The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
大脑会随年龄增长而逐渐发生萎缩与机能衰退，并且这种变化的速度和轨迹在脑区间和个体间存在明显差异。由于神经影像可以反映大脑的健康状态，因此常用于大脑年龄的预测研究。本文对基于神经影像的脑年龄预测模型研究进行了系统的梳理和回顾，根据影像的模态和特征类型对这些研究进行综述，剖析了其优缺点。结果显示，基于神经影像的预测框架具备个体对象脑年龄预测的潜力。最后，本文讨论了脑年龄预测中存在的问题，并对未来的研究方向进行了展望。.
Keywords: brain age; brain ageing; convolution neural network; machine learning; neuroimage; prediction model.