作为神经衰老的重要生物标志物,大脑年龄反映了人类大脑的完整性和健康。准确预测大脑年龄有助于理解神经衰老的潜在机制。在这项研究中,利用T1加权磁共振成像(MRI)数据,提出了一种具有staking策略的交叉分层集成学习算法,以获得脑年龄和推导的预测年龄差异(PAD).该方法的特点是实现两个模块:一个是3D-DenseNet的三个基础学习者,3D-ResNeXt,3D-Inception-v4;另一个是线性回归的14个二级学习者。为了评估性能,我们的方法与单基础学习者进行了比较,常规集成学习算法,和最先进的(SOTA)方法。结果表明,我们提出的模型优于其他模型,具有三个平均绝对误差(MAE)指标,均方根误差(RMSE),和2.9405年的决定系数(R2),3.9458年,和0.9597。此外,正常对照组(NC)三组间PAD存在显著差异,轻度认知障碍(MCI)和阿尔茨海默病(AD),随着整个NC的增长趋势,MCI和AD。结果表明,该算法可以有效地用于计算大脑老化和PAD,并提供早期诊断和评估正常脑老化和AD的潜力。
As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer\'s disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.