Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker. Deep learning models have been established as reliable and efficient brain age estimators, being trained to predict the chronological age of healthy subjects. In this paper, we investigate the impact of a pre-training step on deep learning models for brain age prediction. More precisely, instead of the common approach of pre-training on natural imaging classification, we propose pre-training the models on brain-related tasks, which led to state-of-the-art results in our experiments on ADNI data. Furthermore, we validate the resulting brain age biomarker on images of patients with mild cognitive impairment and Alzheimer's disease. Interestingly, our results indicate that better-performing deep learning models in terms of brain age prediction on healthy patients do not result in more reliable biomarkers.

翻译：利用神经影像数据进行脑龄预测在评估整体脑健康、成功老龄化以及作为疾病生物标志物方面展现出巨大潜力。深度学习模型已被确立为可靠且高效的脑龄估计方法，通过训练预测健康受试者的实际年龄。本文探究了预训练步骤对脑龄预测深度学习模型的影响。具体而言，我们摒弃了常见的基于自然图像分类预训练方法，转而提出在脑部相关任务上对模型进行预训练，该方法在ADNI数据上的实验取得了最先进成果。此外，我们在轻度认知障碍和阿尔茨海默病患者的影像上验证了所得脑龄生物标志物的有效性。有趣的是，我们的结果表明，在健康患者脑龄预测任务中表现更优的深度学习模型，并未产生更可靠的生物标志物。