, Gary Z.;Aizenstein, Howard J.
脑年龄预测是一种机器学习方法，通过神经成像扫描来估计一个人的实际年龄。脑年龄表明一个人的大脑是否比同龄的健康同龄人“更老”，这表明他们可能经历了更高的脑损伤累积暴露，或者受到这些病理性损伤的影响更大。然而，当代脑年龄模型在训练集中包括了淀粉样蛋白病理的老年参与者，因此在研究阿尔茨海默病（AD）时可能会混淆。我们发现淀粉样蛋白状态是脑年龄预测模型的一个关键特征。我们在没有淀粉样蛋白病理的参与者的t1加权MRI图像上训练了一个模型。对MRI数据进行处理以估计灰质体素密度，然后用它来预测实际年龄。我们的模型与以前的模型相比表现准确。值得注意的是，我们证明了AD诊断组之间的差异比其他模型更显著。此外，我们的模型能够描绘出在有和没有淀粉样蛋白的认知正常个体之间，脑年龄相对于实足年龄的显著差异。将淀粉样蛋白状态纳入脑年龄预测模型最终提高了脑年龄作为AD生物标志物的实用性。(C) 2019 Elsevier Inc.版权所有。
Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD). We showed that amyloid status is a critical feature for brain age prediction models. We trained a model on T1-weighted MRI images participants without amyloid pathology. MRI data were processed to estimate gray matter density voxel-wise, which were then used to predict chronological age. Our model performed accurately comparable to previous models. Notably, we demonstrated more significant differences between AD diagnostic groups than other models. In addition, our model was able to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid. Incorporation of amyloid status in brain age prediction models ultimately improves the utility of brain age as a biomarker for AD. (C) 2019 Elsevier Inc. All rights reserved.