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.

中文翻译：

---

改进脑年龄预测模型：将淀粉样蛋白状态纳入阿尔茨海默病

脑年龄预测是一种机器学习方法，可通过神经影像扫描来估计个人的实际年龄。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，或者更容易受到这些病理性损伤的影响。然而，当代脑年龄模型在其训练集中包括患有淀粉样蛋白病理学的老年参与者，因此在研究阿尔茨海默病 (AD) 时可能会混淆。我们表明淀粉样蛋白状态是脑年龄预测模型的关键特征。我们在没有淀粉样蛋白病理的 T1 加权 MRI 图像参与者上训练了一个模型。处理 MRI 数据以估计体素的灰质密度，然后用于预测实足年龄。我们的模型准确地与以前的模型相媲美。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着。此外，我们的模型能够描绘出具有和不具有淀粉样蛋白的认知正常个体之间脑年龄相对于实际年龄的显着差异。在脑年龄预测模型中加入淀粉样蛋白状态最终提高了脑年龄作为 AD 生物标志物的效用。