Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on which we train a uniform deep learning model for brain age estimation. We demonstrate an age estimation accuracy on a hold-out test set (mean absolute error = 4.06 years, r = 0.970) and an independent life span evaluation data set (mean absolute error = 4.21 years, r = 0.960). We further demonstrate the utility of the estimated age in a life span aging analysis of cognitive functions. In summary, we achieve age estimation performance comparable to previous studies, but with a more heterogenous data set confirming the efficacy of this deep learning framework. We also evaluated training with varying age distributions. The analysis of regional contributions to our brain age predictions through multiple analyses, and confirmation of the association of divergence between the estimated and chronological brain age with neuropsychological measures, may be useful in the development and evaluation of similar imaging biomarkers.

中文翻译：

---

利用深度学习和结构 MRI 根据统一的健康人群估计大脑年龄

大量研究已经证实，估计的大脑年龄是一个有价值的生物标志物，可以预测认知能力下降和各种神经系统疾病。在这项工作中，我们整理了健康个体的大规模脑部 MRI 数据集，并在此基础上训练了用于大脑年龄估计的统一深度学习模型。我们在保留测试集（平均绝对误差 = 4.06 岁，r = 0.970）和独立寿命评估数据集（平均绝对误差 = 4.21 岁，r = 0.960）上证明了年龄估计的准确性。我们进一步证明了估计年龄在认知功能的寿命老化分析中的效用。总之，我们实现了与之前的研究相当的年龄估计性能，但更异构的数据集证实了这种深度学习框架的有效性。我们还评估了不同年龄分布的训练。通过多重分析来分析大脑年龄预测的区域贡献，并确认估计大脑年龄和实际大脑年龄之间的差异与神经心理学测量之间的关联，可能有助于开发和评估类似的成像生物标志物。